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transformers
|
# Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921) by Lim et al. (2017) and first released in [this repository](https://github.com/sanghyun-son/EDSR-PyTorch).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2.
## Model description
EDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import EdsrModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, EdsrModel, EdsrConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = EdsrConfig(
scale=4, # train a model to upscale 4x
)
model = EdsrModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |edsr-base |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38.02/0.9607** |
|Set5 |3x |30.39/0.8678 |**35.04/0.9403** |
|Set5 |4x |28.42/0.8101 |**32.12/0.8947** |
|Set14 |2x |30.22/0.8683 |**33.57/0.9172** |
|Set14 |3x |27.53/0.7737 |**30.93/0.8567** |
|Set14 |4x |25.99/0.7023 |**28.60/0.7815** |
|BSD100 |2x |29.55/0.8425 |**32.21/0.8999** |
|BSD100 |3x |27.20/0.7382 |**29.65/0.8204** |
|BSD100 |4x |25.96/0.6672 |**27.61/0.7363** |
|Urban100 |2x |26.66/0.8408 |**32.04/0.9276** |
|Urban100 |3x | |**29.23/0.8723** |
|Urban100 |4x |23.14/0.6573 |**26.02/0.7832** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@InProceedings{Lim_2017_CVPR_Workshops,
author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/edsr-base
|
[
"transformers",
"EDSR",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1707.02921",
"2104.07566"
] |
[] |
TAGS
#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
========================================================================
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Enhanced Deep Residual Networks for Single Image Super-Resolution by Lim et al. (2017) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2.
!Comparing Bicubic upscaling against EDSR x2 upscaling on Set5 Image 4
Model description
-----------------
EDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels.
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against x2 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against x2 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against x2 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
120,
127,
159,
192
] |
[
"passage: TAGS\n#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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] |
null | null |
transformers
|
# Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921) by Lim et al. (2017) and first released in [this repository](https://github.com/sanghyun-son/EDSR-PyTorch).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2.
## Model description
EDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import EdsrModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = EdsrModel.from_pretrained('eugenesiow/edsr', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, EdsrModel, EdsrConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = EdsrConfig(
scale=4, # train a model to upscale 4x
)
model = EdsrModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |edsr |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38.19/0.9612** |
|Set5 |3x |30.39/0.8678 |**35.31/0.9421** |
|Set5 |4x |28.42/0.8101 |**32.5/0.8986** |
|Set14 |2x |30.22/0.8683 |**33.99/0.9215** |
|Set14 |3x |27.53/0.7737 |**31.18/0.862** |
|Set14 |4x |25.99/0.7023 |**28.92/0.7899** |
|BSD100 |2x |29.55/0.8425 |**33.89/0.9266** |
|BSD100 |3x |27.20/0.7382 |**29.77/0.8224** |
|BSD100 |4x |25.96/0.6672 |**28.62/0.7689** |
|Urban100 |2x |26.66/0.8408 |**32.68/0.9331** |
|Urban100 |3x | |**29.75/0.8825** |
|Urban100 |4x |23.14/0.6573 |**26.53/0.7995** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@InProceedings{Lim_2017_CVPR_Workshops,
author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/edsr
|
[
"transformers",
"EDSR",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1707.02921",
"2104.07566"
] |
[] |
TAGS
#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
========================================================================
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Enhanced Deep Residual Networks for Single Image Super-Resolution by Lim et al. (2017) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
EDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels.
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
120,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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null | null |
transformers
|
# Holistic Attention Network (HAN)
HAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Single Image Super-Resolution via a Holistic Attention Network](https://arxiv.org/abs/2008.08767) by Niu et al. (2020) and first released in [this repository](https://github.com/wwlCape/HAN).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features in each layer. However, channel attention treats each convolution layer as a separate process that misses the correlation among different layers. To address this problem, we propose a new holistic attention network (HAN), which consists of a layer attention module (LAM) and a channel-spatial attention module (CSAM), to model the holistic interdependencies among layers, channels, and positions. Specifically, the proposed LAM adaptively emphasizes hierarchical features by considering correlations among layers. Meanwhile, CSAM learns the confidence at all the positions of each channel to selectively capture more informative features. Extensive experiments demonstrate that the proposed HAN performs favorably against the state-of-the-art single image super- resolution approaches.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import HanModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = HanModel.from_pretrained('eugenesiow/han', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, HanModel, HanConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = HanConfig(
scale=4, # train a model to upscale 4x
)
model = HanModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |han |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**** |
|Set5 |3x |30.39/0.8678 |**** |
|Set5 |4x |28.42/0.8101 |**31.21/0.8778** |
|Set14 |2x |30.22/0.8683 |**** |
|Set14 |3x |27.53/0.7737 |**** |
|Set14 |4x |25.99/0.7023 |**28.18/0.7712** |
|BSD100 |2x |29.55/0.8425 |**** |
|BSD100 |3x |27.20/0.7382 |**** |
|BSD100 |4x |25.96/0.6672 |**28.09/0.7533** |
|Urban100 |2x |26.66/0.8408 |**** |
|Urban100 |3x | |**** |
|Urban100 |4x |23.14/0.6573 |**25.1/0.7497** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{niu2020single,
title={Single Image Super-Resolution via a Holistic Attention Network},
author={Ben Niu and Weilei Wen and Wenqi Ren and Xiangde Zhang and Lianping Yang and Shuzhen Wang and Kaihao Zhang and Xiaochun Cao and Haifeng Shen},
year={2020},
eprint={2008.08767},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/han
|
[
"transformers",
"HAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2008.08767",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2008.08767",
"2104.07566"
] |
[] |
TAGS
#transformers #HAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2008.08767 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
Holistic Attention Network (HAN)
================================
HAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Single Image Super-Resolution via a Holistic Attention Network by Niu et al. (2020) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features in each layer. However, channel attention treats each convolution layer as a separate process that misses the correlation among different layers. To address this problem, we propose a new holistic attention network (HAN), which consists of a layer attention module (LAM) and a channel-spatial attention module (CSAM), to model the holistic interdependencies among layers, channels, and positions. Specifically, the proposed LAM adaptively emphasizes hierarchical features by considering correlations among layers. Meanwhile, CSAM learns the confidence at all the positions of each channel to selectively capture more informative features. Extensive experiments demonstrate that the proposed HAN performs favorably against the state-of-the-art single image super- resolution approaches.
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #HAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2008.08767 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
118,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #HAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2008.08767 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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null | null |
transformers
|
# Multi-Scale Deep Super-Resolution System (MDSR)
MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921) by Lim et al. (2017) and first released in [this repository](https://github.com/sanghyun-son/EDSR-PyTorch).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
The MDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import MdsrModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = MdsrModel.from_pretrained('eugenesiow/mdsr-bam', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, MdsrModel, MdsrConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = MdsrConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = MdsrModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |mdsr-bam |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38/0.9607** |
|Set5 |3x |30.39/0.8678 |**35.07/0.9402** |
|Set5 |4x |28.42/0.8101 |**32.19/0.8949** |
|Set14 |2x |30.22/0.8683 |**33.68/0.9182** |
|Set14 |3x |27.53/0.7737 |**31.04/0.8582** |
|Set14 |4x |25.99/0.7023 |**28.73/0.7847** |
|BSD100 |2x |29.55/0.8425 |**33.77/0.9253** |
|BSD100 |3x |27.20/0.7382 |**29.62/0.8188** |
|BSD100 |4x |25.96/0.6672 |**28.5/0.7645** |
|Urban100 |2x |26.66/0.8408 |**32.04/0.9272** |
|Urban100 |3x | |**29.16/0.8717** |
|Urban100 |4x |23.14/0.6573 |**26.02/0.7834** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{wang2021bam,
title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
author={Fanyi Wang and Haotian Hu and Cheng Shen},
year={2021},
eprint={2104.07566},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
```bibtex
@article{ahn2018fast,
title={Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network},
author={Ahn, Namhyuk and Kang, Byungkon and Sohn, Kyung-Ah},
journal={arXiv preprint arXiv:1803.08664},
year={2018}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/mdsr-bam
|
[
"transformers",
"MDSR",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1707.02921",
"2104.07566"
] |
[] |
TAGS
#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us
|
Multi-Scale Deep Super-Resolution System (MDSR)
===============================================
MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Enhanced Deep Residual Networks for Single Image Super-Resolution by Lim et al. (2017) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
The MDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
This model also applies the balanced attention (BAM) method invented by Wang et al. (2021) to further improve the results.
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
116,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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null | null |
transformers
|
# Multi-Scale Deep Super-Resolution System (MDSR)
MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921) by Lim et al. (2017) and first released in [this repository](https://github.com/sanghyun-son/EDSR-PyTorch).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
The MDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import MdsrModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = MdsrModel.from_pretrained('eugenesiow/mdsr', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, MdsrModel, MdsrConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = MdsrConfig(
scale=4, # train a model to upscale 4x
)
model = MdsrModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |mdsr |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38.04/0.9608** |
|Set5 |3x |30.39/0.8678 |**35.11/0.9406** |
|Set5 |4x |28.42/0.8101 |**32.26/0.8953** |
|Set14 |2x |30.22/0.8683 |**33.71/0.9184** |
|Set14 |3x |27.53/0.7737 |**31.06/0.8593** |
|Set14 |4x |25.99/0.7023 |**28.77/0.7856** |
|BSD100 |2x |29.55/0.8425 |**33.79/0.9256** |
|BSD100 |3x |27.20/0.7382 |**29.66/0.8196** |
|BSD100 |4x |25.96/0.6672 |**28.53/0.7653** |
|Urban100 |2x |26.66/0.8408 |**32.14/0.9283** |
|Urban100 |3x | |**29.29/0.8738** |
|Urban100 |4x |23.14/0.6573 |**26.07/0.7851** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@article{ahn2018fast,
title={Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network},
author={Ahn, Namhyuk and Kang, Byungkon and Sohn, Kyung-Ah},
journal={arXiv preprint arXiv:1803.08664},
year={2018}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/mdsr
|
[
"transformers",
"MDSR",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1707.02921",
"2104.07566"
] |
[] |
TAGS
#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
Multi-Scale Deep Super-Resolution System (MDSR)
===============================================
MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Enhanced Deep Residual Networks for Single Image Super-Resolution by Lim et al. (2017) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
The MDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
120,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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null | null |
transformers
|
# Multi-scale Residual Network for Image Super-Resolution (MSRN)
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Multi-scale Residual Network for Image Super-Resolution](https://openaccess.thecvf.com/content_ECCV_2018/html/Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.html) by Li et al. (2018) and first released in [this repository](https://github.com/MIVRC/MSRN-PyTorch).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.
## Model description
The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import MsrnModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = MsrnModel.from_pretrained('eugenesiow/msrn-bam', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, MsrnModel, MsrnConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = MsrnConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = MsrnModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |msrn-bam |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38.02/0.9608** |
|Set5 |3x |30.39/0.8678 |**35.13/0.9408** |
|Set5 |4x |28.42/0.8101 |**32.26/0.8955** |
|Set14 |2x |30.22/0.8683 |**33.73/0.9186** |
|Set14 |3x |27.53/0.7737 |**31.06/0.8588** |
|Set14 |4x |25.99/0.7023 |**28.78/0.7859** |
|BSD100 |2x |29.55/0.8425 |**33.78/0.9253** |
|BSD100 |3x |27.20/0.7382 |**29.65/0.8196** |
|BSD100 |4x |25.96/0.6672 |**28.51/0.7651** |
|Urban100 |2x |26.66/0.8408 |**32.08/0.9276** |
|Urban100 |3x | |**29.26/0.8736** |
|Urban100 |4x |23.14/0.6573 |**26.10/0.7857** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{wang2021bam,
title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
author={Fanyi Wang and Haotian Hu and Cheng Shen},
year={2021},
eprint={2104.07566},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
```bibtex
@InProceedings{Li_2018_ECCV,
author = {Li, Juncheng and Fang, Faming and Mei, Kangfu and Zhang, Guixu},
title = {Multi-scale Residual Network for Image Super-Resolution},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/msrn-bam
|
[
"transformers",
"MSRN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.07566"
] |
[] |
TAGS
#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us
|
Multi-scale Residual Network for Image Super-Resolution (MSRN)
==============================================================
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Multi-scale Residual Network for Image Super-Resolution by Li et al. (2018) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
This model also applies the balanced attention (BAM) method invented by Wang et al. (2021) to further improve the results.
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
107,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
[
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0.12105172127485275,
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0.036115650087594986,
0.12752525508403778,
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0.025081230327486992,
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null | null |
transformers
|
# Multi-scale Residual Network for Image Super-Resolution (MSRN)
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Multi-scale Residual Network for Image Super-Resolution](https://openaccess.thecvf.com/content_ECCV_2018/html/Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.html) by Li et al. (2018) and first released in [this repository](https://github.com/MIVRC/MSRN-PyTorch).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.
## Model description
The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import MsrnModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = MsrnModel.from_pretrained('eugenesiow/msrn', scale=4) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_4x.png') # save the output 4x scaled image to `./scaled_4x.png`
ImageLoader.save_compare(inputs, preds, './scaled_4x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, MsrnModel, MsrnConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = MsrnConfig(
scale=4, # train a model to upscale 4x
)
model = MsrnModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |msrn |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38.08/0.9609** |
|Set5 |3x |30.39/0.8678 |**35.12/0.9409** |
|Set5 |4x |28.42/0.8101 |**32.19/0.8951** |
|Set14 |2x |30.22/0.8683 |**33.75/0.9183** |
|Set14 |3x |27.53/0.7737 |**31.08/0.8593** |
|Set14 |4x |25.99/0.7023 |**28.78/0.7862** |
|BSD100 |2x |29.55/0.8425 |**33.82/0.9258** |
|BSD100 |3x |27.20/0.7382 |**29.67/0.8198** |
|BSD100 |4x |25.96/0.6672 |**28.53/0.7657** |
|Urban100 |2x |26.66/0.8408 |**32.14/0.9287** |
|Urban100 |3x | |**29.31/0.8743** |
|Urban100 |4x |23.14/0.6573 |**26.12/0.7866** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@InProceedings{Agustsson_2017_CVPR_Workshops,
author = {Agustsson, Eirikur and Timofte, Radu},
title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
month = {July},
year = {2017}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/msrn
|
[
"transformers",
"MSRN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.07566"
] |
[] |
TAGS
#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
Multi-scale Residual Network for Image Super-Resolution (MSRN)
==============================================================
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Multi-scale Residual Network for Image Super-Resolution by Li et al. (2018) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
111,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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] |
null | null |
transformers
|
# Pixel Attention Network (PAN)
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Efficient Image Super-Resolution Using Pixel Attention](https://arxiv.org/abs/2010.01073) by Zhao et al. (2020) and first released in [this repository](https://github.com/zhaohengyuan1/PAN).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results.
This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
The model is very lightweight with the model being just 260k to 270k parameters (~1mb).
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import PanModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = PanModel.from_pretrained('eugenesiow/pan-bam', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, PanModel, PanConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = PanConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = PanModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |pan-bam |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**37.7/0.9596** |
|Set5 |3x |30.39/0.8678 |**34.62/0.9371** |
|Set5 |4x |28.42/0.8101 |**31.9/0.8911** |
|Set14 |2x |30.22/0.8683 |**33.4/0.9161** |
|Set14 |3x |27.53/0.7737 |**30.83/0.8545** |
|Set14 |4x |25.99/0.7023 |**28.54/0.7795** |
|BSD100 |2x |29.55/0.8425 |**33.6/0.9234** |
|BSD100 |3x |27.20/0.7382 |**29.47/0.8153** |
|BSD100 |4x |25.96/0.6672 |**28.32/0.7591** |
|Urban100 |2x |26.66/0.8408 |**31.35/0.92** |
|Urban100 |3x | |**28.64/0.861** |
|Urban100 |4x |23.14/0.6573 |**25.6/0.7691** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{wang2021bam,
title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
author={Fanyi Wang and Haotian Hu and Cheng Shen},
year={2021},
eprint={2104.07566},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
```bibtex
@misc{zhao2020efficient,
title={Efficient Image Super-Resolution Using Pixel Attention},
author={Hengyuan Zhao and Xiangtao Kong and Jingwen He and Yu Qiao and Chao Dong},
year={2020},
eprint={2010.01073},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/pan-bam
|
[
"transformers",
"PAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2010.01073",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.01073",
"2104.07566"
] |
[] |
TAGS
#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us
|
Pixel Attention Network (PAN)
=============================
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Efficient Image Super-Resolution Using Pixel Attention by Zhao et al. (2020) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results.
This model also applies the balanced attention (BAM) method invented by Wang et al. (2021) to further improve the results.
The model is very lightweight with the model being just 260k to 270k parameters (~1mb).
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
114,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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null | null |
transformers
|
# Pixel Attention Network (PAN)
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Efficient Image Super-Resolution Using Pixel Attention](https://arxiv.org/abs/2010.01073) by Zhao et al. (2020) and first released in [this repository](https://github.com/zhaohengyuan1/PAN).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results.
The model is very lightweight with the model being just 260k to 270k parameters (~1mb).
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import PanModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = PanModel.from_pretrained('eugenesiow/pan', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, PanModel, PanConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = PanConfig(
scale=4, # train a model to upscale 4x
)
model = PanModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |pan |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**37.77/0.9599** |
|Set5 |3x |30.39/0.8678 |**34.64/0.9376** |
|Set5 |4x |28.42/0.8101 |**31.92/0.8915** |
|Set14 |2x |30.22/0.8683 |**33.42/0.9162** |
|Set14 |3x |27.53/0.7737 |**30.8/0.8544** |
|Set14 |4x |25.99/0.7023 |**28.57/0.7802** |
|BSD100 |2x |29.55/0.8425 |**33.6/0.9235** |
|BSD100 |3x |27.20/0.7382 |**29.47/0.815** |
|BSD100 |4x |25.96/0.6672 |**28.35/0.7595** |
|Urban100 |2x |26.66/0.8408 |**31.31/0.9197** |
|Urban100 |3x | |**28.61/0.8603** |
|Urban100 |4x |23.14/0.6573 |**25.63/0.7692** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{zhao2020efficient,
title={Efficient Image Super-Resolution Using Pixel Attention},
author={Hengyuan Zhao and Xiangtao Kong and Jingwen He and Yu Qiao and Chao Dong},
year={2020},
eprint={2010.01073},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/pan
|
[
"transformers",
"PAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2010.01073",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.01073",
"2104.07566"
] |
[] |
TAGS
#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
Pixel Attention Network (PAN)
=============================
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Efficient Image Super-Resolution Using Pixel Attention by Zhao et al. (2020) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results.
The model is very lightweight with the model being just 260k to 270k parameters (~1mb).
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
118,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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null | null |
transformers
|
# Residual Channel Attention Networks (RCAN)
RCAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://arxiv.org/abs/1807.02758) by Zhang et al. (2018) and first released in [this repository](https://github.com/yulunzhang/RCAN).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.
This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import RcanModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = RcanModel.from_pretrained('eugenesiow/rcan-bam', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, RcanModel, RcanConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = RcanConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = RcanModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |rcan-bam |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**** |
|Set5 |3x |30.39/0.8678 |**** |
|Set5 |4x |28.42/0.8101 |**30.8/0.8701** |
|Set14 |2x |30.22/0.8683 |**** |
|Set14 |3x |27.53/0.7737 |**** |
|Set14 |4x |25.99/0.7023 |**27.91/0.7648** |
|BSD100 |2x |29.55/0.8425 |**** |
|BSD100 |3x |27.20/0.7382 |**** |
|BSD100 |4x |25.96/0.6672 |**27.91/0.7477** |
|Urban100 |2x |26.66/0.8408 |**** |
|Urban100 |3x | |**** |
|Urban100 |4x |23.14/0.6573 |**24.75/0.7346** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{wang2021bam,
title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
author={Fanyi Wang and Haotian Hu and Cheng Shen},
year={2021},
eprint={2104.07566},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
```bibtex
@misc{zhang2018image,
title={Image Super-Resolution Using Very Deep Residual Channel Attention Networks},
author={Yulun Zhang and Kunpeng Li and Kai Li and Lichen Wang and Bineng Zhong and Yun Fu},
year={2018},
eprint={1807.02758},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
{"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]}
| null |
eugenesiow/rcan-bam
|
[
"transformers",
"RCAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1807.02758",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1807.02758",
"2104.07566"
] |
[] |
TAGS
#transformers #RCAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1807.02758 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us
|
Residual Channel Attention Networks (RCAN)
==========================================
RCAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Image Super-Resolution Using Very Deep Residual Channel Attention Networks by Zhang et al. (2018) and first released in this repository.
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4
Model description
-----------------
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.
This model also applies the balanced attention (BAM) method invented by Wang et al. (2021) to further improve the results.
Intended uses & limitations
---------------------------
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the super\_image library:
Here is how to use a pre-trained model to upscale your image:
Training data
-------------
The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
Training procedure
------------------
### Preprocessing
We follow the pre-processing and training method of Wang et al..
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface datasets library to download the data:
The following code gets the data and preprocesses/augments the data.
### Pretraining
The model was trained on GPU. The training code is provided below:
Evaluation results
------------------
The evaluation metrics include PSNR and SSIM.
Evaluation datasets include:
* Set5 - Bevilacqua et al. (2012)
* Set14 - Zeyde et al. (2010)
* BSD100 - Martin et al. (2001)
* Urban100 - Huang et al. (2015)
The results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.
!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2
You can find a notebook to easily run evaluation on pretrained models below:
BibTeX entry and citation info
------------------------------
|
[
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
"TAGS\n#transformers #RCAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1807.02758 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data.",
"### Pretraining\n\n\nThe model was trained on GPU. The training code is provided below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nEvaluation results\n------------------\n\n\nThe evaluation metrics include PSNR and SSIM.\n\n\nEvaluation datasets include:\n\n\n* Set5 - Bevilacqua et al. (2012)\n* Set14 - Zeyde et al. (2010)\n* BSD100 - Martin et al. (2001)\n* Urban100 - Huang et al. (2015)\n\n\nThe results columns below are represented below as 'PSNR/SSIM'. They are compared against a Bicubic baseline.\n\n\n\n!Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2\n\n\nYou can find a notebook to easily run evaluation on pretrained models below:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nBibTeX entry and citation info\n------------------------------"
] |
[
116,
127,
159,
194
] |
[
"passage: TAGS\n#transformers #RCAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1807.02758 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nWe follow the pre-processing and training method of Wang et al..\nLow Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.\nDuring training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.\nData augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.\n\n\nWe need the huggingface datasets library to download the data:\n\n\nThe following code gets the data and preprocesses/augments the data."
] |
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] |
null | null |
transformers
|
korean Mental Health BERT
kcBERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다.
datasets from AIhub
웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 2만9천개)
@inproceedings{lee2020kcbert, title={KcBERT: Korean Comments BERT}, author={Lee, Junbum}, booktitle={Proceedings of the 32nd Annual Conference on Human and Cognitive Language Technology}, pages={437--440}, year={2020} }
|
{}
|
feature-extraction
|
eunjin/koMHBERT-kcbert-based-v1
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
|
korean Mental Health BERT
kcBERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다.
datasets from AIhub
웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 2만9천개)
@inproceedings{lee2020kcbert, title={KcBERT: Korean Comments BERT}, author={Lee, Junbum}, booktitle={Proceedings of the 32nd Annual Conference on Human and Cognitive Language Technology}, pages={437--440}, year={2020} }
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
[
32
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
korean Mental Health BERT -v2
huggingface에 공개된 kcbert-base BERT를 정신건강의학신문을 크롤링한 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 발화 관련 데이터를 모을 수 없는 상황에서 이를 대체할만한 데이터로 제시합니다. 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
정신건강의학신문: http://www.psychiatricnews.net
|
{}
|
feature-extraction
|
eunjin/koMHBERT-kcbert-based-v2
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
|
korean Mental Health BERT -v2
huggingface에 공개된 kcbert-base BERT를 정신건강의학신문을 크롤링한 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 발화 관련 데이터를 모을 수 없는 상황에서 이를 대체할만한 데이터로 제시합니다. 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
정신건강의학신문: URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
[
29
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
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] |
null | null |
transformers
|
korean Mental Health BERT
huggingface에 공개된 KR-Medium BERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다. 이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다.
datasets from AIhub
웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 2만9천개)
|
{}
|
feature-extraction
|
eunjin/koMHBERT-krbert-based-v1
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
|
korean Mental Health BERT
huggingface에 공개된 KR-Medium BERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다. 이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다.
datasets from AIhub
웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 2만9천개)
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
[
32
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
korean Mental Health BERT -v2
huggingface에 공개된 KR-Medium BERT를 정신건강의학신문을 크롤링한 dataset으로 MLM fine-tuining한 Bert Model입니다.
정신건강 발화 관련 데이터를 모을 수 없는 상황에서 이를 대체할만한 데이터로 제시합니다.
향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
정신건강의학신문: http://www.psychiatricnews.net
|
{}
|
feature-extraction
|
eunjin/koMHBERT-krbert-based-v2
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
|
korean Mental Health BERT -v2
huggingface에 공개된 KR-Medium BERT를 정신건강의학신문을 크롤링한 dataset으로 MLM fine-tuining한 Bert Model입니다.
정신건강 발화 관련 데이터를 모을 수 없는 상황에서 이를 대체할만한 데이터로 제시합니다.
향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
정신건강의학신문: URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
[
29
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
* skt/kogpt2-base-v2에 wellness 및 일상챗봇 데이터를 fine-tuning한 모델입니다.
* 유사한 정신건강 상담 도메인에서 바로 사용 가능합니다.
* 깃허브 사이트를 참조해주세요! https://github.com/eunjiinkim/WellnessChatbot
|
{}
|
text-generation
|
eunjin/kogpt2-finetuned-wellness
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
* skt/kogpt2-base-v2에 wellness 및 일상챗봇 데이터를 fine-tuning한 모델입니다.
* 유사한 정신건강 상담 도메인에서 바로 사용 가능합니다.
* 깃허브 사이트를 참조해주세요! URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 417310788
- CO2 Emissions (in grams): 6.826886567147602
## Validation Metrics
- Loss: 0.20949310064315796
- Accuracy: 0.9578392621870883
- Precision: 0.9476190476190476
- Recall: 0.9045454545454545
- AUC: 0.9714032720526227
- F1: 0.9255813953488372
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/evandrodiniz/autonlp-api-boamente-417310788
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("evandrodiniz/autonlp-api-boamente-417310788", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("evandrodiniz/autonlp-api-boamente-417310788", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "unk", "tags": "autonlp", "datasets": ["evandrodiniz/autonlp-data-api-boamente"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 6.826886567147602}
|
text-classification
|
evandrodiniz/autonlp-api-boamente-417310788
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:evandrodiniz/autonlp-data-api-boamente",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"unk"
] |
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-evandrodiniz/autonlp-data-api-boamente #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 417310788
- CO2 Emissions (in grams): 6.826886567147602
## Validation Metrics
- Loss: 0.20949310064315796
- Accuracy: 0.9578392621870883
- Precision: 0.9476190476190476
- Recall: 0.9045454545454545
- AUC: 0.9714032720526227
- F1: 0.9255813953488372
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 417310788\n- CO2 Emissions (in grams): 6.826886567147602",
"## Validation Metrics\n\n- Loss: 0.20949310064315796\n- Accuracy: 0.9578392621870883\n- Precision: 0.9476190476190476\n- Recall: 0.9045454545454545\n- AUC: 0.9714032720526227\n- F1: 0.9255813953488372",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-evandrodiniz/autonlp-data-api-boamente #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 417310788\n- CO2 Emissions (in grams): 6.826886567147602",
"## Validation Metrics\n\n- Loss: 0.20949310064315796\n- Accuracy: 0.9578392621870883\n- Precision: 0.9476190476190476\n- Recall: 0.9045454545454545\n- AUC: 0.9714032720526227\n- F1: 0.9255813953488372",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
69,
42,
81,
17
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-evandrodiniz/autonlp-data-api-boamente #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 417310788\n- CO2 Emissions (in grams): 6.826886567147602## Validation Metrics\n\n- Loss: 0.20949310064315796\n- Accuracy: 0.9578392621870883\n- Precision: 0.9476190476190476\n- Recall: 0.9045454545454545\n- AUC: 0.9714032720526227\n- F1: 0.9255813953488372## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 417310793
- CO2 Emissions (in grams): 9.446754273734577
## Validation Metrics
- Loss: 0.25755178928375244
- Accuracy: 0.9407114624505929
- Precision: 0.8600823045267489
- Recall: 0.95
- AUC: 0.9732501264968797
- F1: 0.9028077753779697
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/evandrodiniz/autonlp-api-boamente-417310793
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("evandrodiniz/autonlp-api-boamente-417310793", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("evandrodiniz/autonlp-api-boamente-417310793", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "unk", "tags": "autonlp", "datasets": ["evandrodiniz/autonlp-data-api-boamente"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 9.446754273734577}
|
text-classification
|
evandrodiniz/autonlp-api-boamente-417310793
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:evandrodiniz/autonlp-data-api-boamente",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"unk"
] |
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-evandrodiniz/autonlp-data-api-boamente #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 417310793
- CO2 Emissions (in grams): 9.446754273734577
## Validation Metrics
- Loss: 0.25755178928375244
- Accuracy: 0.9407114624505929
- Precision: 0.8600823045267489
- Recall: 0.95
- AUC: 0.9732501264968797
- F1: 0.9028077753779697
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 417310793\n- CO2 Emissions (in grams): 9.446754273734577",
"## Validation Metrics\n\n- Loss: 0.25755178928375244\n- Accuracy: 0.9407114624505929\n- Precision: 0.8600823045267489\n- Recall: 0.95\n- AUC: 0.9732501264968797\n- F1: 0.9028077753779697",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-evandrodiniz/autonlp-data-api-boamente #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 417310793\n- CO2 Emissions (in grams): 9.446754273734577",
"## Validation Metrics\n\n- Loss: 0.25755178928375244\n- Accuracy: 0.9407114624505929\n- Precision: 0.8600823045267489\n- Recall: 0.95\n- AUC: 0.9732501264968797\n- F1: 0.9028077753779697",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
69,
42,
75,
17
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-evandrodiniz/autonlp-data-api-boamente #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 417310793\n- CO2 Emissions (in grams): 9.446754273734577## Validation Metrics\n\n- Loss: 0.25755178928375244\n- Accuracy: 0.9407114624505929\n- Precision: 0.8600823045267489\n- Recall: 0.95\n- AUC: 0.9732501264968797\n- F1: 0.9028077753779697## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Afrikaans-AfriBooms
| Feature | Description |
| --- | --- |
| **Name** | `af_udv25_afrikaansafribooms_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (455 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `AOA`, `AOP`, `ASA`, `ASP`, `AVA`, `AVP`, `BO`, `BS`, `BV`, `KN`, `KO`, `LB`, `LO`, `NA`, `NEE`, `NM`, `NME`, `NSE`, `NSED`, `NSM`, `PA`, `PB`, `PDHEB`, `PDHEDP`, `PDHENP`, `PDHEW`, `PDMB`, `PDMP`, `PDMW`, `PDOENP`, `PDOEW`, `PDVEB`, `PDVEDP`, `PDVENP`, `PDVEW`, `PEEB`, `PEEDP`, `PEENP`, `PEMB`, `PEMP`, `PEMW`, `PO`, `PTEB`, `PTEDP`, `PTENP`, `PTEW`, `PTMP`, `PV`, `PW`, `RA`, `RK`, `RL`, `RO`, `RS`, `RSF`, `RV`, `RWD`, `SVS`, `THAB`, `THAO`, `THBB`, `THBO`, `THNB`, `THPB`, `THPO`, `TRAB`, `TRAO`, `TRBB`, `UPB`, `UPD`, `UPI`, `UPO`, `UPS`, `UPV`, `UPW`, `UXD`, `VTHOG`, `VTHOK`, `VTHOO`, `VTHOV`, `VTHSG`, `VTHSO`, `VTUOA`, `VTUOM`, `VTUOP`, `VUOT`, `VVHOG`, `VVHOK`, `VVHOO`, `VVUOM`, `VVUOP`, `ZE`, `ZM`, `ZPL`, `ZPR` |
| **`morphologizer`** | `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `AdpType=Prep\|POS=ADP`, `AdjType=Attr\|Case=Nom\|Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Cop`, `Definite=Ind\|POS=DET\|PronType=Art`, `POS=NUM`, `POS=PART\|PartType=Inf`, `POS=VERB\|Subcat=Tran\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=PRON\|PronType=Rel`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Pas`, `POS=PUNCT`, `POS=CCONJ`, `POS=SCONJ`, `POS=VERB\|Subcat=Intr\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=VERB\|Subcat=Intr\|Tense=Past\|VerbForm=Part`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Pas`, `Degree=Pos\|POS=ADV`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Mod`, `POS=DET\|PronType=Ind`, `POS=X`, `Number=Sing\|POS=PROPN`, `POS=PRON\|PronType=Ind`, `POS=PART\|PartType=Neg`, `POS=VERB\|Subcat=Tran\|Tense=Past\|VerbForm=Part`, `AdjType=Pred\|Case=Nom\|Degree=Pos\|POS=ADJ`, `POS=DET\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SYM`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PART\|PartType=Gen`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|POS=ADV`, `Degree=Dim\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Int`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `AdjType=Attr\|Case=Nom\|Degree=Sup\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `AdjType=Pred\|Case=Nom\|Degree=Cmp\|POS=ADJ`, `POS=VERB\|Subcat=Prep\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Aux`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Rcp`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Cop`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `AdjType=Attr\|Case=Nom\|Degree=Cmp\|POS=ADJ`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `AdjType=Pred\|Case=Nom\|Degree=Sup\|POS=ADJ` |
| **`parser`** | `ROOT`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `7`, `8`, `10`, `12`, `14`, `16`, `18`, `21`, `24`, `26`, `28`, `31`, `32`, `34`, `37`, `39`, `40`, `42`, `44`, `46`, `47`, `49`, `51`, `53`, `54`, `56`, `57`, `58`, `59`, `61`, `64`, `66`, `68`, `69`, `72`, `74`, `75`, `77`, `78`, `81`, `83`, `84`, `85`, `86`, `87`, `90`, `92`, `94`, `96`, `99`, `101`, `103`, `105`, `108`, `110`, `113`, `116`, `117`, `118`, `121`, `123`, `124`, `125`, `127`, `128`, `129`, `133`, `136`, `138`, `141`, `143`, `145`, `147`, `151`, `153`, `154`, `156`, `158`, `159`, `160`, `162`, `164`, `165`, `167`, `168`, `170`, `172`, `174`, `176`, `178`, `179`, `180`, `181`, `183`, `185`, `189`, `190`, `191`, `192`, `194`, `195`, `197`, `198`, `201`, `202`, `203`, `204`, `206`, `207`, `209`, `213`, `214`, `216`, `217`, `218`, `220`, `221`, `222`, `223`, `225`, `226`, `228`, `229`, `231`, `233`, `234`, `236`, `238`, `240`, `241`, `244`, `247`, `248`, `249`, `250`, `252`, `253`, `255`, `256`, `257`, `258`, `261`, `262`, `263`, `265`, `267`, `269`, `270`, `271`, `273`, `275`, `276`, `278`, `279`, `281`, `283`, `285`, `287`, `289`, `291`, `294`, `296`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `305`, `306`, `307`, `309`, `310`, `311`, `313`, `314`, `315`, `317`, `320`, `321`, `323`, `325`, `326`, `327`, `328`, `329`, `330`, `332`, `333`, `335`, `336`, `337`, `338`, `339`, `340`, `341`, `343`, `344`, `347`, `348`, `349`, `351`, `353`, `355`, `357`, `359`, `360`, `361`, `362`, `365`, `366`, `367`, `369`, `371`, `373`, `374`, `375`, `377`, `379`, `381`, `383`, `386`, `388`, `390`, `392`, `393`, `395`, `397`, `398`, `400`, `401`, `402`, `403`, `405`, `406`, `408`, `409`, `411`, `412`, `414`, `417`, `215`, `418`, `419`, `420`, `421`, `422`, `424`, `425`, `426`, `427`, `429`, `431`, `432`, `433`, `434`, `436`, `438`, `439`, `440`, `442`, `443`, `444`, `447`, `449`, `450`, `452` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.92 |
| `TOKEN_P` | 99.89 |
| `TOKEN_R` | 99.94 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 100.00 |
| `SENTS_P` | 100.00 |
| `SENTS_R` | 100.00 |
| `TAG_ACC` | 96.01 |
| `POS_ACC` | 98.52 |
| `MORPH_ACC` | 97.52 |
| `DEP_UAS` | 90.78 |
| `DEP_LAS` | 87.50 |
| `LEMMA_ACC` | 97.87 |
|
{"language": ["af"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/af_udv25_afrikaansafribooms_trf
|
[
"spacy",
"token-classification",
"af",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"af"
] |
TAGS
#spacy #token-classification #af #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Afrikaans-AfriBooms
### Label Scheme
View label scheme (455 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (455 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #af #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (455 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #af #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (455 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Danish-DDT
| Feature | Description |
| --- | --- |
| **Name** | `da_udv25_danishddt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1316 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` |
| **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `POS=CCONJ`, `Definite=Ind\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADJ`, `POS=PRON\|PartType=Inf`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Pos\|POS=ADV`, `Definite=Def\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PRON\|PronType=Dem`, `NumType=Card\|POS=NUM`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `NumType=Ord\|POS=ADJ`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `POS=ADP\|PartType=Inf`, `Degree=Pos\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `POS=PART\|PartType=Inf`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Com\|POS=PRON\|PronType=Ind`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Imp\|POS=VERB`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=X`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `POS=ADV\|PartType=Inf`, `Degree=Sup\|POS=ADV`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|POS=PROPN`, `POS=ADP`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Gender=Com\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=INTJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=SYM`, `Case=Nom\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Degree=Sup\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Ind\|Style=Arch`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Foreign=Yes\|POS=X`, `POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|POS=PRON\|PronType=Int,Rel`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Abbr=Yes\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Def\|Degree=Abs\|POS=ADJ`, `Definite=Ind\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Definite=Ind\|POS=NOUN`, `Gender=Com\|Number=Plur\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Degree=Abs\|POS=ADV`, `POS=VERB\|VerbForm=Ger`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|Tense=Pres`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=PRON\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Pres\|VerbForm=Part`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|POS=AUX`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|POS=NOUN`, `Number[psor]=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Dem`, `Definite=Def\|Number=Plur\|POS=NOUN` |
| **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:loc`, `obl:tmod`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `7`, `9`, `11`, `13`, `15`, `17`, `19`, `21`, `23`, `27`, `31`, `33`, `35`, `37`, `39`, `42`, `44`, `45`, `5`, `47`, `49`, `51`, `53`, `55`, `57`, `59`, `63`, `67`, `69`, `73`, `75`, `77`, `79`, `81`, `83`, `85`, `87`, `89`, `91`, `93`, `95`, `97`, `101`, `103`, `104`, `106`, `109`, `113`, `115`, `116`, `117`, `118`, `119`, `122`, `124`, `127`, `130`, `133`, `134`, `135`, `138`, `140`, `141`, `144`, `146`, `148`, `149`, `151`, `153`, `154`, `156`, `157`, `158`, `159`, `160`, `164`, `166`, `169`, `172`, `175`, `177`, `179`, `181`, `183`, `185`, `188`, `6`, `190`, `192`, `195`, `197`, `199`, `201`, `203`, `205`, `207`, `209`, `212`, `214`, `216`, `217`, `220`, `221`, `222`, `224`, `227`, `228`, `229`, `230`, `232`, `234`, `236`, `238`, `239`, `241`, `243`, `244`, `247`, `248`, `249`, `250`, `252`, `253`, `254`, `255`, `257`, `258`, `262`, `264`, `270`, `274`, `277`, `278`, `280`, `282`, `284`, `286`, `289`, `290`, `292`, `293`, `294`, `295`, `296`, `297`, `298`, `301`, `302`, `304`, `305`, `306`, `308`, `310`, `312`, `314`, `315`, `317`, `319`, `323`, `324`, `326`, `328`, `330`, `332`, `334`, `336`, `339`, `341`, `342`, `344`, `345`, `346`, `348`, `350`, `353`, `356`, `357`, `359`, `362`, `363`, `365`, `366`, `368`, `369`, `370`, `372`, `374`, `375`, `376`, `378`, `380`, `381`, `385`, `387`, `388`, `392`, `394`, `398`, `401`, `402`, `403`, `405`, `406`, `407`, `408`, `409`, `410`, `411`, `414`, `415`, `416`, `419`, `422`, `423`, `426`, `430`, `431`, `432`, `433`, `436`, `437`, `438`, `439`, `440`, `441`, `442`, `443`, `445`, `446`, `448`, `449`, `450`, `451`, `452`, `453`, `456`, `457`, `460`, `462`, `468`, `469`, `471`, `472`, `473`, `474`, `476`, `478`, `480`, `481`, `484`, `485`, `486`, `488`, `489`, `491`, `492`, `493`, `494`, `495`, `496`, `498`, `500`, `502`, `505`, `507`, `508`, `510`, `511`, `512`, `514`, `515`, `517`, `519`, `521`, `522`, `524`, `525`, `528`, `530`, `532`, `533`, `535`, `536`, `537`, `539`, `542`, `543`, `546`, `547`, `550`, `551`, `553`, `554`, `556`, `557`, `558`, `561`, `562`, `563`, `564`, `567`, `569`, `570`, `573`, `575`, `576`, `577`, `578`, `579`, `580`, `582`, `583`, `584`, `585`, `587`, `588`, `590`, `591`, `593`, `597`, `598`, `600`, `601`, `602`, `603`, `605`, `606`, `607`, `608`, `609`, `610`, `612`, `614`, `617`, `618`, `621`, `623`, `625`, `626`, `627`, `628`, `629`, `630`, `631`, `633`, `634`, `635`, `636`, `638`, `639`, `640`, `641`, `642`, `643`, `645`, `646`, `647`, `649`, `650`, `651`, `653`, `656`, `657`, `659`, `660`, `661`, `662`, `664`, `665`, `667`, `670`, `671`, `672`, `674`, `675`, `676`, `677`, `678`, `679`, `680`, `681`, `683`, `685`, `686`, `688`, `689`, `690`, `691`, `692`, `693`, `694`, `696`, `697`, `698`, `699`, `701`, `702`, `703`, `704`, `705`, `706`, `707`, `709`, `711`, `714`, `715`, `717`, `720`, `721`, `722`, `723`, `725`, `728`, `730`, `731`, `732`, `734`, `736`, `738`, `740`, `742`, `746`, `747`, `748`, `750`, `752`, `753`, `754`, `758`, `759`, `763`, `764`, `766`, `768`, `769`, `773`, `775`, `776`, `778`, `779`, `780`, `781`, `782`, `785`, `788`, `789`, `790`, `791`, `795`, `796`, `797`, `798`, `800`, `801`, `803`, `805`, `806`, `807`, `808`, `810`, `812`, `813`, `815`, `816`, `818`, `821`, `822`, `823`, `825`, `827`, `830`, `832`, `836`, `837`, `838`, `840`, `841`, `844`, `846`, `848`, `850`, `851`, `852`, `854`, `856`, `858`, `860`, `861`, `863`, `864`, `865`, `866`, `867`, `868`, `870`, `872`, `873`, `874`, `875`, `880`, `882`, `884`, `885`, `886`, `887`, `889`, `891`, `892`, `893`, `894`, `895`, `896`, `898`, `902`, `903`, `905`, `907`, `908`, `909`, `911`, `912`, `913`, `914`, `915`, `917`, `918`, `919`, `920`, `922`, `923`, `924`, `926`, `927`, `928`, `929`, `931`, `934`, `935`, `936`, `938`, `939`, `940`, `941`, `942`, `944`, `945`, `947`, `949`, `951`, `952`, `954`, `955`, `956`, `958`, `960`, `961`, `962`, `969`, `970`, `974`, `975`, `977`, `978`, `979`, `980`, `981`, `983`, `984`, `987`, `988`, `989`, `993`, `995`, `998`, `1000`, `1001`, `1002`, `1004`, `1007`, `1011`, `1012`, `1014`, `1017`, `1018`, `1020`, `1021`, `1022`, `1023`, `1025`, `1026`, `1027`, `1029`, `1030`, `1031`, `1032`, `1033`, `1034`, `1036`, `1037`, `1038`, `1040`, `1042`, `1044`, `1045`, `1048`, `1050`, `1051`, `1053`, `1054`, `1056`, `1057`, `1058`, `1059`, `1060`, `1061`, `1062`, `1064`, `1066`, `1067`, `1069`, `1070`, `1072`, `1073`, `1076`, `1078`, `1080`, `1081`, `1085`, `1086`, `1087`, `1088`, `1089`, `1090`, `1092`, `1093`, `1094`, `1096`, `1097`, `1098`, `1100`, `1101`, `1102`, `1106`, `1109`, `1110`, `1111`, `1113`, `1114`, `1116`, `1117`, `1119`, `1120`, `1122`, `1123`, `1125`, `1127`, `1128`, `1131`, `1132`, `1133`, `1134`, `1135`, `1136`, `1137`, `1138`, `1141`, `831`, `1142`, `1143`, `1144`, `1146`, `1148`, `1150`, `1152`, `1153`, `1155`, `1157`, `1158`, `1160`, `1161`, `1162`, `1163`, `1168`, `1170`, `1171`, `1174`, `1175`, `1176`, `1178`, `1181`, `1182`, `1183`, `1185`, `1186`, `1189`, `1191`, `1192`, `1193`, `1194`, `1195`, `1196`, `1198`, `1199`, `1201`, `1203`, `1204`, `1205`, `1206`, `1207`, `1208`, `1209`, `1210`, `1211`, `1212`, `1213`, `1214`, `1215`, `1218`, `1219`, `1220`, `1222`, `1223`, `1224`, `1225`, `1226`, `1227`, `1229`, `1231`, `1232`, `1235`, `1236`, `1238`, `1239`, `1242`, `1244`, `1247`, `1248`, `1249`, `1250`, `1251`, `1253`, `1255`, `1257`, `1258`, `1259`, `1261`, `1263`, `1265`, `1266`, `1267`, `1269`, `1271`, `1272`, `1273`, `1274`, `1276`, `1277`, `1278`, `1280`, `1281`, `1282`, `1283`, `1285`, `1286`, `1287`, `1288`, `1289`, `1291`, `1293`, `1294`, `1295`, `1297`, `1298`, `1299`, `1300`, `1303`, `1305`, `1307`, `1309`, `1310`, `1311`, `1312`, `1315`, `1316`, `1318`, `1321`, `1322`, `1323`, `1324`, `1325`, `1326`, `1327`, `1329`, `1330`, `1331`, `1332`, `1333`, `1334`, `1335`, `1336`, `1337`, `1338`, `1339`, `1341`, `1342`, `1343`, `1344`, `1345`, `1346`, `1347`, `1348`, `1349`, `1351`, `1352`, `1353`, `1354`, `1355`, `1357`, `1358`, `1359`, `1360`, `1362`, `1364`, `1365`, `1367`, `1368`, `1369`, `1370`, `1371`, `1372`, `1374`, `1376`, `1377`, `1379`, `1380`, `1382`, `1383`, `1384`, `1386`, `1387`, `1389`, `1390`, `1391`, `1392`, `1394`, `1396`, `1398`, `1399`, `1400`, `1401`, `1403`, `1404`, `1405`, `1406`, `1407`, `1408`, `1409`, `1410`, `1147`, `1411`, `1413`, `1414`, `1415`, `1418`, `1420`, `1421`, `1422`, `1423`, `1426`, `1427`, `1428`, `1430`, `1431`, `1433`, `1438`, `1439`, `1440`, `1441`, `1442`, `1444`, `1446`, `1448`, `1449`, `1453`, `1454`, `1456`, `1457`, `1459`, `1463`, `1465`, `1466`, `1468`, `1469`, `1470`, `1472`, `1476`, `1478`, `1479`, `1480`, `1481`, `1482`, `1483`, `1485`, `1486`, `1487`, `1488`, `1490`, `1491`, `1493`, `1494`, `1496`, `1498`, `1500`, `1502`, `1503`, `1504`, `1505`, `1506`, `1508`, `1509`, `1511`, `1512`, `1513`, `1514`, `1516`, `1518`, `1519`, `1521`, `1522`, `1524`, `1525`, `1527`, `1533`, `1534`, `1535`, `1536`, `1538`, `1540`, `1541`, `1544`, `1545`, `1547`, `1548`, `1549`, `1550`, `1551`, `1552`, `1556`, `1557`, `1559`, `1560`, `1561`, `1562`, `1563`, `1564`, `1568`, `1569`, `1571`, `1572`, `1574`, `1577`, `1578`, `1579`, `1580`, `1581`, `1583`, `1585`, `1586`, `1587`, `1588`, `1589`, `1590`, `1591`, `1594`, `1595`, `1596`, `1597`, `1598`, `1599`, `1602`, `1603`, `1605`, `1606`, `1608`, `1610`, `1612`, `1613`, `1614`, `1616`, `1618`, `1619`, `1620`, `1621`, `1622`, `1623`, `1626`, `1627`, `1629`, `1630`, `1631`, `1632`, `1634`, `1636`, `1637`, `1638`, `1639`, `1640`, `1641`, `1642`, `1644`, `1645`, `1647`, `1649`, `1651`, `1653`, `1656`, `1657`, `1658`, `1659`, `1660`, `1661`, `1663`, `1665`, `1666`, `1667`, `1668`, `1670`, `1673`, `1674`, `1676`, `1677`, `1678`, `1679`, `1680`, `1681`, `1684`, `1685`, `1687`, `1688`, `1689`, `1690`, `1692`, `1693`, `1643`, `1694`, `1695`, `1696`, `1697`, `1699`, `1701`, `1702`, `1704`, `1706`, `1708`, `1710`, `1711`, `1712`, `1714`, `1715`, `1717`, `1719`, `1720`, `1721`, `1722`, `1723`, `1724`, `1725`, `1726`, `1727`, `1728`, `1729`, `1730`, `1732`, `1734`, `1735`, `1737`, `1739`, `1741`, `1742`, `1743`, `1745`, `1747`, `1749`, `1750`, `1751`, `1753`, `1754`, `1756`, `1758`, `1759`, `1760`, `1761`, `1762`, `1764`, `1766`, `1768`, `1769`, `1770`, `1771`, `1772`, `1773`, `1774` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.96 |
| `TOKEN_P` | 99.95 |
| `TOKEN_R` | 99.96 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 96.89 |
| `SENTS_P` | 97.15 |
| `SENTS_R` | 96.63 |
| `TAG_ACC` | 98.49 |
| `POS_ACC` | 98.48 |
| `MORPH_ACC` | 98.20 |
| `DEP_UAS` | 89.67 |
| `DEP_LAS` | 87.29 |
| `LEMMA_ACC` | 97.55 |
|
{"language": ["da"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/da_udv25_danishddt_trf
|
[
"spacy",
"token-classification",
"da",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"da"
] |
TAGS
#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Danish-DDT
### Label Scheme
View label scheme (1316 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1316 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1316 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1316 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_German-HDT
| Feature | Description |
| --- | --- |
| **Name** | `de_udv25_germanhdt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (62832 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `$(`, `$,`, `$.`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPRART`, `APZR`, `ART`, `CARD`, `FM`, `ITJ`, `KOKOM`, `KON`, `KOUI`, `KOUS`, `NE`, `NN`, `PDAT`, `PDS`, `PIAT`, `PIDAT`, `PIS`, `PPER`, `PPOSAT`, `PPOSS`, `PRELAT`, `PRELS`, `PRF`, `PROAV`, `PTKA`, `PTKANT`, `PTKNEG`, `PTKVZ`, `PTKZU`, `PWAT`, `PWAV`, `PWS`, `TRUNC`, `VAFIN`, `VAIMP`, `VAINF`, `VAPP`, `VMFIN`, `VMINF`, `VMPP`, `VVFIN`, `VVIMP`, `VVINF`, `VVIZU`, `VVPP`, `XY` |
| **`morphologizer`** | `AdpType=Prep\|Case=Dat\|POS=ADP`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Sing\|POS=PROPN\|Person=3`, `Foreign=Yes\|POS=X\|Person=3`, `POS=PUNCT\|PunctType=Comm`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `AdpType=Prep\|POS=ADP`, `Gender=Neut\|Number=Plur\|POS=NOUN\|Person=3`, `POS=CCONJ`, `POS=PUNCT\|PunctType=Peri`, `NumType=Card\|Number=Plur\|POS=NUM\|Person=3`, `Gender=Fem\|Number=Plur\|POS=NOUN\|Person=3`, `AdpType=Prep\|Case=Dat\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `POS=PUNCT\|PunctType=Brck`, `POS=PROPN\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=ADV`, `POS=SCONJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|VerbForm=Inf`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Number=Sing\|POS=PROPN\|Person=3`, `Degree=Cmp\|POS=ADJ\|Variant=Short`, `POS=ADP\|PartType=Vbp`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `AdpType=Prep\|Case=Acc\|POS=ADP`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART\|Polarity=Neg`, `Degree=Cmp\|POS=ADV`, `ConjType=Comp\|POS=CCONJ`, `Degree=Pos\|POS=ADJ\|Variant=Short`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Aspect=Perf\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Number=Plur\|POS=DET\|Person=3`, `Degree=Sup\|POS=ADJ\|Variant=Short`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Hyph=Yes\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN\|Person=3`, `POS=PART\|PartType=Inf`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=NOUN\|Person=3`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=AUX\|VerbForm=Inf`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=AUX\|VerbForm=Inf\|VerbType=Mod`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `AdpType=Prep\|Case=Dat\|Gender=Fem\|POS=ADP\|PronType=Art`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `POS=ADJ`, `Degree=Cmp\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Art`, `POS=ADV\|PronType=Int`, `Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=DET\|Person=3`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Number=Plur\|POS=NOUN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Degree=Cmp\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `AdpType=Post\|Case=Dat\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|POS=AUX\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Number=Plur\|POS=NOUN\|Person=3`, `Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Degree=Sup\|POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Sup\|Number=Plur\|POS=ADJ\|Person=3`, `Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|Person=3`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Number=Plur\|POS=PROPN\|Person=3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `AdpType=Prep\|Case=Acc\|Gender=Neut\|POS=ADP\|PronType=Art`, `Case=Gen\|Number=Sing\|POS=PROPN\|Person=3`, `Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=ADJ\|Person=3`, `POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Dat\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `POS=ADJ\|Person=3`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `AdpType=Circ\|POS=ADP`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `AdpType=Prep\|Case=Nom\|POS=ADP`, `Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Foreign=Yes\|POS=X`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|POS=PROPN\|Person=3`, `Case=Dat\|Number=Plur\|POS=DET\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=ADJ\|Person=3`, `POS=DET`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=X`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `AdpType=Post\|Case=Acc\|POS=ADP`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Degree=Sup\|Number=Plur\|POS=ADJ`, `POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Dat\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=3`, `NumType=Card\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Degree=Pos\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3`, `Degree=Sup\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `POS=ADJ\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Foreign=Yes\|Number=Sing\|POS=X`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|POS=AUX\|VerbForm=Part\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Gender=Masc\|POS=NOUN\|Person=3`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=INTJ\|PartType=Res`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Foreign=Yes\|Gender=Neut\|Number=Sing\|POS=X\|Person=3`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Int`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Gender=Neut\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM\|Person=3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|POS=NOUN\|Person=3`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN\|Person=3`, `Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Number=Plur\|POS=DET\|Person=3`, `Case=Nom\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Hyph=Yes\|Number=Plur\|POS=NOUN\|Person=3`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|POS=PROPN\|Person=3`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Degree=Pos\|Number=Sing\|POS=ADJ\|Person=3`, `POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|POS=PRON\|PronType=Ind,Neg,Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Degree=Sup\|Number=Plur\|POS=ADJ`, `POS=PRON\|PronType=Int`, `Degree=Pos\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Hyph=Yes\|POS=NOUN\|Person=3`, `Degree=Pos\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Dat\|NumType=Card\|Number=Plur\|POS=NUM\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|POS=SCONJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=DET\|Person=3\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|Person=3`, `AdpType=Post\|Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|POS=PROPN\|Person=3`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Ind,Neg,Tot`, `Degree=Pos\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|POS=PROPN\|Person=3`, `Case=Nom\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN\|Person=3`, `AdpType=Prep\|Case=Acc\|Gender=Fem\|POS=ADP\|PronType=Art`, `Degree=Pos\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Case=Nom\|POS=PRON\|PronType=Rel`, `Case=Acc\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN\|Person=3`, `AdpType=Prep\|Case=Dat\|Gender=Neut\|POS=ADP\|PronType=Art`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Dat\|POS=NOUN\|Person=3`, `Degree=Pos\|POS=VERB\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3\|Variant=Short`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=SCONJ\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|POS=DET\|PronType=Art`, `Degree=Pos\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Neg,Tot`, `AdpType=Prep\|POS=ADP\|PronType=Art`, `Number=Sing\|POS=PRON\|PronType=Ind,Neg,Tot`, `Degree=Sup\|Number=Plur\|POS=DET\|Person=3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind,Neg,Tot`, `Number=Sing\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=NOUN\|Person=3`, `AdpType=Prep\|Case=Dat\|Gender=Masc\|POS=ADP\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Degree=Pos\|Gender=Neut\|POS=ADJ`, `Gender=Fem\|POS=ADJ`, `Degree=Pos\|Gender=Fem\|POS=ADJ`, `Gender=Masc\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|VerbType=Mod`, `POS=DET\|Person=3`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `expl:pv`, `flat`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `reparandum`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | -- |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 100.00 |
| `TOKEN_P` | 100.00 |
| `TOKEN_R` | 100.00 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 99.75 |
| `SENTS_P` | 99.74 |
| `SENTS_R` | 99.76 |
| `TAG_ACC` | 97.84 |
| `POS_ACC` | 97.82 |
| `MORPH_ACC` | 78.11 |
| `DEP_UAS` | 97.28 |
| `DEP_LAS` | 95.88 |
| `LEMMA_ACC` | 92.04 |
|
{"language": ["de"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/de_udv25_germanhdt_trf
|
[
"spacy",
"token-classification",
"de",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"de"
] |
TAGS
#spacy #token-classification #de #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_German-HDT
### Label Scheme
View label scheme (62832 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (62832 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #de #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (62832 labels for 6 components)",
"### Accuracy"
] |
[
32,
18,
5
] |
[
"passage: TAGS\n#spacy #token-classification #de #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (62832 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
# Welcome to Healthsea ✨
Create better access to health with machine learning and natural language processing. This is the trained healthsea pipeline for analyzing user reviews to supplements by extracting their effects on health. This pipeline features a trained NER model and a custom Text Classification model with Clause Segmentation and Blinding capabilities.
> Read more in the [blog post](https://explosion.ai/blog/healthsea) and visit the [healthsea repository](https://github.com/explosion/healthsea) for all training workflows, custom components and training data.
| Feature | Description |
| --- | --- |
| **Name** | `en_healthsea` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `sentencizer`, `tok2vec`, `ner`, `benepar`, `segmentation`, `clausecat`, `aggregation` |
| **Components** | `sentencizer`, `tok2vec`, `ner`, `benepar`, `segmentation`, `clausecat`, `aggregation` |
| **Vectors** | 684830 keys, 684830 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | MIT |
| **Author** | [Explosion](explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (6 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `BENEFIT`, `CONDITION` |
| **`clausecat`** | `POSITIVE`, `NEUTRAL`, `NEGATIVE`, `ANAMNESIS` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 80.34 |
| `ENTS_P` | 80.77 |
| `ENTS_R` | 79.92 |
| `CATS_SCORE` | 74.87 |
| `CATS_MICRO_P` | 82.17 |
| `CATS_MICRO_R` | 80.85 |
| `CATS_MICRO_F` | 81.51 |
| `CATS_MACRO_P` | 78.01 |
| `CATS_MACRO_R` | 72.41 |
| `CATS_MACRO_F` | 74.87 |
| `CATS_MACRO_AUC` | 92.76 |
| `CATS_LOSS` | 297.22 |
|
{"language": ["en"], "tags": ["spacy", "token-classification", "text-classification"]}
|
text-classification
|
explosion/en_healthsea
|
[
"spacy",
"token-classification",
"text-classification",
"en",
"model-index",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#spacy #token-classification #text-classification #en #model-index #has_space #region-us
|
Welcome to Healthsea
====================
Create better access to health with machine learning and natural language processing. This is the trained healthsea pipeline for analyzing user reviews to supplements by extracting their effects on health. This pipeline features a trained NER model and a custom Text Classification model with Clause Segmentation and Blinding capabilities.
>
> Read more in the blog post and visit the healthsea repository for all training workflows, custom components and training data.
>
>
>
### Label Scheme
View label scheme (6 labels for 2 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (6 labels for 2 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #text-classification #en #model-index #has_space #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6 labels for 2 components)",
"### Accuracy"
] |
[
30,
16,
5
] |
[
"passage: TAGS\n#spacy #token-classification #text-classification #en #model-index #has_space #region-us \n### Label Scheme\n\n\n\nView label scheme (6 labels for 2 components)### Accuracy"
] |
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null | null |
spacy
|
# 🪐 spaCy Project: Categorization of emotions in Reddit posts (Text Classification) This project uses spaCy to train a text classifier on the [GoEmotions dataset](https://github.com/google-research/google-research/tree/master/goemotions)
| Feature | Description |
| --- | --- |
| **Name** | `en_textcat_goemotions` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `transformer`, `textcat_multilabel` |
| **Components** | `transformer`, `textcat_multilabel` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [GoEmotions dataset](https://github.com/google-research/google-research/tree/master/goemotions) |
| **License** | `MIT` |
| **Author** | [Explosion](explosion.ai) |
> The dataset that this model is trained on has known flaws described [here](https://github.com/google-research/google-research/tree/master/goemotions#disclaimer) as well as label errors resulting from [annotator disagreement](https://www.youtube.com/watch?v=khZ5-AN-n2Y). Anyone using this model should be aware of these limitations of the dataset.
### Label Scheme
<details>
<summary>View label scheme (28 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`textcat_multilabel`** | `admiration`, `amusement`, `anger`, `annoyance`, `approval`, `caring`, `confusion`, `curiosity`, `desire`, `disappointment`, `disapproval`, `disgust`, `embarrassment`, `excitement`, `fear`, `gratitude`, `grief`, `joy`, `love`, `nervousness`, `optimism`, `pride`, `realization`, `relief`, `remorse`, `sadness`, `surprise`, `neutral` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `CATS_SCORE` | 90.22 |
| `CATS_MICRO_P` | 66.67 |
| `CATS_MICRO_R` | 47.81 |
| `CATS_MICRO_F` | 55.68 |
| `CATS_MACRO_P` | 55.00 |
| `CATS_MACRO_R` | 41.93 |
| `CATS_MACRO_F` | 46.29 |
| `CATS_MACRO_AUC` | 90.22 |
| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
| `TRANSFORMER_LOSS` | 83.51 |
| `TEXTCAT_MULTILABEL_LOSS` | 4549.84 |
|
{"language": ["en"], "license": "mit", "tags": ["spacy", "text-classification"], "model-index": [{"name": "en_textcat_goemotions", "results": []}]}
|
text-classification
|
explosion/en_textcat_goemotions
|
[
"spacy",
"text-classification",
"en",
"license:mit",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#spacy #text-classification #en #license-mit #region-us
|
spaCy Project: Categorization of emotions in Reddit posts (Text Classification) This project uses spaCy to train a text classifier on the GoEmotions dataset
============================================================================================================================================================
>
> The dataset that this model is trained on has known flaws described here as well as label errors resulting from annotator disagreement. Anyone using this model should be aware of these limitations of the dataset.
>
>
>
### Label Scheme
View label scheme (28 labels for 1 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (28 labels for 1 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #text-classification #en #license-mit #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (28 labels for 1 components)",
"### Accuracy"
] |
[
21,
16,
5
] |
[
"passage: TAGS\n#spacy #text-classification #en #license-mit #region-us \n### Label Scheme\n\n\n\nView label scheme (28 labels for 1 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_English-EWT
| Feature | Description |
| --- | --- |
| **Name** | `en_udv25_englishewt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1760 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `GW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`morphologizer`** | `Number=Sing\|POS=PROPN`, `POS=PUNCT`, `Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `POS=ADP`, `Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|POS=DET\|PronType=Art`, `POS=AUX\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `POS=VERB\|VerbForm=Ger`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `POS=SCONJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `NumType=Card\|POS=NUM`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Ger`, `POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|POS=PROPN`, `Degree=Pos\|NumType=Ord\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=CCONJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PRON`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=DET`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADV`, `Degree=Cmp\|POS=ADV`, `Number=Sing\|POS=PRON`, `Degree=Cmp\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV\|PronType=Dem`, `POS=ADV\|PronType=Int`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Degree=Sup\|POS=ADJ`, `POS=PRON\|PronType=Int`, `NumType=Mult\|POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `POS=DET\|PronType=Int`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=DET\|PronType=Dem`, `POS=PRON\|Poss=Yes\|PronType=Int`, `Case=Acc\|POS=PRON\|Person=2\|PronType=Prs`, `POS=X`, `POS=PRON\|PronType=Dem`, `Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=ADV\|PronType=Rel`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `POS=INTJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=X`, `POS=SYM`, `Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|POS=AUX\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|POS=CCONJ`, `POS=SCONJ\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=SYM`, `POS=DET\|Typo=Yes`, `Degree=Pos\|POS=PROPN`, `Abbr=Yes\|POS=ADP`, `POS=ADP\|Typo=Yes`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Abbr=Yes\|POS=PART`, `POS=AUX\|Typo=Yes\|VerbForm=Fin`, `Degree=Pos\|POS=ADJ\|Typo=Yes`, `POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=NOUN\|Typo=Yes`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Abbr=Yes\|Number=Sing\|POS=NOUN`, `Degree=Pos\|POS=NOUN`, `POS=CCONJ\|Typo=Yes`, `Number=Sing\|POS=X`, `Abbr=Yes\|POS=SCONJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|POS=AUX\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `POS=ADV\|Typo=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=NUM`, `POS=PRON\|Poss=Yes\|PronType=Rel`, `Abbr=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|POS=INTJ`, `Abbr=Yes\|POS=VERB\|VerbForm=Inf`, `Abbr=Yes\|Number=Sing\|POS=PRON`, `Abbr=Yes\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=PRON\|PronType=Int`, `Abbr=Yes\|POS=AUX\|VerbForm=Fin`, `Abbr=Yes\|POS=ADV`, `Abbr=Yes\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Mood=Ind\|POS=AUX\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `POS=ADJ`, `Number=Plur\|POS=NOUN\|Typo=Yes`, `POS=DET\|PronType=Rel\|Typo=Yes`, `POS=PART\|Typo=Yes`, `Abbr=Yes\|POS=DET`, `POS=DET\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Degree=Pos\|NumType=Ord\|POS=ADV`, `POS=NOUN`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `POS=PRON\|Typo=Yes`, `Number=Plur\|POS=VERB`, `POS=VERB\|Typo=Yes\|VerbForm=Inf`, `Mood=Ind\|POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Fin`, `Mood=Imp\|POS=AUX\|VerbForm=Inf`, `Abbr=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Abbr=Yes\|Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Mood=Ind\|POS=AUX\|Tense=Past\|Typo=Yes\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=VERB\|Typo=Yes\|VerbForm=Ger`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Abbr=Yes\|POS=PRON`, `Abbr=Yes\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:predet`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nmod:npmod`, `nmod:poss`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:npmod`, `obl:tmod`, `orphan`, `parataxis`, `punct`, `reparandum`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `2`, `4`, `6`, `8`, `10`, `12`, `13`, `15`, `17`, `19`, `21`, `23`, `26`, `28`, `29`, `30`, `32`, `34`, `36`, `39`, `42`, `43`, `45`, `47`, `49`, `51`, `53`, `55`, `57`, `59`, `61`, `62`, `64`, `67`, `69`, `71`, `73`, `75`, `77`, `79`, `81`, `83`, `85`, `87`, `1`, `89`, `90`, `92`, `94`, `95`, `97`, `99`, `101`, `105`, `106`, `108`, `110`, `111`, `112`, `113`, `115`, `117`, `119`, `121`, `122`, `124`, `125`, `126`, `127`, `128`, `129`, `130`, `132`, `133`, `136`, `137`, `138`, `139`, `142`, `143`, `145`, `150`, `153`, `156`, `157`, `159`, `162`, `163`, `164`, `167`, `169`, `171`, `174`, `176`, `177`, `179`, `182`, `184`, `187`, `189`, `191`, `193`, `194`, `197`, `198`, `201`, `203`, `204`, `208`, `210`, `211`, `213`, `214`, `215`, `217`, `220`, `221`, `224`, `225`, `227`, `229`, `231`, `233`, `235`, `236`, `239`, `241`, `242`, `244`, `246`, `247`, `248`, `249`, `250`, `251`, `252`, `254`, `256`, `258`, `259`, `261`, `263`, `264`, `265`, 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`2010`, `2011`, `2012`, `2013`, `2014`, `2015`, `2016`, `2017`, `2019`, `2020`, `2021`, `2022`, `2023`, `2024`, `2025`, `2026`, `2029`, `2030`, `2031`, `2032`, `2033`, `2034`, `2035`, `2037`, `2038`, `2039`, `2040`, `2041`, `2042`, `2043`, `2044`, `2045`, `2047` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.15 |
| `TOKEN_P` | 99.18 |
| `TOKEN_R` | 99.11 |
| `TOKEN_ACC` | 99.83 |
| `SENTS_F` | 90.62 |
| `SENTS_P` | 90.99 |
| `SENTS_R` | 90.26 |
| `TAG_ACC` | 96.36 |
| `POS_ACC` | 96.94 |
| `MORPH_ACC` | 96.91 |
| `DEP_UAS` | 91.90 |
| `DEP_LAS` | 89.42 |
| `LEMMA_ACC` | 97.36 |
|
{"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/en_udv25_englishewt_trf
|
[
"spacy",
"token-classification",
"en",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#spacy #token-classification #en #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_English-EWT
### Label Scheme
View label scheme (1760 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1760 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #en #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1760 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #en #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1760 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Spanish-AnCora
| Feature | Description |
| --- | --- |
| **Name** | `es_udv25_spanishancora_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `GNU GPL 3.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (2060 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `AUX_PRON`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `PUNCT_VERB_PRON_PUNCT`, `SCONJ`, `SYM`, `VERB`, `VERB_PRON`, `X` |
| **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Preppron\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `AdpType=Prep\|POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PROPN`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=PRON\|PronType=Int,Rel`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=SCONJ`, `POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Number=Plur\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=PUNCT\|PunctType=Peri`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Comm`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `POS=ADV`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `POS=PRON\|PronType=Ind`, `POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `POS=PUNCT\|PunctType=Quot`, `POS=PUNCT`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `NumType=Card\|POS=NUM`, `POS=VERB\|VerbForm=Ger`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADV`, `POS=AUX\|VerbForm=Inf`, `Number=Plur\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc,Dat\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `AdvType=Tim\|POS=NOUN`, `AdpType=Prep\|POS=ADV`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `NumType=Card\|Number=Plur\|POS=NUM`, `AdpType=Preppron\|POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `NumForm=Digit\|POS=NOUN`, `Number=Sing\|POS=PRON\|PronType=Dem`, `AdpType=Preppron\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|POS=NOUN`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `AdvType=Tim\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PUNCT\|PunctType=Colo`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PUNCT\|PunctType=Semi`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=INTJ`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `AdpType=Prep\|POS=ADJ`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PUNCT\|PunctType=Dash`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|POS=NOUN`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc,Dat\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=NOUN\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `POS=DET\|PronType=Ind`, `POS=DET\|PronType=Int,Rel`, `AdvType=Tim\|POS=ADV`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Degree=Abs\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc,Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Degree=Abs\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Case=Acc,Dat\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=SCONJ\|PronType=Int,Rel`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=SYM`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Com\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Pre\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=NOUN\|VerbForm=Fin`, `Case=Acc,Dat\|Mood=Imp\|Number=Plur,Sing\|POS=VERB\|Person=1,2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Case=Acc,Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Number=Sing\|POS=VERB\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Abs\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Dat\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `AdpType=Prep\|Degree=Cmp\|POS=ADV`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Fin`, `Case=Acc,Dat\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Ind`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|PunctType=Quot\|VerbForm=Inf`, `Case=Com\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Case=Dat\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Ind`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=PRON\|PronType=Dem`, `POS=AUX\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `AdvType=Tim\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Ind`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN\|VerbForm=Part`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Ind`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Com\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `POS=X`, `Case=Com\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `POS=ADP`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=AUX\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `Case=Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=NOUN\|PunctType=Comm`, `Degree=Cmp\|POS=ADJ`, `Gender=Masc\|POS=ADJ`, `Degree=Abs\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `POS=PRON\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Ind`, `Number=Sing\|POS=DET\|PronType=Int,Rel` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:pass`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `5`, `6`, `8`, `10`, `14`, `16`, `18`, `20`, `22`, `24`, `25`, `27`, `29`, `33`, `36`, `38`, `40`, `42`, `45`, `48`, `50`, `54`, `57`, `59`, `60`, `62`, `64`, `66`, `68`, `71`, `73`, `75`, `77`, `81`, `83`, `85`, `87`, `88`, `91`, `93`, `95`, `97`, `99`, `100`, `102`, `104`, `106`, `108`, `110`, `112`, `114`, `115`, `117`, `119`, `120`, `122`, `49`, `125`, `126`, `128`, `130`, `134`, `138`, `140`, `143`, `145`, `146`, `148`, `150`, `151`, `153`, `156`, `158`, `160`, `162`, `164`, `167`, `170`, `171`, `173`, `177`, `178`, `179`, `181`, `182`, `184`, `186`, `187`, `188`, `191`, `193`, `195`, `198`, `201`, `202`, `13`, `204`, `206`, `208`, `210`, `214`, `216`, `218`, `221`, `223`, `224`, `226`, `228`, `230`, `232`, `234`, `235`, `237`, `239`, `241`, `242`, `244`, `248`, `250`, `254`, `257`, `258`, `260`, `261`, `262`, `264`, `265`, `266`, `267`, `269`, `271`, `273`, `277`, `278`, `280`, `284`, `286`, `288`, `289`, `290`, `291`, `293`, `296`, `298`, `300`, `302`, `304`, `306`, `308`, `309`, `313`, `315`, `319`, `321`, `322`, `323`, `324`, `325`, `327`, `328`, `330`, `332`, `336`, `338`, `339`, `341`, `342`, `343`, `345`, `347`, `348`, `350`, `351`, `352`, `354`, `355`, `357`, `359`, `361`, `363`, `365`, `367`, `370`, `372`, `375`, `377`, `379`, `382`, `385`, `389`, `391`, `393`, `395`, `397`, `398`, `400`, `402`, `404`, `408`, `410`, `413`, `415`, `416`, `418`, `419`, `420`, `422`, `424`, `427`, `429`, `431`, `433`, `434`, `435`, `436`, `438`, `440`, `441`, `443`, `445`, `447`, `448`, `450`, `451`, `452`, `454`, `456`, `457`, `458`, `460`, `462`, `463`, `465`, `466`, `468`, `470`, `473`, `477`, `478`, `480`, `481`, `483`, `485`, `489`, `491`, `492`, `494`, `496`, `498`, `500`, `501`, `504`, `505`, `506`, `507`, `509`, `511`, `514`, `516`, `519`, `521`, `522`, `524`, `526`, `528`, `532`, `535`, `538`, `541`, `543`, `545`, `546`, `548`, `550`, `554`, `555`, `557`, `559`, `560`, `561`, `562`, `564`, `565`, `567`, `569`, `571`, `572`, `573`, `575`, `576`, `579`, `582`, `584`, `586`, `589`, `590`, `591`, `592`, `595`, `596`, `597`, `599`, `600`, `601`, `603`, `606`, `607`, `608`, `610`, `615`, `617`, `618`, `622`, `624`, `625`, `626`, `627`, `629`, `631`, `633`, `585`, `634`, `636`, `637`, `638`, `639`, `643`, `644`, `646`, `647`, `648`, `650`, `651`, `653`, `654`, `657`, `658`, `660`, `662`, `663`, `667`, `669`, `671`, `673`, `674`, `678`, `680`, `683`, `684`, `685`, `686`, `688`, `689`, `692`, `693`, `695`, `696`, `697`, `699`, `701`, `702`, `704`, `707`, `709`, `711`, `712`, `714`, `715`, `717`, `718`, `719`, `720`, `722`, `725`, `728`, `730`, `732`, `733`, `734`, `735`, `736`, `738`, `739`, `740`, `741`, `743`, `745`, `748`, `750`, `752`, `753`, `755`, `756`, `759`, `760`, `763`, `764`, `765`, `766`, `768`, `770`, `772`, `773`, `774`, `775`, `776`, `778`, `779`, `780`, `783`, `785`, `786`, `788`, `791`, `793`, `795`, `797`, `798`, `800`, `803`, `804`, `805`, `807`, `808`, `810`, `813`, `816`, `819`, `821`, `823`, `824`, `825`, `826`, `829`, `832`, `833`, `836`, `129`, `837`, `838`, `839`, `843`, `845`, `846`, `848`, `849`, `851`, `852`, `853`, `855`, `856`, `857`, `858`, `862`, `864`, `866`, `868`, `869`, `873`, `875`, `877`, `878`, `879`, `882`, `884`, `886`, `888`, `890`, `891`, `892`, `893`, `895`, `897`, `898`, `900`, `902`, `904`, `906`, `907`, `909`, `910`, `912`, `914`, `915`, `916`, `918`, `920`, `921`, `923`, `924`, `926`, `928`, `930`, `931`, `933`, `935`, `936`, `937`, `939`, `940`, `943`, `944`, `945`, `946`, `947`, `949`, `951`, `952`, `953`, `955`, `956`, `957`, `0`, `959`, `961`, `963`, `965`, `966`, `968`, `969`, `970`, `972`, `973`, `975`, `976`, `978`, `979`, `980`, `982`, `983`, `984`, `986`, `987`, `989`, `990`, `993`, `995`, `996`, `997`, `1000`, `1003`, `1004`, `1006`, `1007`, `1008`, `1010`, `1012`, `1013`, `1014`, `1015`, `1017`, `1018`, `1021`, `1025`, `1027`, `1029`, `1030`, `1032`, `1034`, `1035`, `1036`, `1038`, `1039`, `1041`, `1043`, `1044`, `1045`, `1046`, `1047`, `1049`, `1050`, `1052`, `1053`, `1054`, `1055`, `1056`, `1057`, `1058`, `1060`, `1061`, `1063`, `1065`, `1067`, `1069`, `1070`, `1072`, `1075`, `1076`, `1077`, `1078`, `1079`, `1080`, `1081`, `1082`, `1085`, `1086`, `1088`, `1090`, `1091`, `1092`, `1093`, `1094`, `1096`, `1097`, `1100`, `1101`, `1103`, `1104`, `1106`, `1108`, `1109`, `1111`, `1112`, `1114`, `1115`, `1116`, `598`, `26`, `1117`, `1118`, `1119`, `1121`, `1122`, `1123`, `1124`, `1125`, `1127`, `1128`, `1130`, `1132`, `1133`, `1135`, `1137`, `1139`, `1140`, `1141`, `1142`, `1144`, `1147`, `1151`, `1152`, `1153`, `1155`, `1157`, `1160`, `1162`, `1163`, `1165`, `1166`, `1170`, `1171`, `1173`, `1175`, `1177`, `1179`, `1180`, `1183`, `1185`, `1186`, `1188`, `1189`, `1191`, `1192`, `1193`, `1196`, `65`, `1197`, `1198`, `1202`, `1204`, `1206`, `1208`, `1209`, `1210`, `1213`, `1214`, `1215`, `1218`, `1220`, `1221`, `1223`, `1225`, `1226`, `1228`, `1230`, `1232`, `1233`, `1235`, `1236`, `1237`, `1238`, `1241`, `1242`, `1243`, `1244`, `1248`, `1253`, `1254`, `1256`, `1259`, `1260`, `1262`, `1264`, `1265`, `1266`, `1267`, `1269`, `1272`, `1273`, `1274`, `1275`, `1277`, `1280`, `1283`, `1286`, `1289`, `1291`, `1293`, `1294`, `1295`, `1296`, `1297`, `1298`, `1300`, `1301`, `1303`, `1307`, `1309`, `1311`, `1312`, `1316`, `1317`, `1318`, `1319`, `1321`, `1322`, `1323`, `1324`, `1325`, `1326`, `1327`, `1329`, `1330`, `1331`, `1332`, `1333`, `1334`, `1335`, `1336`, `1338`, `1339`, `1341`, `1342`, `1344`, `1346`, `1347`, `1348`, `1349`, `1350`, `1351`, `1352`, `1354`, `1356`, `1357`, `1359`, `1360`, `1361`, `1363`, `1364`, `1365`, `1369`, `1370`, `1371`, `1372`, `1373`, `1377`, `1378`, `1379`, `1381`, `1382`, `1383`, `1385`, `1386`, `1388`, `1389`, `1390`, `1391`, `1392`, `1394`, `1395`, `1396`, `1398`, `1399`, `1400`, `1402`, `1403`, `1406`, `1408`, `1409`, `1410`, `1413`, `1415`, `1416`, `1417`, `1418`, `1419`, `1421`, `1422`, `1423`, `1425`, `1427`, `1428`, `1431`, `1432`, `1433`, `1434`, `1435`, `1437`, `1438`, `1441`, `1442`, `1443`, `1445`, `1446`, `1447`, `1448`, `1449`, `1450`, `1452`, `1453`, `1454`, `1455`, `1457`, `1458`, `1460`, `1462`, `1463`, `1464`, `1467`, `1468`, `1469`, `1470`, `1472`, `1477`, `1479`, `1481`, `1484`, `1486`, `1488`, `1489`, `1492`, `1494`, `1495`, `1496`, `1498`, `1500`, `1501`, `1503`, `1504`, `1505`, `1507`, `1509`, `1510`, `1512`, `1513`, `1514`, `1516`, `1518`, `1519`, `1520`, `1523`, `1525`, `1526`, `1527`, `1529`, `1531`, `1532`, `1533`, `1535`, `1536`, `1537`, `1538`, `1540`, `1541`, `1542`, `1544`, `1546`, `1547`, `1548`, `124`, `1549`, `1551`, `1553`, `1555`, `1557`, `1560`, `1561`, `1563`, `1564`, `1565`, `1569`, `1571`, `1572`, `1573`, `1574`, `1575`, `1577`, `1579`, `1581`, `1582`, `1583`, `1585`, `1588`, `1589`, `1590`, `1591`, `1592`, `1595`, `1596`, `1597`, `1598`, `1599`, `1600`, `1601`, `1603`, `1605`, `1609`, `1611`, `1613`, `1614`, `1618`, `1619`, `1622`, `1624`, `1626`, `1628`, `1630`, `1631`, `1634`, `1636`, `1637`, `1638`, `1640`, `1642`, `1643`, `1644`, `1645`, `1646`, `1648`, `1649`, `1650`, `1651`, `1652`, `1653`, `1654`, `1656`, `1658`, `1660`, `1662`, `1665`, `1667`, `1668`, `1669`, `1671`, `1672`, `1673`, `1674`, `1675`, `1676`, `1678`, `1680`, `1681`, `1682`, `1683`, `1684`, `1685`, `1686`, `1688`, `1689`, `1690`, `1691`, `1692`, `1694`, `1696`, `1697`, `1698`, `1700`, `1701`, `1702`, `1703`, `1704`, `1706`, `1708`, `1709`, `1710`, `1711`, `1712`, `1713`, `1714`, `1715`, `1717`, `1718`, `1719`, `1721`, `1722`, `1724`, `1725`, `1726`, `1728`, `1729`, `1730`, `1731`, `1732`, `1733`, `1735`, `1737`, `1739`, `1741`, `1743`, `1744`, `1745`, `1747`, `1749`, `1750`, `1752`, `1753`, `1756`, `1758`, `1760`, `1761`, `1762`, `1764`, `1765`, `1767`, `1769`, `1772`, `1773`, `1774`, `1775`, `1777`, `1778`, `1781`, `1783`, `1784`, `1786`, `1790`, `1791`, `1792`, `1793`, `1795`, `1796`, `1798`, `1799`, `1801`, `1802`, `1804`, `1805`, `1806`, `1807`, `1809`, `1810`, `1811`, `1814`, `1816`, `1817`, `1818`, `1819`, `1820`, `1822`, `1824`, `1826`, `1827`, `1829`, `1831`, `1832`, `1834`, `1836`, `1838`, `1840`, `1842`, `1843`, `1844`, `1845`, `1847`, `1848`, `1850`, `1851`, `1853`, `1854`, `1856`, `1859`, `1860`, `1861`, `1863`, `1865`, `1866`, `1868`, `1869`, `1870`, `1871`, `1873`, `1875`, `1877`, `1879`, `1881`, `1883`, `1884`, `1887`, `1889`, `1890`, `1892`, `1893`, `1894`, `1895`, `1897`, `1899`, `1902`, `1903`, `1904`, `1906`, `1907`, `1909`, `1910`, `1912`, `1913`, `1914`, `1916`, `1917`, `1918`, `1920`, `1921`, `1923`, `1926`, `1927`, `1928`, `1929`, `1930`, `1931`, `1932`, `1933`, `1934`, `1935`, `1937`, `1938`, `1939`, `1942`, `1943`, `1944`, `1945`, `1946`, `1947`, `1948`, `1949`, `1950`, `1952`, `1953`, `1955`, `1956`, `1957`, `1958`, `1959`, `1961`, `1964`, `1967`, `1969`, `1971`, `1972`, `1974`, `1975`, `1977`, `1978`, `1979`, `1980`, `1981`, `1922`, `1982`, `1983`, `1984`, `1986`, `1988`, `1989`, `1990`, `1992`, `1993`, `1994`, `1995`, `1998`, `1999`, `2000`, `2003`, `2006`, `2007`, `2008`, `2009`, `2011`, `2013`, `2015`, `2016`, `2017`, `2018`, `2020`, `2023`, `2027`, `2028`, `2030`, `2031`, `2032`, `2033`, `2034`, `2035`, `2036`, `2039`, `2042`, `2043`, `2045`, `2047`, `2050`, `2052`, `2053`, `2054`, `2055`, `2056`, `2057`, `2061`, `2062`, `2063`, `2064`, `2065`, `2066`, `2067`, `2068`, `2069`, `2070`, `2073`, `2074`, `2075`, `2076`, `2078`, `2079`, `2080`, `2081`, `2082`, `2083`, `2084`, `2089`, `2090`, `2092`, `2093`, `2094`, `2095`, `2096`, `2098`, `2099`, `2100`, `2101`, `2103`, `2104`, `2106`, `2108`, `2109`, `2110`, `2113`, `2116`, `2119`, `2121`, `2124`, `2125`, `2126`, `2127`, `2128`, `2129`, `2132`, `2133`, `2134`, `2136`, `2137`, `2138`, `2139`, `2140`, `2141`, `2142`, `2143`, `2145`, `2146`, `2147`, `2148`, `2149`, `2150`, `2151`, `2152`, `2153`, `2154`, `2155`, `2157`, `2159`, `2160`, `2161`, `2162`, `2163`, `2164`, `2166`, `2167`, `2169`, `2172`, `2173`, `2174`, `2175`, `2178`, `2180`, `2181`, `2184`, `2186`, `2189`, `2190`, `2191`, `2192`, `2194`, `2195`, `2197`, `2199`, `2200`, `2202`, `2203`, `2204`, `2205`, `2210`, `2211`, `2212`, `2214`, `2215`, `2216`, `2217`, `2218`, `2219`, `2220`, `2221`, `2222`, `2223`, `2225`, `2227`, `2228`, `2229`, `2230`, `2231`, `2232`, `2233`, `2234`, `2235`, `2238`, `2239`, `2240`, `2241`, `2242`, `2243`, `2244`, `2245`, `2246`, `2250`, `2252`, `2254`, `2255`, `2256`, `2257`, `2258`, `2259`, `2260`, `2262`, `2264`, `2265`, `2266`, `2267`, `2268`, `2269`, `2270`, `2271`, `2272`, `2273`, `2274`, `2275`, `2276`, `2277`, `2278`, `2279`, `2280`, `2281`, `2283`, `2284`, `2285`, `2286`, `2287`, `2288`, `2289`, `2290`, `2291`, `2293`, `2294`, `2295`, `2296`, `2297`, `2298`, `2299`, `2301`, `2303`, `2304`, `2305`, `2306`, `2307`, `2308`, `2309`, `2310`, `2312`, `2313`, `2314`, `2315`, `2317`, `2319`, `2320`, `2321`, `2322`, `2324`, `2325`, `2326`, `2328`, `2329`, `2330`, `2331`, `2332`, `2333`, `2334`, `2335`, `2336`, `2337`, `2338`, `2339`, `2341`, `2342`, `2346`, `2347`, `2352`, `2353`, `2356`, `2358`, `2359`, `2360`, `2361`, `2362`, `2364`, `2365`, `2366`, `2368`, `2371`, `2372`, `2374`, `2375`, `2376`, `2377`, `2378`, `2379`, `2380`, `2382`, `2383`, `2384`, `2386`, `2387`, `2388`, `2389`, `2391`, `2394`, `2395`, `2396`, `2398`, `2399`, `2400`, `2401`, `2403`, `2404`, `2406`, `2409`, `2410`, `2411`, `2415`, `2418`, `2419`, `2420`, `2421`, `2422`, `2423`, `2424`, `2425`, `2427`, `430`, `2428`, `2429`, `2430`, `2431`, `2432`, `2433`, `2434`, `2435`, `2436`, `2437`, `2438`, `2439`, `2440`, `2441`, `2442`, `2444`, `2445`, `2446`, `2447`, `2448`, `2449`, `2450`, `2451`, `2452`, `2453`, `2454`, `2456`, `2457`, `2458`, `2460`, `2461`, `2462`, `2463`, `2464`, `2465`, `2466`, `2467`, `2468`, `2469`, `2472`, `2474`, `2475`, `2476`, `2479`, `2480`, `2481`, `2482`, `2483`, `2484`, `2486`, `2487`, `2488`, `2490`, `2491`, `2493`, `2494`, `2495`, `2496`, `2497`, `2499`, `2500`, `2501`, `2502`, `2503`, `2504`, `2505`, `2506`, `2507`, `2508`, `2509`, `2510`, `2511`, `2512`, `2514`, `2515`, `2516`, `2517`, `2518`, `2519`, `2520`, `2521`, `2522`, `2523`, `2524`, `2525`, `2527`, `2528`, `2529`, `2530`, `2531`, `2532`, `2533`, `2535`, `2536`, `2537`, `2538`, `2539`, `2540`, `2541`, `2542`, `2543`, `2544`, `2545`, `2546`, `2547`, `2548`, `2550`, `2552`, `2554`, `2555`, `2556`, `2557`, `2558`, `2559`, `2560`, `2561`, `2562`, `2563`, `2566`, `2567`, `2568`, `2569`, `2570`, `2572`, `2574`, `2576`, `2577`, `2578`, `2580`, `2582`, `2583`, `2584`, `2585` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.98 |
| `TOKEN_P` | 99.98 |
| `TOKEN_R` | 99.99 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 97.99 |
| `SENTS_P` | 97.43 |
| `SENTS_R` | 98.55 |
| `TAG_ACC` | 98.92 |
| `POS_ACC` | 99.03 |
| `MORPH_ACC` | 97.96 |
| `DEP_UAS` | 93.99 |
| `DEP_LAS` | 91.95 |
| `LEMMA_ACC` | 98.93 |
|
{"language": ["es"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/es_udv25_spanishancora_trf
|
[
"spacy",
"token-classification",
"es",
"license:gpl-3.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Spanish-AnCora
### Label Scheme
View label scheme (2060 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (2060 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2060 labels for 6 components)",
"### Accuracy"
] |
[
29,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (2060 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Finnish-TDT
| Feature | Description |
| --- | --- |
| **Name** | `fi_udv25_finnishtdt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (12912 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `A`, `Adj`, `Adp`, `Adv`, `Adv_V`, `C`, `C_V`, `Foreign`, `Interj`, `N`, `Num`, `Pron`, `Punct`, `Symb`, `V`, `V_Pron` |
| **`morphologizer`** | `Case=Nom\|Number=Sing\|POS=NOUN`, `NumType=Ord\|POS=ADJ`, `Case=Ade\|Number=Sing\|POS=NOUN`, `Case=Nom\|Derivation=U\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Case=Par\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Par\|Number=Plur\|POS=NOUN`, `Case=Ill\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Case=Nom\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `POS=SCONJ`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Case=Abl\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Clitic=Kaan\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Dem`, `Clitic=Kin\|POS=ADV`, `Case=Gen\|Number=Plur\|POS=PROPN`, `Case=Ess\|Number=Sing\|POS=NOUN`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ela\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `POS=ADJ`, `Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Par\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ine\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ade\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Sing\|POS=PROPN`, `Case=Par\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=All\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ill\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=All\|Derivation=U\|Number=Sing\|POS=NOUN`, `AdpType=Post\|POS=ADP`, `Case=Nom\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Abl\|Number=Sing\|POS=NOUN`, `Case=All\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Ine\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Par\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Par\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Gen\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Par\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Par\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Tra\|Number=Sing\|POS=NOUN`, `Case=Ela\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Par\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `InfForm=1\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ine\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `InfForm=1\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Derivation=Sti\|POS=ADV`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Plur\|POS=NOUN`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Par\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Ine\|Clitic=Kin\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=All\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Ill\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Par\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Case=All\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=All\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=All\|Number=Plur\|POS=NOUN`, `Case=Ela\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Clitic=Kaan\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Clitic=Ka\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Connegative=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Tra\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=0\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=All\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=All\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Clitic=Kin\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Par\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ill\|Derivation=Ja\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ine\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON`, `Case=Nom\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Clitic=Ko\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ine\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Ade\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Derivation=Ttain\|POS=ADV`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ine\|InfForm=2\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|VerbForm=Inf\|Voice=Act`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Ela\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ine\|Number=Plur\|POS=NOUN`, `Case=Com\|POS=NOUN\|Person[psor]=3`, `Case=Com\|POS=PRON\|Person[psor]=3\|PronType=Ind`, `Number[psor]=Sing\|POS=ADV\|Person[psor]=1`, `Case=Par\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Par\|Number=Sing\|POS=PRON\|PronType=Int`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Derivation=Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Par\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Ade\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Connegative=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Ill\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=SCONJ\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Par\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `AdpType=Post\|POS=ADP\|Person[psor]=3`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Ill\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Ill\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Nom\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Case=Ill\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=All\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Abbr=Yes\|Case=Ine\|Number=Sing\|POS=NOUN`, `Case=Ine\|InfForm=2\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person[psor]=1\|VerbForm=Inf\|Voice=Act`, `Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Number=Plur\|POS=NOUN`, `Case=Nom\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Par\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Ine\|Number=Sing\|POS=PROPN\|Style=Coll`, `Abbr=Yes\|Case=Par\|Number=Sing\|POS=NOUN`, `Case=Ess\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Ess\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=PROPN`, `Case=Par\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Ine\|InfForm=2\|Number=Sing\|POS=VERB\|Person[psor]=3\|VerbForm=Inf\|Voice=Act`, `NumType=Card\|POS=NUM`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ill\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ill\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|InfForm=2\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Gen\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=Ela\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Case=Ade\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ade\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ess\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Case=Ade\|Number=Sing\|POS=PRON\|PronType=Dem`, `Connegative=Yes\|Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Clitic=Ko\|Number=Sing\|POS=SCONJ\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Plur\|POS=PRON\|PronType=Dem`, `Connegative=Yes\|Mood=Cnd\|POS=AUX\|VerbForm=Fin`, `Case=Ela\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Par\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Par\|Derivation=Llinen,Vs\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `POS=SYM`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Rel`, `Clitic=Ka\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=0\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Clitic=Kaan\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ess\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=SCONJ\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Clitic=Kaan\|POS=ADV`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Par\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Ade\|Derivation=U\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=ADV`, `Case=Ine\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Par\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=1`, `Case=All\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `POS=ADV\|Typo=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ela\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ela\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=All\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ine\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Par\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Ela\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=All\|Clitic=Kin\|Number=Sing\|POS=PROPN`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Derivation=Vs\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=All\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `AdpType=Prep\|POS=ADP`, `Case=Par\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Ine\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Style=Coll`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Derivation=Vs\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Style=Coll`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Style=Coll`, `POS=INTJ`, `Case=Nom\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Par\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ess\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Style=Coll`, `Case=Ine\|InfForm=3\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Ela\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Dem`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ela\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ine\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Par\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Ine\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Ela\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Abl\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Ill\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Abl\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Abl\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Tra\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Ill\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Abe\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Tra\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ade\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Typo=Yes`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=All\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=All\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|Case=Nom\|Number=Sing\|POS=NOUN`, `Case=Nom\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Case=Par\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Clitic=Kin\|Mood=Cnd\|POS=AUX\|VerbForm=Fin\|Voice=Pass`, `Clitic=Han\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Derivation=U\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Ind`, `Abbr=Yes\|Case=Gen\|Number=Sing\|POS=PROPN`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=All\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Clitic=Han\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Derivation=Sti\|POS=ADV\|Typo=Yes`, `Case=All\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ill\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Case=Nom\|Derivation=Tar\|Number=Sing\|POS=NOUN`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Derivation=Minen\|Number=Plur\|POS=NOUN`, `Case=Ill\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Clitic=Kin\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ill\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Par\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Ade\|Number=Sing\|POS=PROPN`, `Case=Nom\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ess\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Clitic=Ka\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Derivation=U\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=NOUN\|Style=Coll`, `Case=Ill\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Clitic=Kaan\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Par\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Par\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=VERB\|Person=0\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Case=Ela\|Number=Sing\|POS=PROPN`, `Case=Nom\|Clitic=Pa\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=ADJ\|Typo=Yes`, `POS=ADV\|Style=Coll`, `Case=All\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Tra\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Par\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=All\|Number=Sing\|POS=NOUN\|Style=Coll`, `Clitic=Han\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Dem\|Typo=Yes`, `Case=Ine\|Derivation=Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Par\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Ine\|Number=Plur\|POS=PRON\|PronType=Dem`, `Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=AUX\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Clitic=Kin\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ade\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=All\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Case=Par\|NumType=Card\|Number=Plur\|POS=NUM\|Typo=Yes`, `Clitic=Ko\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Connegative=Yes\|Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Case=Ill\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Ela\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Abbr=Yes\|Case=Abl\|Number=Sing\|POS=PROPN`, `Case=Abl\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Kin\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Ade\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `Case=Ade\|Degree=Cmp\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Par\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|NumType=Card\|Number=Plur\|POS=NUM\|Typo=Yes`, `Case=Ess\|Number=Sing\|POS=PRON\|PronType=Dem`, `Clitic=Han\|POS=ADV`, `Case=Par\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Nom\|Clitic=Kin\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Par\|Derivation=Llinen\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Abl\|Number=Plur\|POS=NOUN`, `Case=Abl\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=All\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Par\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ine\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Par\|Derivation=Ton,Vs\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Ela\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Gen\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=All\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Case=Par\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Par\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Tra\|InfForm=1\|Number=Sing\|POS=VERB\|Person[psor]=3\|VerbForm=Inf\|Voice=Act`, `Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=All\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|Case=Ade\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Par\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Derivation=Inen\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=1`, `Case=Nom\|Clitic=Kin\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Clitic=Kaan\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|InfForm=2\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Ill\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Case=Par\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=All\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Nom\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=SCONJ\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Ill\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=All\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Derivation=U\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Abl\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ess\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ela\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ela\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Gen\|Derivation=Minen\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Clitic=Ko\|Number=Sing\|POS=VERB\|Person=0\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|InfForm=3\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Gen\|Clitic=Han\|Number=Sing\|POS=NOUN`, `Case=Ill\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Ess\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ela\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON\|Reflex=Yes`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Clitic=Kaan\|Connegative=Yes\|Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Degree=Sup\|Derivation=Sti\|POS=ADV`, `Case=Ine\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Case=Tra\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Par\|Derivation=Inen,Vs\|Number=Plur\|POS=NOUN`, `Case=Abl\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=All\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Ess\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Par\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|PronType=Rcp`, `Case=Par\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Ill\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Case=All\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Tra\|Clitic=Kin\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Case=Nom\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Ess\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Tra\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Clitic=Kin\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Ade\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Par\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Ess\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Clitic=Kin\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Com\|Derivation=U\|POS=NOUN\|Person[psor]=3`, `Case=Par\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=All\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Clitic=Kaan\|Connegative=Yes\|Mood=Cnd\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Ade\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Abl\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Derivation=Inen,Vs\|Number=Plur\|POS=NOUN`, `Case=Ill\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ine\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ess\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Abe\|InfForm=3\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|VerbForm=Inf\|Voice=Act`, `Clitic=Kaan\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Plur\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Degree=Cmp\|Derivation=Sti\|POS=ADV`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Nom\|Derivation=Ja,Tar\|Number=Sing\|POS=NOUN`, `Case=Par\|Degree=Sup\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Ess\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Clitic=Kin\|InfForm=1\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ela\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Case=Ine\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|Person[psor]=3\|PronType=Rcp`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ill\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ela\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Abe\|Clitic=Kaan\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Par\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Par\|Clitic=Kaan\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=Par\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Connegative=Yes\|Mood=Cnd\|POS=VERB\|VerbForm=Fin`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Abl\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Abl\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Abl\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Gen\|Derivation=U\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Ela\|Degree=Sup\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Ela\|Derivation=U\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Abl\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Ess\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Tra\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin\|Voice=Act`, `Clitic=Kaan\|Connegative=Yes\|Mood=Cnd\|POS=VERB\|VerbForm=Fin`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ill\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ill\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Par\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Ine\|Clitic=Han\|Number=Sing\|POS=NOUN`, `Mood=Pot\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|InfForm=3\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=All\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=All\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Clitic=Ko\|POS=CCONJ`, `Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `POS=NUM`, `Case=Par\|Clitic=Kin\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Degree=Cmp\|POS=ADV`, `Case=Ine\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|Case=Ela\|Number=Sing\|POS=NOUN`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=All\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Par\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ill\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ade\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Abbr=Yes\|POS=NOUN`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Person[psor]=3\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ela\|Derivation=Inen,Vs\|Number=Plur\|POS=NOUN`, `Case=Ine\|InfForm=2\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Par\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ade\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Ade\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Clitic=Han\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Plur\|POS=PRON\|Person[psor]=3\|PronType=Rcp`, `Case=Ela\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Par\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Tra\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Abl\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Abe\|Number=Sing\|POS=NOUN`, `Case=Par\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Ess\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Ess\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Derivation=Inen\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Ela\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=All\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ela\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=All\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Ill\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=All\|Number=Sing\|POS=PROPN`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=0\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Ade\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|Case=Gen\|Number=Sing\|POS=NOUN`, `Case=Gen\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Dem`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ine\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Ade\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=INTJ`, `Case=Ade\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ela\|Derivation=Llinen,Vs\|Number=Plur\|POS=NOUN`, `Case=Ade\|Number=Plur\|POS=PRON\|PronType=Dem`, `Clitic=Pa\|POS=ADV`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Ela\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Ess\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=0\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Ill\|Number=Plur\|POS=PROPN`, `Case=Gen\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=All\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Clitic=Pa,S\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko\|Connegative=Yes\|Mood=Cnd\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Par\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Han\|Number=Sing\|POS=NOUN`, `Case=Ill\|POS=PRON\|PronType=Rel`, `Clitic=Kin\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|Case=All\|Number=Sing\|POS=NOUN`, `Case=Abl\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Clitic=Ko\|POS=SCONJ`, `Case=Ess\|Number=Sing\|POS=PRON\|PronType=Dem\|Style=Coll`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Case=Nom\|Number=Plur\|POS=PRON\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=Gen\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|Style=Coll\|VerbForm=Part\|Voice=Act`, `Case=All\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ\|Style=Coll`, `Case=Ade\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ\|Style=Coll`, `Abbr=Yes\|Case=Par\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Connegative=Yes\|Mood=Cnd\|POS=AUX\|Style=Coll\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=AUX\|PartForm=Past\|Style=Coll\|VerbForm=Part\|Voice=Act`, `Case=All\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Ill\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Ill\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|NumType=Card\|Number=Sing\|POS=NUM\|Style=Coll`, `Case=Ill\|Clitic=Kaan\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Number[psor]=Plur\|POS=ADV\|Person[psor]=1`, `Abbr=Yes\|Case=Ine\|Number=Sing\|POS=PROPN`, `Case=Ela\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=All\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=Abl\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Par\|Degree=Cmp\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Par\|Derivation=Llinen\|Number=Plur\|POS=NOUN`, `Case=Par\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Clitic=Kin\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `AdpType=Post\|Number[psor]=Plur\|POS=ADP\|Person[psor]=1\|Style=Coll`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=All\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Abl\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Kaan\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Par\|Number=Plur\|POS=PROPN`, `Case=Par\|Degree=Cmp\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Clitic=Kaan\|Connegative=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Number=Plur\|POS=NOUN\|Style=Coll`, `Clitic=Ka\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Ill\|InfForm=3\|Number=Sing\|POS=VERB\|Style=Coll\|VerbForm=Inf\|Voice=Act`, `Case=Par\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Abbr=Yes\|Case=Nom\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Case=Ela\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Com\|Number=Plur\|POS=PROPN\|Person[psor]=3`, `Case=Ess\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Par\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=Abl\|Number=Sing\|POS=PRON\|Reflex=Yes`, `Clitic=Kin\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Clitic=Kaan\|NumType=Card\|Number=Sing\|POS=NUM`, `InfForm=1\|Number=Sing\|POS=VERB\|Style=Coll\|VerbForm=Inf\|Voice=Act`, `Case=Ill\|Clitic=Kaan\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Case=Nom\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Par\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Number=Sing\|POS=ADJ\|Style=Coll`, `POS=INTJ\|Style=Coll`, `Case=Ill\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=All\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Case=Par\|Derivation=U\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Mood=Ind\|POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Pass`, `Case=Par\|NumType=Ord\|Number=Sing\|POS=ADJ\|Style=Coll`, `Number=Plur\|POS=SCONJ\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Clitic=Kaan\|Connegative=Yes\|Mood=Ind\|POS=AUX\|Style=Coll\|Tense=Pres\|VerbForm=Fin`, `Case=Tra\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Com\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ine\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Ess\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Derivation=Lainen\|Number=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Abl\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ill\|Derivation=U\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Tra\|Number=Plur\|POS=NOUN`, `Case=Ine\|InfForm=2\|Number=Sing\|Number[psor]=Plur\|POS=AUX\|Person[psor]=1\|VerbForm=Inf\|Voice=Act`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Par\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Ade\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Style=Coll`, `Case=Ade\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ill\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ill\|NumType=Ord\|Number=Sing\|POS=ADJ`, `AdpType=Post\|POS=ADP\|Typo=Yes`, `Case=Ill\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Par\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ine\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Tra\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ess\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ine\|InfForm=2\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|VerbForm=Inf\|Voice=Act`, `Case=Tra\|Degree=Cmp\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Ade\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=2\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Par\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ine\|InfForm=3\|Number=Sing\|POS=VERB\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Par\|Clitic=Kin\|Derivation=U\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Clitic=Kin\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ine\|InfForm=2\|POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Ill\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Tra\|InfForm=1\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|VerbForm=Inf\|Voice=Act`, `Case=Ill\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Tra\|InfForm=1\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|VerbForm=Inf\|Voice=Act`, `Case=Ela\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ill\|InfForm=3\|Number=Sing\|POS=VERB\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Clitic=Pa,S\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Clitic=Pa,S\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=NOUN\|Style=Coll`, `Connegative=Yes\|Mood=Cnd\|POS=VERB\|Style=Coll\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Style=Coll`, `Clitic=Kin\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Ela\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person[psor]=3\|Style=Coll`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ill\|Derivation=U\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ela\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Style=Coll`, `Case=Ela\|Number=Plur\|POS=PRON\|Person[psor]=3\|PronType=Rcp`, `Case=Ela\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ill\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Ade\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=ADJ\|Style=Coll`, `Connegative=Yes\|Mood=Pot\|POS=AUX\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|PronType=Prs\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko,S\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Abl\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Clitic=Ko\|POS=ADV`, `Case=Par\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Par\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=Abl\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=NUM`, `POS=NOUN\|Typo=Yes`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ade\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=2\|VerbForm=Part\|Voice=Act`, `Case=Ine\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ine\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Clitic=Kin\|POS=SCONJ`, `Case=Nom\|Clitic=Kin\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Ade\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Par\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Neg\|VerbForm=Part\|Voice=Act`, `Clitic=Kaan\|Derivation=Sti\|POS=ADV`, `Case=Ill\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Ade\|Clitic=Kin\|Number=Plur\|POS=NOUN`, `Case=Par\|Clitic=Kin\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Clitic=Kin\|Number=Plur\|POS=NOUN`, `Case=All\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Ess\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Ess\|Number=Sing\|POS=PROPN`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ade\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ill\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=CCONJ\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Par\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Style=Coll`, `Case=Gen\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Style=Coll`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Par\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Past\|Person[psor]=1\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Clitic=Ka\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Gen\|NumType=Ord\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Int`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Par\|Derivation=Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ine\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=2\|VerbForm=Part\|Voice=Act`, `Case=Par\|Number=Plur\|POS=ADJ`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Tra\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Clitic=Kin\|InfForm=1\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Clitic=Kin\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=0\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Ade\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Degree=Cmp\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Ill\|Degree=Sup\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=AUX\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Clitic=Ko\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Han\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ade\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|POS=AUX\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Degree=Pos\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person[psor]=1`, `Case=Par\|Degree=Sup\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ine\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Derivation=Llinen,Vs\|Number=Plur\|POS=NOUN`, `Case=Par\|Degree=Pos\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|PartForm=Past\|Person[psor]=1\|VerbForm=Part\|Voice=Pass`, `Case=Tra\|InfForm=1\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|VerbForm=Inf\|Voice=Act`, `Case=Ine\|Degree=Pos\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Pass`, `Case=Ade\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ela\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=All\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=All\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|Person[psor]=3\|PronType=Ind`, `Case=Abl\|Number=Plur\|POS=PRON\|PronType=Ind`, `Clitic=Kaan\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Derivation=Llinen,Vs\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Par\|Derivation=Minen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ade\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Derivation=U\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|Degree=Sup\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Tra\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Tra\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Par\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=VERB\|PartForm=Neg\|VerbForm=Part\|Voice=Act`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Ade\|Number=Plur\|POS=PROPN`, `Case=Ess\|Number=Sing\|POS=PRON\|PronType=Rel`, `Connegative=Yes\|Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `POS=NOUN`, `Case=All\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ela\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Ill\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Abe\|Number=Plur\|POS=NOUN`, `Case=Ill\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Clitic=Kin\|Derivation=Sti\|POS=ADV`, `Case=Ine\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Degree=Pos\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Pass`, `Case=Ess\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Ela\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ade\|Number=Sing\|POS=PRON`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Clitic=Ka\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|PronType=Ind`, `Case=Gen\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Ill\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ins\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Ela\|Derivation=Inen,Vs\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=All\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Par\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Derivation=Ton,Vs\|Number=Plur\|POS=NOUN`, `Case=Ela\|Derivation=Ton,Vs\|Number=Plur\|POS=NOUN`, `Case=Par\|Derivation=Ton,Vs\|Number=Plur\|POS=NOUN`, `Case=Ade\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Par\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=2\|VerbForm=Part\|Voice=Act`, `Clitic=Kin\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Ill\|Degree=Cmp\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ine\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Number=Sing\|POS=ADV\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Tra\|InfForm=1\|Number=Sing\|Number[psor]=Plur\|POS=AUX\|Person[psor]=1\|VerbForm=Inf\|Voice=Act`, `Case=Gen\|Clitic=Kin\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Ess\|Degree=Pos\|Number=Plur\|POS=AUX\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ine\|InfForm=2\|Number=Sing\|POS=VERB\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Case=Abl\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ela\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Ela\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Ade\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ine\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Par\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ill\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Case=Ine\|Derivation=Minen\|Number=Plur\|POS=NOUN`, `Case=Ade\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Par\|Number=Plur\|POS=PRON\|Person[psor]=3\|PronType=Rcp`, `Case=Ine\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Tra\|Degree=Pos\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|PartForm=Pres\|Person[psor]=2\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Derivation=Minen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Degree=Pos\|POS=ADJ`, `Case=Ela\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Ine\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ela\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PROPN`, `Case=Ill\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Ela\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=All\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ess\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|Person[psor]=3`, `Case=Par\|Clitic=Kin\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=All\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Com\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=All\|Derivation=Ja\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Derivation=Vs\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ess\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Case=Ess\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Clitic=Han\|Degree=Pos\|Number=Sing\|POS=NOUN`, `Case=Ade\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Clitic=Han\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=All\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=All\|Derivation=Ja\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Degree=Sup\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Nom\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|Case=Ill\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Clitic=Ko\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Abl\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Clitic=Pa\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Par\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Abbr=Yes\|Case=Ela\|Number=Plur\|POS=NOUN`, `Case=All\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Clitic=Kaan\|Number=Sing\|POS=NUM\|PronType=Ind`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Par\|Clitic=Kaan\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ine\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Case=Ade\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Clitic=Ko\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Clitic=Kaan\|Number=Sing\|POS=NOUN`, `Case=Ine\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Degree=Sup\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ill\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Clitic=Ko\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Par\|Clitic=Kaan\|Number=Sing\|POS=NOUN`, `Case=Ine\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Tra\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Ade\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2\|Style=Coll`, `Case=Ill\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ade\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Derivation=Ton,Vs\|Number=Sing\|POS=NOUN`, `Case=Nom\|Clitic=Han\|Number=Plur\|POS=NOUN`, `Case=Ela\|Number=Plur\|POS=PROPN`, `Case=Gen\|Clitic=Kin\|Number=Sing\|POS=PROPN`, `Case=Par\|Derivation=Ja\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Derivation=Ton,Vs\|Number=Sing\|POS=NOUN`, `AdpType=Post\|Clitic=Kin\|POS=ADP`, `Case=Ade\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Number=Plur\|POS=PRON`, `Case=Par\|Derivation=U\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Abl\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PROPN`, `Case=All\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Case=Abe\|Clitic=Kaan\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=All\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Derivation=Llinen,Vs\|Number=Plur\|POS=NOUN`, `Case=All\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ela\|Derivation=Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ade\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Par\|Clitic=Kin\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Ill\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=All\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ine\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Ade\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=All\|Derivation=Lainen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Par\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|Derivation=Lainen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Par\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Abe\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Degree=Sup\|POS=ADV`, `Case=Tra\|Degree=Cmp\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Ill\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ill\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Degree=Pos\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=NUM`, `Number=Plur\|POS=ADV\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Connegative=Yes\|Mood=Cnd\|POS=AUX\|Typo=Yes\|VerbForm=Fin`, `Number=Sing\|POS=ADV\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Case=Ill\|Derivation=U\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ill\|Clitic=Kaan\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Clitic=Kaan\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Abl\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Dem\|Typo=Yes`, `Clitic=Han\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Number[psor]=Sing\|POS=ADV\|Person[psor]=2`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Clitic=Han\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Par\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Ind\|Typo=Yes`, `Case=Nom\|Clitic=Kaan\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Par\|Clitic=Kaan\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Abl\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Tra\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ill\|Clitic=Kin\|Number=Plur\|POS=NOUN`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person[psor]=1\|PronType=Rcp`, `Case=Ine\|InfForm=2\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|VerbForm=Inf\|Voice=Act`, `Case=Ela\|Derivation=U\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Gen\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=1`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Clitic=Kaan\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Kaan\|Number=Plur\|POS=PRON\|PronType=Ind`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=2`, `Case=All\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `AdpType=Post\|Number[psor]=Plur\|POS=ADP\|Person[psor]=2`, `Number=Sing\|POS=CCONJ\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `POS=CCONJ\|Style=Coll`, `Case=Tra\|InfForm=1\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Clitic=Kaan\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=0\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Number=Sing\|POS=SCONJ\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Tra\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Abl\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Clitic=Kaan\|Number=Sing\|POS=NOUN`, `Clitic=Kaan\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ade\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ine\|Number=Sing\|POS=PRON`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=All\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Kaan\|InfForm=1\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=All\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Ade\|Derivation=U\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ela\|Clitic=Kin\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Kaan\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Par\|Clitic=Kin\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Tra\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1\|Style=Arch`, `Case=Ill\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Number=Plur\|POS=CCONJ\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Sing\|POS=PRON`, `Clitic=Han\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Style=Coll\|VerbForm=Part\|Voice=Pass`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|Person[psor]=3\|PronType=Rcp`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Connegative=Yes\|Mood=Ind\|POS=AUX\|Style=Coll\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Clitic=Kaan\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Pa\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Clitic=Pa\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Derivation=Ja\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Ess\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Derivation=U\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ine\|InfForm=2\|Number=Sing\|POS=AUX\|Person[psor]=3\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Clitic=Han\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Tra\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Abl\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Abe\|InfForm=3\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Clitic=Ko\|InfForm=1\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Nom\|Degree=Sup\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Ade\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Case=Par\|Clitic=Kaan\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Derivation=Inen,Vs\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=1`, `Case=Tra\|InfForm=1\|Number=Sing\|POS=AUX\|Person[psor]=3\|VerbForm=Inf\|Voice=Act`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Sing\|POS=PRON\|PronType=Int\|Style=Coll`, `Case=Par\|Number=Sing\|POS=NUM`, `Case=Ess\|NumType=Ord\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Derivation=Minen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Clitic=Kin\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Past\|Person[psor]=1\|Style=Coll\|VerbForm=Part\|Voice=Pass`, `Case=Tra\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Ine\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ess\|Clitic=Kin\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Clitic=Ka\|Number=Sing\|POS=VERB\|Person=0\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Ind\|Style=Coll`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Degree=Sup\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ine\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Ine\|Clitic=Kaan\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ine\|Number=Sing\|POS=NUM`, `Case=All\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Ade\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Ade\|Degree=Sup\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Pa\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Ine\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Typo=Yes`, `Case=Com\|Degree=Pos\|POS=ADJ`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Clitic=Ko\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Kin\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ine\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Par\|Degree=Sup\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Pot\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Clitic=Han\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Derivation=Vs\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Clitic=Han\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Number=Plur\|POS=VERB\|Person=0\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=1`, `Case=Ill\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Mood=Pot\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `InfForm=1\|Number=Sing\|POS=VERB\|Style=Arch\|VerbForm=Inf\|Voice=Act`, `Case=All\|Degree=Pos\|Derivation=Minen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Case=Nom\|Degree=Sup\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Clitic=S\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Int\|Typo=Yes`, `Clitic=Han,Pa\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Abe\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Nom\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Derivation=Minen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Clitic=Han\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=All\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `POS=INTJ\|Typo=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=AUX\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Gen\|Derivation=U\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Clitic=Han,Ko\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Clitic=Ko\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ela\|Derivation=Ton,Vs\|Number=Sing\|POS=NOUN`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Derivation=Inen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=1`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Connegative=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=SCONJ\|Style=Coll`, `Clitic=Han\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Clitic=Kaan\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Ess\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `AdpType=Post\|Number[psor]=Sing\|POS=ADP\|Person[psor]=1`, `Case=Nom\|Clitic=Kaan\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Clitic=Pa\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Clitic=Ko\|Number=Sing\|POS=PRON\|PronType=Int`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Style=Coll\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `AdpType=Post\|Number[psor]=Sing\|POS=ADP\|Person[psor]=2\|Style=Coll`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Style=Coll`, `Connegative=Yes\|Mood=Ind\|POS=VERB\|Style=Coll\|Tense=Pres\|VerbForm=Fin`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Clitic=Kaan\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=All\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ade\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=All\|Number=Sing\|POS=PRON\|Reflex=Yes`, `Case=Tra\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Clitic=Kaan\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Clitic=S\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=0\|VerbForm=Fin\|Voice=Act`, `Clitic=Han,Pa\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Clitic=Kaan\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Ine\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Ade\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Ade\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Degree=Pos\|Derivation=Inen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=1`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Past\|Style=Coll\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|POS=VERB\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Par\|Number=Sing\|POS=PROPN\|Style=Coll`, `Clitic=Kin\|POS=ADV\|Style=Coll`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Derivation=Sti\|POS=ADV\|Style=Coll`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Clitic=Ko\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2\|Style=Coll`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Style=Coll`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Style=Coll`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=ADJ\|Style=Coll`, `AdpType=Post\|Number[psor]=Sing\|POS=ADP\|Person[psor]=1\|Style=Coll`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Style=Coll`, `Case=All\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Ess\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|Style=Coll\|VerbForm=Part\|Voice=Act`, `Case=Ins\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Number=Sing\|POS=PROPN`, `Clitic=Pa\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=AUX\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Pass`, `Case=All\|Clitic=Han\|Number=Sing\|POS=NOUN`, `Clitic=Han\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Derivation=U\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Ela\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Dem`, `Clitic=Pa\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Clitic=Ko\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Number=Sing\|POS=PRON`, `Case=Gen\|Number=Plur\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Case=Par\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Clitic=Kaan\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Clitic=Kin\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Clitic=Kaan\|Connegative=Yes\|Mood=Cnd\|POS=AUX\|VerbForm=Fin`, `Clitic=S\|POS=ADV`, `Case=Gen\|Clitic=Ko\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Clitic=Han\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=All\|Clitic=Kaan\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Par\|Clitic=Kin\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ade\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Clitic=Kin\|Number=Sing\|POS=PROPN`, `Case=Ins\|InfForm=2\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Ine\|Clitic=Kaan\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Number=Plur\|POS=ADV\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Ade\|Clitic=S\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Ade\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Style=Coll`, `Case=Ess\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Par\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Clitic=Han,Ko\|POS=ADV`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `AdpType=Post\|Number[psor]=Sing\|POS=ADP\|Person[psor]=2`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ela\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|PartForm=Pres\|Person[psor]=2\|VerbForm=Part\|Voice=Act`, `Clitic=Ka\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Style=Coll`, `Case=Nom\|Clitic=Kin\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Clitic=Ko\|Derivation=Sti\|POS=ADV`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=2`, `Case=Nom\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ess\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Ela\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=2\|Reflex=Yes`, `Case=Acc\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ess\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Ill\|Derivation=Ton,Vs\|Number=Sing\|POS=NOUN`, `Case=Par\|Clitic=Kin\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=2\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Clitic=Kin\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Clitic=Kaan\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ela\|Clitic=Kin\|Number=Plur\|POS=NOUN`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=1`, `Case=Ade\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Clitic=Ko,S\|POS=ADV\|Style=Coll`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Int\|Style=Coll`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Number=Sing\|POS=NOUN\|Style=Coll`, `Clitic=Ko\|POS=ADV\|Style=Coll`, `Case=Nom\|Derivation=U\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Dem\|Style=Coll`, `Connegative=Yes\|Mood=Cnd\|POS=VERB\|Style=Coll\|VerbForm=Fin`, `Case=Par\|Number=Sing\|POS=PRON\|PronType=Ind\|Style=Coll`, `Case=Abl\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Par\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2\|Style=Coll`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Number=Plur\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Ade\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=Ill\|Number=Sing\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=Ine\|Number=Sing\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=Nom\|Clitic=Kaan\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Style=Coll`, `Case=Par\|Degree=Cmp\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Ind\|Typo=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Style=Coll`, `Case=Nom\|Clitic=Kin\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ade\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Abl\|NumType=Card\|Number=Sing\|POS=NUM`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Ill\|Clitic=Kaan\|Number=Sing\|POS=NOUN`, `Case=Par\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ade\|Number=Plur\|POS=PRON\|PronType=Rcp\|Typo=Yes`, `Case=Ade\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Par\|Derivation=U\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `POS=ADV\|Person[psor]=3\|Typo=Yes`, `Clitic=Pa\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Par\|Clitic=Kaan\|NumType=Card\|Number=Sing\|POS=NUM`, `Clitic=Pa,S\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Number=Plur\|POS=PRON\|PronType=Dem\|Style=Coll`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Clitic=Han\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Clitic=Han,Ko\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Connegative=Yes\|Mood=Pot\|POS=VERB\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Style=Coll\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person[psor]=3\|PronType=Rcp`, `Number[psor]=Plur\|POS=ADV\|Person[psor]=2`, `Mood=Pot\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Abl\|Derivation=U\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=ADJ\|Person[psor]=3`, `Clitic=Pa\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Plur\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=Ine\|Number=Plur\|POS=PROPN`, `Case=All\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2\|Style=Coll`, `Case=All\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Style=Coll`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=All\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person[psor]=3`, `Case=Nom\|Clitic=Kaan\|Number=Sing\|POS=PROPN\|Style=Coll`, `Case=All\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Abl\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Ade\|Derivation=Vs\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Person[psor]=3\|VerbForm=Inf\|Voice=Act`, `Clitic=Kaan\|POS=ADV\|Style=Coll`, `Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Pa\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ess\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ill\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=Nom\|Clitic=Kin\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Clitic=Ko\|Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Ine\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Ko\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Clitic=Han,Ko\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Par\|Number=Plur\|POS=PRON\|Person[psor]=3`, `Case=Ade\|Number=Sing\|POS=PROPN\|Style=Coll`, `Case=Ess\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Ela\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Ela\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Style=Coll`, `Case=Ill\|Clitic=Kaan\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Clitic=Kin\|Mood=Cnd\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Ela\|Clitic=Ko\|Number=Sing\|POS=PROPN`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=2\|VerbForm=Part\|Voice=Act`, `Case=Ade\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Ine\|Derivation=Minen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ill\|Number=Sing\|POS=NUM`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Ind\|Style=Coll`, `Clitic=S\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=AUX\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Pass`, `Case=Abl\|Derivation=Inen,Vs\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Par\|Clitic=Kaan\|Number=Sing\|POS=PRON\|PronType=Dem\|Style=Coll`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Ko,S\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Style=Coll`, `Case=Ill\|Number=Sing\|POS=PRON`, `Case=Ill\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Clitic=Ko,S\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Ela\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Clitic=Kin\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Derivation=Minen\|Number=Plur\|POS=NOUN`, `AdpType=Post\|POS=ADP\|Style=Coll`, `Case=Gen\|Number=Plur\|POS=NUM`, `Case=Ela\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Par\|NumType=Card\|Number=Sing\|POS=NUM\|Style=Coll`, `Case=Gen\|Derivation=Ton\|Number=Plur\|POS=NOUN`, `Case=Nom\|Clitic=Ko\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|PronType=Prs\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Number=Sing\|POS=PRON\|Reflex=Yes`, `Case=All\|Clitic=Pa\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Abl\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Clitic=Kaan\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Abl\|Number=Plur\|POS=PRON\|Person[psor]=3`, `Case=Gen\|Clitic=Kin\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Gen\|Clitic=Kaan\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Int`, `Clitic=Han\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Clitic=Pa\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=All\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Gen\|Clitic=Han\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Clitic=Ko\|Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Ine\|Derivation=Inen,Vs\|Number=Plur\|POS=NOUN`, `Case=Ine\|Clitic=Kin\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Nom\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=All\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Abl\|Number=Sing\|POS=PRON\|PronType=Prs\|Style=Coll`, `Case=Par\|Clitic=Kin\|Number=Sing\|POS=NOUN\|Style=Coll`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Number=Sing\|POS=SCONJ\|Person=1\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Abl\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Par\|Clitic=Ko\|Number=Sing\|POS=NOUN`, `Mood=Pot\|Number=Sing\|POS=AUX\|Person=0\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=Ade\|Derivation=U\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Abl\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Clitic=Kin\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Clitic=Han\|Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Abl\|Number=Sing\|POS=PRON\|PronType=Dem\|Style=Coll`, `Case=Tra\|InfForm=1\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person[psor]=2\|VerbForm=Inf\|Voice=Act`, `Clitic=Han\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|Case=Nom\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Clitic=Kaan\|Connegative=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Ela\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `Clitic=Kaan\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Clitic=Kin\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|Person[psor]=3`, `Clitic=Pa,S\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Clitic=Pa\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Past\|Style=Coll\|VerbForm=Part\|Voice=Pass`, `Case=Ess\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Int`, `Clitic=Han\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes\|Style=Coll`, `Case=Par\|Clitic=Kin\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ine\|Derivation=Lainen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Ine\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ\|Style=Coll`, `Case=Ine\|Derivation=Vs\|Number=Plur\|POS=NOUN\|Style=Coll`, `Case=Par\|Derivation=Ja\|Number=Sing\|POS=NOUN\|Style=Coll`, `Case=Par\|Derivation=Lainen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Ine\|Number=Sing\|POS=PRON\|PronType=Ind\|Style=Coll`, `Case=Ine\|Derivation=Inen\|Number=Plur\|POS=ADJ\|Style=Coll`, `Case=Gen\|Degree=Sup\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=1`, `Case=Nom\|Clitic=Kin\|Number=Plur\|POS=NOUN\|Style=Coll`, `Case=Ine\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ela\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|PartForm=Agt\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=2`, `Case=Par\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=2\|Reflex=Yes`, `Case=Ade\|Number=Sing\|POS=PRON\|PronType=Rel\|Style=Coll`, `Clitic=Pa,S\|POS=ADV`, `Case=Ess\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Nom\|Clitic=Kin\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Clitic=Kaan\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|Style=Coll\|VerbForm=Part\|Voice=Act`, `Case=All\|Number=Plur\|POS=PROPN`, `Clitic=Ko,S\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=PRON\|PronType=Ind\|Style=Coll`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Clitic=Kaan\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ela\|Clitic=Han\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Clitic=Ko,S\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Clitic=Pa,S\|Number=Sing\|POS=VERB\|Person=0\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Clitic=Han,Ko\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Number=Sing\|POS=PRON`, `Case=Nom\|Clitic=Kaan\|POS=PRON\|PronType=Ind`, `Clitic=Pa\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=PRON\|PronType=Int\|Style=Coll`, `Connegative=Yes\|Mood=Pot\|POS=AUX\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Clitic=Kaan\|Connegative=Yes\|Mood=Ind\|POS=VERB\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=All\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Gen\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ\|Person[psor]=3`, `Case=Gen\|Clitic=Kin\|Degree=Pos\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Connegative=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Clitic=Han\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=All\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `Case=All\|Number=Plur\|POS=PRON`, `Case=Nom\|Clitic=Kaan\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Pot\|Number=Sing\|POS=AUX\|Person=0\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Degree=Pos\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Derivation=Ja\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Ess\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ade\|Degree=Sup\|Number=Plur\|POS=ADJ\|Person[psor]=3`, `Clitic=Han\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Com\|Derivation=Ja\|POS=NOUN\|Person[psor]=3`, `Clitic=Pa,S\|Mood=Ind\|POS=AUX\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ade\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Ela\|Number=Sing\|POS=PRON\|Person[psor]=3`, `Case=Ade\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Par\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ade\|Clitic=Kin\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Derivation=Lainen\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Sing\|POS=ADJ`, `Case=Ins\|Number=Plur\|POS=PROPN`, `Case=Gen\|Clitic=Kin\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Case=Gen\|POS=PRON\|Person[psor]=3\|PronType=Rcp`, `Case=Acc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Ess\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Clitic=Kin\|Number=Sing\|POS=PROPN`, `Case=Nom\|Degree=Cmp\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Derivation=Inen\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Ela\|Clitic=Kaan\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Ade\|Degree=Pos\|Number=Plur\|POS=ADJ\|Person[psor]=3`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Derivation=Inen\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Clitic=Kin\|Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Ill\|NumType=Card\|Number=Sing\|POS=NUM\|Style=Coll`, `Case=Ine\|Derivation=U\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ade\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Clitic=Kin\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Tra\|Clitic=Kin\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Sing\|POS=PROPN\|Style=Coll`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=ADJ\|Style=Coll`, `Case=Ine\|Number=Plur\|POS=NOUN\|Style=Coll`, `Clitic=Kaan\|InfForm=1\|Number=Sing\|POS=VERB\|Style=Coll\|VerbForm=Inf\|Voice=Act`, `Case=Ill\|Number=Plur\|POS=NOUN\|Style=Coll`, `Clitic=Han,Ko\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Clitic=Ko,S\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Style=Coll\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=AUX\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|PronType=Prs\|Style=Coll\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Abl\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ill\|Derivation=Llinen,Vs\|Number=Plur\|POS=NOUN`, `Case=All\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Derivation=Ton,Vs\|Number=Plur\|POS=NOUN`, `Case=Ela\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Pass`, `Case=Ela\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Ill\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=All\|Number=Plur\|POS=PRON\|Person[psor]=3\|PronType=Rcp`, `Case=Ela\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Mood=Ind\|POS=VERB\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Pass`, `Case=All\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=ADJ\|Typo=Yes`, `Case=Gen\|Degree=Pos\|Derivation=Inen\|POS=ADJ`, `Case=Ess\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Ess\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Case=Par\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Tra\|NumType=Card\|Number=Sing\|POS=NUM`, `Clitic=Pa\|Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Ine\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Derivation=Inen,Vs\|Number=Plur\|POS=NOUN`, `Case=Gen\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Gen\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Ela\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ela\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Tra\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|Case=Tra\|Number=Sing\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ess\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Tra\|Degree=Cmp\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Derivation=Minen\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Case=All\|Number=Sing\|POS=PROPN`, `Case=All\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Nom\|Derivation=U\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Gen\|NumType=Ord\|POS=ADJ`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=ADJ\|Typo=Yes`, `POS=X`, `Case=Par\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Style=Coll`, `Case=Gen\|Derivation=Inen\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=ADJ\|Style=Coll`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Ade\|Clitic=Kin\|Derivation=U\|Number=Sing\|POS=NOUN`, `Case=Nom\|Clitic=Kin\|Derivation=Ja\|Number=Plur\|POS=NOUN`, `Case=All\|Clitic=Kin\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=All\|Clitic=Kin\|Number=Plur\|POS=NOUN`, `Case=Ill\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Clitic=Kin\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Pres\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Clitic=Han\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ela\|Clitic=Kaan\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ade\|Derivation=U\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1\|Typo=Yes`, `Case=Nom\|Clitic=Kin\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Par\|NumType=Ord\|POS=ADJ`, `Case=Par\|Degree=Sup\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=All\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=All\|Clitic=Kin\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Derivation=U\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Derivation=Ja\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ade\|Clitic=Kaan\|Number=Sing\|POS=NOUN`, `Case=Ess\|Clitic=Kin\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Abl\|Degree=Pos\|Derivation=Ja\|Number=Plur\|POS=ADJ`, `Case=Tra\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Abbr=Yes\|Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ill\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ela\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person[psor]=2`, `Case=Nom\|Degree=Pos\|Number=Plur\|Number[psor]=Plur\|POS=AUX\|PartForm=Pres\|Person[psor]=2\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Neg\|VerbForm=Part\|Voice=Act`, `Clitic=Kin\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Clitic=Kin\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Tra\|Derivation=Vs\|Number=Sing\|POS=NOUN`, `Case=Ill\|Degree=Cmp\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Past\|Person[psor]=1\|VerbForm=Part\|Voice=Act`, `Case=Ins\|InfForm=3\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Gen\|Clitic=Kin\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person[psor]=1\|Reflex=Yes`, `Case=Nom\|Derivation=Ton\|Number=Plur\|POS=NOUN`, `Case=Ade\|Clitic=Han\|Number=Sing\|POS=NOUN`, `Case=Tra\|Degree=Sup\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Degree=Cmp\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Clitic=Kin\|InfForm=2\|Number=Sing\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Ela\|Number=Plur\|POS=PRON\|Person[psor]=3`, `Case=Gen\|Degree=Sup\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Par\|Clitic=S\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Abl\|Clitic=Pa\|Number=Sing\|POS=PRON\|PronType=Int`, `Clitic=Ko\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Clitic=Pa,S\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|PartForm=Pres\|Person[psor]=2\|VerbForm=Part\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=PROPN`, `Clitic=Kin\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Ade\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Clitic=Han\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ess\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN`, `Clitic=Han\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Clitic=Han\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Clitic=Pa,S\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=0\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Case=Nom\|Clitic=Kin\|Derivation=Inen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ade\|Number=Plur\|POS=PRON`, `Case=All\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Par\|Derivation=Lainen,Vs\|Number=Sing\|POS=NOUN`, `AdpType=Post\|Clitic=Kaan\|POS=ADP`, `AdpType=Prep\|POS=ADP\|Person[psor]=3`, `Case=Ine\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Ine\|Derivation=Ton,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Par\|Derivation=Lainen\|Number=Plur\|POS=ADJ\|Style=Coll`, `AdpType=Prep\|Clitic=Kaan\|POS=ADP`, `Case=Nom\|Clitic=Han\|Number=Sing\|POS=PROPN`, `Clitic=Pa\|InfForm=1\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Ade\|Clitic=Kaan\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ine\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ill\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Case=Ade\|Derivation=Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Derivation=U\|Number=Plur\|POS=NOUN`, `Case=Gen\|Clitic=Kaan\|Number=Sing\|POS=PROPN`, `Case=Com\|Clitic=Kin\|Derivation=U\|POS=NOUN\|Person[psor]=3`, `Case=Ade\|Derivation=U\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=2\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ill\|Clitic=Kin\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ade\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=All\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Case=Gen\|Derivation=U\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=All\|Degree=Sup\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Ill\|Number=Plur\|POS=PRON\|Person[psor]=3`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Degree=Sup\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Nom\|Degree=Sup\|Derivation=Ton\|Number=Sing\|POS=ADJ`, `Case=Par\|Derivation=Vs\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Par\|Degree=Cmp\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Ess\|Number=Sing\|POS=PRON\|PronType=Ind\|Typo=Yes`, `Case=Ela\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ill\|Derivation=Lainen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ill\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Clitic=Han\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Sup\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Case=Ela\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Case=Ess\|Degree=Sup\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Clitic=Kin\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Neg\|VerbForm=Part\|Voice=Act`, `Case=Ela\|Degree=Pos\|Derivation=Llinen\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Ela\|InfForm=3\|Number=Sing\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Com\|Degree=Pos\|Derivation=Inen\|POS=ADJ`, `Case=Com\|Degree=Pos\|Derivation=Llinen\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Typo=Yes`, `Connegative=Yes\|Mood=Cnd\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Derivation=Ton,Vs\|Number=Plur\|POS=NOUN`, `Case=Gen\|Derivation=Ja\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ine\|Clitic=Kin\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Gen\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ill\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ela\|Number=Sing\|POS=NUM`, `Abbr=Yes\|Case=Ill\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Par\|Derivation=Inen,Vs\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ela\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Tra\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ade\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Derivation=Tar\|Number=Plur\|POS=NOUN`, `Case=Gen\|Derivation=Tar\|Number=Sing\|POS=NOUN`, `Case=Ade\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ess\|Degree=Sup\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Abl\|Derivation=Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Clitic=Kaan\|Connegative=Yes\|Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Clitic=Kin\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Plur\|POS=PRON`, `Case=Ill\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Ade\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Ess\|Derivation=Lainen\|Number=Plur\|POS=NOUN`, `Case=Ade\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=Gen\|Derivation=Lainen,Vs\|Number=Sing\|POS=NOUN`, `Case=All\|Derivation=Lainen\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ine\|Degree=Cmp\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ill\|Derivation=Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `POS=PROPN\|Typo=Yes`, `Case=All\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Ine\|Number=Sing\|POS=PRON\|Person[psor]=3\|Reflex=Yes`, `Abbr=Yes\|Case=Nom\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Derivation=Lainen\|Number=Sing\|POS=PROPN`, `Case=Ela\|Degree=Cmp\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Abl\|Number=Plur\|POS=PROPN`, `Case=Ess\|Derivation=Ja\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Abbr=Yes\|Case=Tra\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Derivation=Lainen\|Number=Sing\|POS=ADJ`, `Case=Tra\|Degree=Pos\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Derivation=Lainen,Vs\|Number=Sing\|POS=NOUN`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Typo=Yes`, `Case=Nom\|Degree=Sup\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Sup\|Number=Sing\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Degree=Cmp\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Ela\|Clitic=Kin\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADJ`, `Case=Ine\|Degree=Cmp\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Par\|Derivation=Tar\|Number=Sing\|POS=NOUN`, `Case=Ela\|Degree=Sup\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Clitic=Kin\|Degree=Pos\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Ess\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Case=Ela\|Derivation=U\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=All\|Clitic=Kin\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ela\|Derivation=Lainen,Vs\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Derivation=Ton\|Number=Sing\|POS=VERB\|PartForm=Neg\|VerbForm=Part\|Voice=Act`, `POS=CCONJ\|Typo=Yes`, `Case=All\|Number=Sing\|POS=PRON`, `Case=Ess\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN`, `Case=Ine\|Derivation=Llinen,Vs\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Ine\|POS=SYM`, `Abbr=Yes\|Case=Ess\|Number=Sing\|POS=NOUN`, `Case=Par\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Abbr=Yes\|Case=Nom\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ade\|Clitic=Kin\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Par\|Degree=Cmp\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Tra\|Derivation=Ja\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Abl\|Derivation=Minen\|Number=Sing\|POS=NOUN`, `Clitic=Ko\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Nom\|Derivation=Inen,Vs\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ade\|Clitic=Kaan\|Derivation=Ja\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=PROPN`, `Case=Par\|Derivation=Inen\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Neg\|VerbForm=Part\|Voice=Act`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|Person[psor]=3\|VerbForm=Part\|Voice=Act`, `Case=Ade\|Degree=Sup\|Derivation=Inen\|Number=Plur\|POS=ADJ\|Person[psor]=3`, `Case=Ade\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=All\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Par\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Clitic=Kin\|Number=Plur\|POS=PROPN`, `Case=Par\|Derivation=Inen\|Number=Plur\|POS=ADJ`, `Case=Ine\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Tra\|Derivation=Ja\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Abbr=Yes\|Case=Tra\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|Case=Gen\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ade\|InfForm=3\|Number=Sing\|POS=VERB\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Case=Par\|Degree=Sup\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Par\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=All\|NumType=Ord\|POS=ADJ`, `Case=All\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=Par\|NumType=Card\|Number=Sing\|POS=ADJ`, `Case=Ela\|Derivation=Vs\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=PRON\|Reflex=Yes`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=AUX\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|Case=Ine\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Number=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Case=Ill\|Number=Plur\|POS=NOUN\|Person[psor]=3\|Typo=Yes`, `Case=Tra\|Derivation=Vs\|Number=Plur\|POS=NOUN`, `Case=Ess\|Derivation=Inen,Vs\|Number=Plur\|POS=NOUN`, `Case=Gen\|Clitic=Kin\|Number=Plur\|POS=PRON`, `Case=Ill\|Clitic=Kin\|Number=Plur\|POS=PRON`, `Case=Ine\|Number=Plur\|POS=PRON\|PronType=Ind\|Style=Coll`, `Case=Ill\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Case=Ade\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Par\|Derivation=Llinen\|Number=Plur\|POS=ADJ`, `Case=Ess\|Derivation=Lainen\|Number=Sing\|POS=NOUN`, `Case=Tra\|Degree=Sup\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Ela\|Number=Sing\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=All\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ess\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Tra\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Abbr=Yes\|Case=Gen\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Degree=Sup\|Derivation=Lainen\|Number=Plur\|POS=ADJ`, `Case=Ess\|Derivation=Inen\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Tra\|Degree=Sup\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Derivation=Llinen,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Degree=Pos\|Derivation=Lainen\|POS=ADJ`, `Abbr=Yes\|Case=Par\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Abl\|Degree=Pos\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|Case=Par\|Number=Sing\|POS=PROPN`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Agt\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Par\|Derivation=Tar\|Number=Plur\|POS=NOUN`, `Case=Nom\|Derivation=Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ela\|Degree=Sup\|Derivation=Ton\|Number=Plur\|POS=ADJ`, `InfForm=1\|Number=Sing\|POS=VERB\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Case=Ade\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=All\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Abl\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=All\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Ine\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=All\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Agt\|VerbForm=Part\|Voice=Act`, `Case=All\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Gen\|Derivation=Llinen\|Number=Sing\|POS=ADJ`, `Case=Gen\|Derivation=Ton,Vs\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ill\|Clitic=Kin\|Degree=Pos\|Derivation=Inen\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|Case=Nom\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Derivation=Ja\|Number=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ine\|Clitic=Kin\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=PROPN\|Person[psor]=3`, `Case=Abl\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Par\|POS=SYM`, `Case=Ine\|Derivation=Ton,Vs\|Number=Sing\|POS=NOUN`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=VERB\|PartForm=Past\|VerbForm=Part\|Voice=Act`, `Case=Ine\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Mood=Pot\|POS=VERB\|Typo=Yes\|VerbForm=Fin\|Voice=Pass`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Derivation=Minen\|Number=Sing\|POS=NOUN\|Person[psor]=3\|Style=Coll`, `Case=Par\|Number=Plur\|POS=NOUN\|Person[psor]=3\|Style=Coll`, `Case=Nom\|Clitic=Kin\|Number=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Tra\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Par\|Degree=Pos\|Number=Sing\|POS=VERB\|PartForm=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Tra\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Ade\|Number=Sing\|POS=NUM`, `Case=Par\|Derivation=Ton\|Number=Plur\|POS=NOUN`, `Case=Ine\|Degree=Pos\|Derivation=Lainen\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Ine\|Derivation=Ton,Vs\|Number=Plur\|POS=NOUN`, `Case=Gen\|Clitic=Kin\|Degree=Pos\|Number=Plur\|POS=PRON\|PronType=Ind` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `compound`, `compound:nn`, `compound:prt`, `conj`, `cop`, `cop:own`, `csubj`, `csubj:cop`, `dep`, `det`, `discourse`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `goeswith`, `mark`, `nmod`, `nmod:gobj`, `nmod:gsubj`, `nmod:poss`, `nsubj`, `nsubj:cop`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp`, `xcomp:ds` |
| **`experimental_edit_tree_lemmatizer`** | `3`, `4`, `7`, `10`, `13`, `15`, `19`, `21`, `23`, `25`, `29`, `35`, `40`, `41`, `45`, `48`, `50`, `52`, `55`, `57`, `59`, `61`, `64`, `67`, `71`, `73`, `75`, `77`, `80`, `85`, `86`, `90`, `92`, `94`, `96`, `99`, `101`, `103`, `104`, `106`, `109`, `111`, `112`, `114`, `117`, `120`, `123`, `127`, `130`, `134`, `136`, `138`, `141`, `145`, `147`, `149`, `151`, `153`, `157`, `158`, `160`, `161`, `163`, `166`, `168`, `170`, `173`, `175`, `177`, `179`, `181`, `184`, `187`, `191`, `194`, `198`, `199`, `201`, `202`, `205`, `207`, `210`, `212`, `214`, `217`, `218`, `222`, `224`, `226`, `228`, `230`, `232`, `234`, `236`, `239`, `241`, `243`, `246`, `249`, `251`, `253`, `254`, `256`, `258`, `260`, `261`, `264`, `267`, `269`, `271`, `273`, `274`, `278`, `281`, `282`, `284`, `286`, `289`, `291`, `292`, `294`, `299`, `301`, `304`, `306`, `308`, `310`, `313`, `316`, `317`, `320`, `322`, `327`, `329`, `334`, `336`, `338`, `340`, `344`, `345`, `348`, `350`, `352`, `354`, `357`, `359`, `362`, `363`, `365`, `366`, `367`, `368`, `369`, `370`, `372`, `375`, `377`, `380`, `382`, `385`, `387`, `389`, `390`, `392`, `395`, `397`, `400`, `403`, `406`, `408`, `411`, `413`, `415`, `417`, `419`, `421`, `423`, `425`, `428`, `431`, `433`, `436`, `438`, `440`, `442`, `443`, `446`, `448`, `451`, `453`, `455`, `457`, `459`, `461`, `463`, `466`, `469`, `471`, `473`, `476`, `477`, `481`, `482`, `484`, `488`, `490`, `491`, `495`, `498`, `501`, `503`, `506`, `509`, `513`, `515`, `517`, `519`, `521`, `523`, `526`, `528`, `529`, `531`, `533`, `535`, `537`, `538`, `539`, `542`, `544`, `546`, `548`, `551`, `553`, `556`, `558`, `562`, `564`, `566`, `568`, `570`, `574`, `576`, `578`, `582`, `584`, `586`, `588`, `591`, `593`, `595`, `596`, `598`, `600`, `601`, `602`, `604`, `606`, `608`, `609`, `435`, `610`, `611`, `614`, `616`, `617`, `620`, `622`, `625`, `626`, `628`, `630`, `631`, `633`, `635`, `637`, `638`, `640`, `641`, `643`, `645`, `646`, `650`, `651`, `653`, `655`, `657`, `659`, `660`, `664`, `667`, `671`, `672`, `674`, `677`, `681`, `683`, `684`, `686`, `687`, `689`, `691`, `693`, `695`, `698`, `701`, `703`, `705`, `707`, `710`, `713`, `716`, `720`, `723`, `725`, `726`, `730`, `731`, `734`, `736`, `738`, `739`, `741`, `744`, `748`, `749`, `750`, `752`, `755`, `757`, `759`, `761`, `762`, `763`, `767`, `769`, `772`, `774`, `777`, `780`, `781`, `782`, `784`, `785`, `787`, `788`, `790`, `792`, `793`, `794`, `797`, `799`, `802`, `803`, `805`, `807`, `810`, `388`, `811`, `813`, `815`, `817`, `821`, `823`, `824`, `826`, `828`, `829`, `831`, `832`, `833`, `834`, `836`, `838`, `840`, `842`, `844`, `845`, `847`, `849`, `852`, `855`, `857`, `861`, `863`, `865`, `867`, `868`, `870`, `872`, `875`, `876`, `878`, `879`, `881`, `883`, `886`, `888`, `890`, `891`, `892`, `895`, `896`, `898`, `900`, `903`, `907`, `910`, `912`, `914`, `915`, `917`, `920`, `921`, `924`, `926`, `928`, `930`, `932`, `934`, `937`, `940`, `941`, `943`, `944`, `945`, `946`, `947`, `949`, `952`, `954`, `956`, `960`, `963`, `966`, `969`, `971`, `972`, `974`, `977`, `978`, `981`, `983`, `985`, `987`, `990`, `991`, `993`, `995`, `996`, `999`, `1002`, `1006`, `1008`, `1011`, `1013`, `1016`, `1018`, `1020`, `1022`, `1024`, `1026`, `1028`, `1030`, `1032`, `1034`, `1036`, `1038`, `1040`, `1043`, `1044`, `1046`, `1048`, `1051`, `1054`, `1056`, `1057`, `1060`, `1062`, `1064`, `1066`, `1067`, `1069`, `1071`, `1074`, `1077`, `1078`, `1081`, `1084`, `1086`, `1087`, `1089`, `1091`, `1093`, `1095`, `1096`, `1098`, `1100`, `1101`, `1103`, `1104`, `1106`, `1108`, `1111`, `1114`, `1116`, `1118`, `1119`, `1121`, `1123`, `1125`, `1127`, `1128`, `1133`, `1136`, `1139`, `1141`, `1143`, `1146`, `1149`, `1150`, `1151`, `1153`, `1156`, `1157`, `1159`, `1161`, `1163`, `1167`, `1169`, `1171`, `1172`, `1174`, `1176`, `1180`, `1181`, `1184`, `1186`, `1189`, `1190`, `1193`, `1195`, `1197`, `1199`, `1202`, `1204`, `1205`, `1206`, `1207`, `1209`, `1210`, `1212`, `1214`, `1218`, `1220`, `1222`, `1224`, `1225`, `1227`, `1229`, `1230`, `1232`, `1235`, `1236`, `1238`, `1239`, `1241`, `1245`, `1246`, `1248`, `1249`, `1251`, `1252`, `1253`, `1255`, `1256`, `1259`, `1260`, `1262`, `1263`, `1265`, `1268`, `1269`, `1271`, `1272`, `1275`, `1276`, `1277`, `1279`, `1280`, `1283`, `1285`, `1286`, `1289`, `1291`, `1294`, `1295`, `1298`, `1300`, `1302`, `1304`, `1306`, `1308`, `1311`, `1312`, `1313`, `1314`, `1316`, `1317`, `1318`, `1320`, `1322`, `1323`, `1325`, `1327`, `1330`, `1332`, `1334`, `1339`, `1341`, `1344`, `1345`, `1347`, `1349`, `1352`, `1355`, `1356`, `1360`, `1363`, `1365`, `1367`, `1368`, `1369`, `1372`, `1374`, `1376`, `1377`, `1379`, `1380`, `1382`, `1384`, `1386`, `1389`, `1391`, `1392`, `1393`, `1396`, `1399`, `1400`, `1401`, `1403`, `1405`, `1406`, `1408`, `1411`, `1414`, `1416`, `1417`, `1419`, `1420`, `1422`, `1423`, `1425`, `1428`, `1430`, `1433`, `1436`, `1437`, `1439`, `1442`, `1444`, `1446`, `1449`, `1451`, `1454`, `1456`, `1457`, `1459`, `1461`, `1462`, `1464`, `1465`, `1467`, `1469`, `1470`, `1472`, `1475`, `1477`, `1478`, `1480`, `1482`, `1483`, `1484`, `1486`, `1487`, `1489`, `1491`, `1492`, `1494`, `1497`, `1498`, `1499`, `1501`, `1503`, `1506`, `1507`, `1511`, `1513`, `1514`, `1517`, `1519`, `1521`, `1523`, `1526`, `1528`, `1531`, `1533`, `1535`, `1536`, `1538`, `1540`, `1542`, `1545`, `1547`, `1549`, `1550`, `1551`, `1552`, `1554`, `1555`, `1556`, `1558`, `1559`, `1560`, `1562`, `1563`, `1564`, `1566`, `1568`, `1570`, `1575`, `1577`, `1578`, `1579`, `1580`, `1582`, `1585`, `1586`, `1589`, `1590`, `1592`, `1594`, `1598`, `1600`, `1601`, `1603`, `1604`, `1605`, `1607`, `1609`, `1610`, `1613`, `1616`, `1618`, `1619`, `1621`, `1622`, `1624`, `1627`, `1629`, `1631`, `1633`, `1635`, `1638`, `1640`, `1643`, `1646`, `1647`, `1649`, `1651`, `1654`, `1655`, `1658`, `1662`, `1663`, `1666`, `1669`, `1671`, `1673`, `1676`, `1679`, `1682`, `1685`, `1686`, `1688`, `1690`, `1693`, `1695`, `1698`, `1700`, `1702`, `1704`, `1705`, `1707`, `1710`, `1713`, `1715`, `1717`, `1719`, `1721`, `1724`, `1725`, `1727`, `1729`, `1730`, `1731`, `1732`, `1734`, `1736`, `1737`, `1738`, `1741`, `1744`, `1746`, `1747`, `1749`, `1751`, `1752`, `1753`, `1754`, `1756`, `1758`, `1761`, `1762`, `1764`, `1765`, `1766`, `1767`, `1769`, `1772`, `1774`, `1777`, `1778`, `1779`, `1781`, `1782`, `1784`, `1787`, `1790`, `1792`, `1794`, `1798`, `1800`, `1803`, `1805`, `1807`, `1809`, `1810`, `1811`, `1813`, `1815`, `1816`, `1820`, `1823`, `1824`, `1827`, `1830`, `1832`, `1833`, `1834`, `1835`, `1836`, `1838`, `1841`, `1842`, `1843`, `1845`, `1847`, `1849`, `1853`, `1856`, `1858`, `1860`, `1861`, `1863`, `1864`, `1865`, `1866`, `1867`, `1869`, `1870`, `1874`, `1875`, `1876`, `1879`, `1881`, `1882`, `1883`, `1886`, `1887`, `1890`, `1891`, `1893`, `1896`, `1898`, `1901`, `1903`, `1906`, `1908`, `1910`, `1912`, `1914`, `1917`, `1919`, `1921`, `1923`, `1926`, `1927`, `1928`, `1930`, `1931`, `1933`, `1935`, `1937`, `1939`, `1941`, `1943`, `1944`, `1946`, `1948`, `1950`, `1952`, `1955`, `1956`, `1957`, `1958`, `1960`, `1962`, `1963`, `1965`, `1967`, `1969`, `1970`, `1972`, `1973`, `1974`, `1975`, `1976`, `1978`, `1981`, `1984`, `1986`, `1989`, `1992`, `1994`, `1995`, `1996`, `1998`, `2000`, `2003`, `2004`, `2006`, `2007`, `2008`, `2009`, `2011`, `2013`, `2016`, `2017`, `2019`, `2020`, `2022`, `2025`, `2028`, `2029`, `2031`, `2034`, `2035`, `2038`, `2041`, `2043`, `2045`, `2047`, `2049`, `2051`, `2052`, `2055`, `2057`, `2058`, `2060`, `2062`, `2063`, `2065`, `2067`, `2069`, `2071`, `2073`, `2074`, `2076`, `2078`, `2082`, `2084`, `2086`, `2088`, `2089`, `2090`, `2092`, `2093`, `2094`, `2096`, `2098`, `2100`, `2102`, `2104`, `2107`, `2109`, `2110`, `2111`, `2112`, `2114`, `2115`, `2116`, `2117`, `2119`, `2121`, `2124`, `2125`, `2126`, `2129`, `2130`, `2132`, `2135`, `2137`, `2140`, `2142`, `2144`, `2146`, `2147`, `2148`, `2150`, `2151`, `2152`, `2153`, `2156`, `2159`, `2161`, `2163`, `2164`, `2165`, `2167`, `2169`, `2170`, `2171`, `2172`, `2173`, `2176`, `2178`, `2180`, `2182`, `2183`, `2186`, `2188`, `2191`, `2193`, `2195`, `2197`, `2198`, `2199`, `2202`, `2204`, `2206`, `2208`, `2210`, `2211`, `2214`, `2218`, `2219`, `2222`, `2224`, `2226`, `2227`, `2228`, `2229`, `2232`, `2234`, `2237`, `2239`, `2240`, `2242`, `2243`, `2245`, `2246`, `2247`, `2248`, `2249`, `2252`, `2253`, `2256`, `2258`, `2261`, `2263`, `2265`, `2269`, `2271`, `2273`, `2274`, `2276`, `2277`, `2279`, `2282`, `2284`, `2287`, `2290`, `2292`, `2293`, `2294`, `2296`, `2297`, `2300`, `2301`, `2303`, `2305`, `2308`, `2310`, `2312`, `2313`, `2315`, `2316`, `2317`, `2319`, `2321`, `2322`, `2324`, `2325`, `2326`, `2330`, `2332`, `2334`, `2335`, `2338`, `2340`, `2341`, `2343`, `2345`, `2346`, `2348`, `2349`, `2350`, `2352`, `2354`, `2356`, `2358`, `2360`, `2362`, `2364`, `2368`, `2371`, `2376`, `2377`, `2379`, `2381`, `2382`, `2383`, `2384`, `2385`, `2387`, `2388`, `2389`, `2390`, `2392`, `2393`, `2394`, `2395`, `2396`, `2399`, `2401`, `2403`, `2405`, `2408`, `2409`, `2411`, `2414`, `2416`, `2418`, `2421`, `2423`, `2425`, `2428`, `2429`, `2430`, `2432`, `2435`, `2437`, `2439`, `2441`, `2445`, `2448`, `2449`, `2451`, `2452`, `2453`, `2454`, `2456`, `2459`, `2462`, `2463`, `2464`, `2466`, `2467`, `2469`, `2472`, `2475`, `2476`, `2478`, `2481`, `2483`, `2485`, `2488`, `2490`, `2493`, `2497`, `2499`, `2502`, `2504`, `2506`, `2509`, `2511`, `2513`, `2514`, `2516`, `2520`, `2523`, `2526`, `2527`, `2530`, `2531`, `2533`, `2534`, `2536`, `2537`, `2539`, `2540`, `2543`, `2546`, `2548`, `2551`, `2554`, `2555`, `2557`, `2559`, `2560`, `2562`, `2563`, `2566`, `2568`, `2570`, `2572`, `2575`, `2578`, `2580`, `2583`, `2586`, `2588`, `2590`, `2593`, `2596`, `2598`, `2601`, `2603`, `2605`, `2608`, `2611`, `2614`, `2615`, `2617`, `2618`, `2620`, `2623`, `2626`, `2629`, `2631`, `2633`, `2635`, `2637`, `2639`, `2640`, `2642`, `2644`, `2646`, `2648`, `2652`, `2655`, `2657`, `2660`, `2662`, `2663`, `2666`, `2668`, `2669`, `2672`, `2676`, `2679`, `2682`, `2685`, `2687`, `2689`, `2691`, `2693`, `2695`, `2697`, `2699`, `2702`, `2703`, `2705`, `2707`, `2709`, `2711`, `2713`, `2714`, `2720`, `2722`, `2724`, `2726`, `2728`, `2730`, `2732`, `2734`, `2736`, `2738`, `2740`, `2743`, `2746`, `2749`, `2753`, `2755`, `2757`, `2759`, `2760`, `2762`, `2765`, `2766`, `2767`, `2769`, `2771`, `2772`, `2775`, `2778`, `2781`, `2783`, `2786`, `2788`, `2792`, `2793`, `2796`, `2798`, `2799`, `2802`, `2805`, `2806`, `2809`, `2810`, `2813`, `2814`, `2817`, `2820`, `2822`, `2823`, `2824`, `2825`, `2827`, `2829`, `2831`, `2833`, `2835`, `2837`, `2839`, `2842`, `2844`, `2845`, `2846`, `2849`, `2851`, `2853`, `2855`, `2857`, `2860`, `2862`, `2864`, `2865`, `2867`, `2869`, `2871`, `2874`, `2875`, `2877`, `2879`, `2880`, `2881`, `2882`, `2883`, `2886`, `2888`, `2889`, `2890`, `2892`, `2893`, `2894`, `2896`, `2899`, `2900`, `2902`, `2903`, `2904`, `2905`, `2906`, `2907`, `2909`, `2912`, `2915`, `2917`, `2918`, `2920`, `2922`, `2924`, `2925`, `2926`, `2928`, `2930`, `2932`, `2936`, `2938`, 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`17689`, `17691`, `17692`, `17694`, `17697`, `17698`, `17700`, `17701`, `17702`, `17703`, `17706`, `17707`, `17709`, `17713`, `17714`, `17715`, `17717`, `17718`, `17719`, `17720`, `17722`, `17723`, `17725`, `17726`, `17727`, `17729`, `17730`, `17732`, `17733`, `17737`, `17738`, `17739`, `17740`, `17741`, `17743`, `17744`, `17745`, `17747`, `17749`, `17752`, `17753`, `17754`, `17756`, `17760`, `17761`, `17762`, `17763`, `17764`, `17766`, `17767`, `17769`, `17771`, `17773`, `17775`, `17776`, `17778`, `17779`, `17780`, `17781`, `17782`, `17784`, `17787`, `17790`, `17792`, `17794`, `17795`, `17796`, `17797`, `17798`, `17802`, `17804`, `17805`, `17807`, `17808`, `17809`, `17811`, `17813`, `17816`, `17817`, `17818`, `17819`, `17820`, `17821`, `17822`, `17823`, `17825`, `17826`, `17827`, `17828`, `17829`, `17830`, `17831`, `17832`, `17833`, `17834`, `17835`, `17836`, `17837`, `17838`, `17840`, `17841`, `17842`, `17843`, `17844`, `17846`, `17848`, `17849`, `17850`, `17852`, `17853`, `17854`, `17855`, `17856`, `17858`, `17860`, `17863`, `17864`, `17865`, `17869`, `17871`, `17873`, `17875`, `17876`, `17879`, `17881`, `17884`, `17887`, `17890`, `17891`, `17892`, `17894`, `17895`, `17896`, `17897`, `17898`, `17901`, `17902`, `17903`, `17904`, `17907`, `17908`, `17909`, `17910`, `17912`, `17914`, `17917`, `17918`, `17923`, `17925`, `17927`, `17929`, `17930`, `17932`, `17934`, `17935`, `17937`, `17939`, `17940`, `17941`, `17942`, `17943`, `17945`, `17946`, `17948`, `17949`, `17951`, `17952`, `17954`, `17955`, `17956`, `17957`, `17958`, `17959`, `17961`, `17962`, `17964`, `17965`, `17967`, `17968`, `17971`, `17972`, `17973`, `17975`, `17976`, `17977`, `17978`, `17980`, `17982`, `17984`, `17985`, `17988`, `17989`, `17991`, `17993`, `17995`, `17996`, `17998`, `18000`, `18002`, `18004`, `18005`, `18006`, `18011`, `18012`, `18014`, `18015`, `18016`, `18017`, `18018`, `18019`, `18023`, `18024`, `18026`, `18028`, `18029`, `18030`, `18031`, `18032`, `18033`, `18034`, `18035`, `18036`, `18037`, `18038`, `18039`, `18040`, `18041`, `18042`, `18043`, `18044`, `18045`, `18047`, `18048`, `18050`, `18053`, `18054`, `18055`, `18056`, `18057`, `18059`, `18061`, `18062`, `18064`, `18065`, `18067`, `18068`, `18070`, `18071`, `18073`, `18074`, `18076`, `18078`, `18079`, `18081`, `18082`, `18085`, `18086`, `18087`, `18088`, `18091`, `18092`, `18093`, `18095`, `18096`, `18098`, `18099`, `18102`, `18104`, `18106`, `18108`, `18109`, `18110`, `18112`, `18113`, `18114`, `18115`, `18116`, `18117`, `18118`, `18120`, `18122`, `18124`, `18127`, `18129`, `18130`, `18132`, `18133`, `18134`, `18138`, `18140`, `18142`, `18144`, `18145`, `18147`, `18148`, `18149`, `18150`, `18153`, `18155`, `18157`, `18158`, `18159`, `18160`, `18162`, `18164`, `18167`, `18168`, `18169`, `18170`, `18172`, `18173`, `18176`, `18177`, `18179`, `18181`, `18182`, `18184`, `18186`, `18188`, `18189`, `18192`, `18193`, `18194`, `18195`, `18196`, `18197`, `18198`, `18200`, `18201`, `18203`, `18204`, `18205`, `18207`, `18208`, `18211`, `18213`, `18214`, `18215`, `18216`, `18218`, `18219`, `18220`, `18222`, `18224`, `18226`, `18227`, `18228`, `18229`, `18232`, `18234`, `18236`, `18237`, `18238`, `18239`, `18241`, `18242`, `18243`, `18244`, `18245` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.79 |
| `TOKEN_P` | 99.79 |
| `TOKEN_R` | 99.80 |
| `TOKEN_ACC` | 99.97 |
| `SENTS_F` | 96.20 |
| `SENTS_P` | 96.95 |
| `SENTS_R` | 95.45 |
| `TAG_ACC` | 98.33 |
| `POS_ACC` | 97.91 |
| `MORPH_ACC` | 95.92 |
| `DEP_UAS` | 91.92 |
| `DEP_LAS` | 89.41 |
| `LEMMA_ACC` | 88.22 |
|
{"language": ["fi"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/fi_udv25_finnishtdt_trf
|
[
"spacy",
"token-classification",
"fi",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fi"
] |
TAGS
#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Finnish-TDT
### Label Scheme
View label scheme (12912 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (12912 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (12912 labels for 6 components)",
"### Accuracy"
] |
[
32,
18,
5
] |
[
"passage: TAGS\n#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (12912 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_French-Sequoia
| Feature | Description |
| --- | --- |
| **Name** | `fr_udv25_frenchsequoia_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `LGPL-LR` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (916 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADP_DET`, `ADP_PRON`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` |
| **`morphologizer`** | `POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `POS=ADP`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Ord\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=ADV`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|PronType=Rel`, `Number=Sing\|POS=DET\|Poss=Yes`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Definite=Def\|Number=Plur\|POS=ADP\|PronType=Art`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Number=Plur\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|PronType=Int`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Plur\|POS=DET\|Poss=Yes`, `POS=AUX\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `POS=PRON\|Person=3\|Reflex=Yes`, `Gender=Masc\|POS=NOUN`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PRON\|Person=3`, `Number=Plur\|POS=NOUN`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=AUX\|Tense=Pres\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=3`, `Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes`, `Gender=Masc\|POS=PRON`, `POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=PRON`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=PRON`, `POS=NUM`, `Gender=Fem\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=PRON`, `Number=Plur\|POS=PRON\|Person=3`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Number=Plur\|POS=PRON\|Person=2`, `NumType=Card\|POS=PRON`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `NumType=Card\|POS=NOUN`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3`, `Gender=Fem\|Number=Sing\|POS=DET`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=DET`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|POS=PRON`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=X`, `POS=SYM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `POS=DET`, `Gender=Masc\|Number=Plur\|POS=PRON`, `POS=PART`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Plur\|POS=DET`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|Reflex=Yes`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=1\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Masc\|NumType=Card\|POS=NUM` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advcl:cleft`, `advmod`, `amod`, `appos`, `aux:caus`, `aux:pass`, `aux:tense`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `expl:comp`, `expl:pass`, `expl:subj`, `fixed`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:caus`, `nsubj:pass`, `nummod`, `obj`, `obl:agent`, `obl:arg`, `obl:mod`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `3`, `4`, `6`, `8`, `10`, `12`, `14`, `16`, `20`, `22`, `24`, `26`, `30`, `32`, `34`, `36`, `39`, `40`, `42`, `44`, `45`, `48`, `50`, `52`, `54`, `56`, `58`, `61`, `63`, `66`, `70`, `72`, `74`, `77`, `79`, `81`, `82`, `84`, `86`, `88`, `89`, `91`, `95`, `97`, `99`, `102`, `103`, `106`, `110`, `111`, `113`, `114`, `115`, `118`, `119`, `123`, `125`, `126`, `128`, `130`, `132`, `133`, `134`, `136`, `138`, `139`, `140`, `142`, `143`, `144`, `146`, `148`, `150`, `152`, `155`, `157`, `160`, `161`, `163`, `165`, `167`, `171`, `173`, `174`, `176`, `177`, `179`, `181`, `183`, `185`, `187`, `189`, `191`, `192`, `195`, `197`, `198`, `200`, `202`, `203`, `205`, `208`, `210`, `211`, `212`, `214`, `217`, `218`, `221`, `225`, `227`, `229`, `230`, `232`, `234`, `236`, `238`, `240`, `242`, `243`, `245`, `247`, `248`, `251`, `253`, `255`, `257`, `258`, `260`, `261`, `264`, `267`, `268`, `269`, `272`, `273`, `276`, `277`, `278`, `279`, `284`, `287`, `288`, `291`, `293`, `295`, `298`, `299`, `301`, `304`, `306`, `307`, `309`, `310`, `313`, `315`, `318`, `319`, `322`, `324`, `325`, `327`, `329`, `330`, `332`, `333`, `336`, `339`, `341`, `342`, `344`, `346`, `347`, `350`, `351`, `353`, `356`, `358`, `359`, `361`, `363`, `365`, `367`, `369`, `373`, `376`, `378`, `379`, `380`, `382`, `384`, `386`, `389`, `390`, `391`, `394`, `396`, `398`, `399`, `401`, `404`, `406`, `409`, `412`, `414`, `418`, `421`, `423`, `424`, `426`, `428`, `429`, `430`, `434`, `436`, `438`, `440`, `441`, `443`, `446`, `447`, `448`, `451`, `453`, `456`, `457`, `458`, `460`, `462`, `463`, `465`, `468`, `470`, `472`, `474`, `480`, `482`, `483`, `485`, `486`, `490`, `493`, `494`, `497`, `499`, `500`, `501`, `503`, `506`, `509`, `511`, `512`, `514`, `516`, `518`, `522`, `523`, `526`, `530`, `532`, `534`, `537`, `539`, `540`, `541`, `543`, `545`, `546`, `548`, `550`, `551`, `552`, `554`, `556`, `557`, `558`, `561`, `563`, `565`, `567`, `570`, `571`, `573`, `574`, `575`, `576`, `578`, `579`, `581`, `582`, `583`, `584`, `586`, `587`, `588`, `589`, `590`, `592`, `595`, `600`, `603`, `604`, `606`, `608`, `611`, `612`, `614`, `615`, `616`, `618`, `619`, `620`, `621`, `622`, `623`, `624`, `625`, `626`, `627`, `628`, `629`, `630`, `631`, `632`, `633`, `634`, `635`, `636`, `638`, `640`, `644`, `646`, `647`, `648`, `650`, `652`, `654`, `657`, `659`, `660`, `661`, `662`, `663`, `664`, `665`, `666`, `668`, `672`, `674`, `675`, `677`, `678`, `679`, `680`, `681`, `682`, `683`, `684`, `685`, `686`, `687`, `688`, `689`, `690`, `691`, `692`, `693`, `694`, `695`, `696`, `697`, `698`, `699`, `700`, `701`, `702`, `704`, `705`, `706`, `707`, `708`, `709`, `710`, `711`, `712`, `713`, `714`, `715`, `716`, `717`, `718`, `719`, `720`, `721`, `722`, `723`, `724`, `725`, `726`, `727`, `728`, `729`, `730`, `731`, `732`, `733`, `734`, `735`, `736`, `737`, `738`, `739`, `740`, `741`, `743`, `744`, `747`, `748`, `749`, `750`, `751`, `752`, `753`, `754`, `755`, `756`, `758`, `760`, `762`, `763`, `766`, `767`, `768`, `770`, `772`, `773`, `774`, `775`, `776`, `777`, `778`, `779`, `781`, `783`, `784`, `786`, `787`, `789`, `790`, `791`, `794`, `795`, `796`, `797`, `798`, `799`, `800`, `801`, `802`, `803`, `807`, `809`, `812`, `813`, `815`, `817`, `819`, `821`, `825`, `828`, `829`, `832`, `833`, `834`, `837`, `838`, `839`, `841`, `842`, `844`, `846`, `849`, `851`, `853`, `854`, `855`, `858`, `861`, `862`, `866`, `868`, `869`, `871`, `872`, `874`, `876`, `879`, `880`, `882`, `885`, `887`, `891`, `893`, `895`, `898`, `899`, `902`, `903`, `905`, `906`, `908`, `910`, `911`, `912`, `914`, `917`, `920`, `923`, `925`, `927`, `929`, `932`, `933`, `934`, `936`, `938`, `939`, `943`, `944`, `945`, `946`, `947`, `950`, `952`, `954`, `956`, `958`, `959`, `961`, `963`, `965`, `967`, `969`, `971`, `973`, `976`, `978`, `979`, `980`, `981`, `984`, `986`, `987`, `990`, `993`, `994`, `996`, `998`, `999`, `1000`, `1001`, `1002`, `1004`, `1006`, `1007`, `1009`, `1010`, `1012`, `1014`, `1016`, `1018`, `1021`, `1023`, `1026`, `1027`, `1029`, `1031`, `1033`, `1034`, `1036`, `1037`, `1039`, `1041`, `1043`, `1044`, `1045`, `1046`, `1049`, `1051`, `1053`, `1054`, `1055`, `1056`, `1057`, `1058`, `1059`, `1061`, `1063`, `1065`, `1067`, `1068`, `1070`, `1072`, `1073`, `1075`, `1077`, `1078`, `1080`, `1081`, `1082`, `1084`, `1085`, `1087`, `1088`, `1089`, `1090`, `1091`, `1092`, `1094`, `1095`, `1097`, `1098`, `1100`, `1103`, `1106`, `1108`, `1110`, `1111`, `1113`, `1116`, `1117`, `1119`, `1121`, `1124`, `1127`, `1129`, `1131`, `1132`, `1133`, `1135`, `1136`, `1138`, `1139`, `1141`, `1142`, `1145`, `1148`, `1153`, `1154`, `1156`, `1157`, `1159`, `1161` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.70 |
| `TOKEN_P` | 99.69 |
| `TOKEN_R` | 99.71 |
| `TOKEN_ACC` | 99.96 |
| `SENTS_F` | 94.42 |
| `SENTS_P` | 94.42 |
| `SENTS_R` | 94.42 |
| `TAG_ACC` | 98.65 |
| `POS_ACC` | 98.56 |
| `MORPH_ACC` | 97.55 |
| `DEP_UAS` | 94.68 |
| `DEP_LAS` | 92.60 |
| `LEMMA_ACC` | 97.41 |
|
{"language": ["fr"], "license": "lgpl-lr", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/fr_udv25_frenchsequoia_trf
|
[
"spacy",
"token-classification",
"fr",
"license:lgpl-lr",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_French-Sequoia
### Label Scheme
View label scheme (916 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (916 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (916 labels for 6 components)",
"### Accuracy"
] |
[
30,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (916 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Irish-IDT
| Feature | Description |
| --- | --- |
| **Name** | `ga_udv25_irishidt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1662 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `!`, `.`, `...`, `?`, `Abr`, `Ad`, `Adj`, `Art`, `CM`, `CU`, `Cmp`, `Cmpd`, `CmpdNoGen`, `Comp`, `Cond`, `Coord`, `Cop`, `Cp`, `Deg`, `Dem`, `Det`, `Dir`, `Foreign`, `FutInd`, `Gn`, `Idf`, `Imper`, `Inf`, `Item`, `Itj`, `Its`, `Loc`, `Nm`, `Noun`, `Num`, `PastImp`, `PastInd`, `Pat`, `Pers`, `Poss`, `Prep`, `PresImp`, `PresInd`, `PresSubj`, `Pron`, `Punct`, `Q`, `Ref`, `Rel`, `Simp`, `Subord`, `Subst`, `Sup`, `Temp`, `Unknown`, `VD`, `VI`, `VT`, `VTI`, `Vb`, `Voc`, `Web`, `cionn` |
| **`morphologizer`** | `POS=ADP`, `Case=NomAcc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Cop`, `Number=Sing\|POS=PRON\|Person=3`, `Mood=Ind\|POS=VERB\|Tense=Fut`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Case=NomAcc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `POS=PART\|PartType=Inf`, `POS=NOUN\|VerbForm=Inf`, `Number=Sing\|POS=ADP\|PronType=Art`, `POS=ADV`, `POS=PUNCT`, `POS=PART\|PartType=Vb\|Polarity=Neg`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Fut`, `Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Case=NomAcc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=NomAcc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Int\|POS=AUX\|Polarity=Neg\|Tense=Pres\|VerbForm=Cop`, `Degree=Pos\|POS=ADJ`, `POS=PART\|PartType=Vb\|PronType=Rel`, `Form=Len\|Mood=Cnd\|POS=VERB`, `Case=NomAcc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=ADP\|Person=1`, `Case=NomAcc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Emp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Rel\|Tense=Pres`, `Case=NomAcc\|Form=Ecl\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=NomAcc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=DET\|Person=1\|Poss=Yes`, `POS=PART\|PartType=Cmpl`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Past`, `POS=PRON\|PronType=Dem`, `POS=PART\|PartType=Vb`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Past`, `Number=Sing\|POS=PRON\|Person=2`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=PART\|PartType=Comp`, `Degree=Cmp,Sup\|POS=ADJ`, `Case=NomAcc\|Form=Len\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Pres`, `NumType=Card\|POS=NUM`, `POS=ADJ\|VerbForm=Part`, `Number=Plur\|POS=ADP\|Person=1`, `Form=Len\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `POS=PRON\|PronType=Int`, `Mood=Ind\|POS=VERB\|PronType=Rel\|Tense=Pres`, `Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres`, `Dialect=Munster\|POS=X`, `POS=ADP\|PrepForm=Cmpd`, `Case=NomAcc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=NomAcc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Form=Ecl\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `POS=NOUN\|VerbForm=Vnoun`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Masc\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Form=Len\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Number=Plur\|POS=DET\|Person=3\|Poss=Yes`, `Case=NomAcc\|Form=Ecl\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|Voice=Auto`, `Number=Plur\|POS=PRON\|Person=3`, `Case=Gen\|Definite=Def\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Form=Len\|POS=NOUN\|VerbForm=Inf`, `POS=PART\|PartType=Ad`, `POS=PART\|PartType=Pat`, `POS=NUM`, `Mood=Ind\|POS=VERB\|Tense=Pres`, `Case=NomAcc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Form=Len\|POS=VERB`, `POS=PRON\|Reflex=Yes`, `POS=VERB`, `Case=NomAcc\|Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=NomAcc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=SCONJ\|VerbForm=Cop`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=NomAcc\|Form=HPref\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=DET\|PronType=Dem`, `Form=Len\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Case=NomAcc\|Form=HPref\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Masc\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Fem\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Case=Dat\|Form=Ecl\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=ADP\|Person=3`, `POS=PART\|PartType=Comp`, `POS=PART`, `Case=NomAcc\|Form=Ecl\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=NomAcc\|Form=Len\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=DET\|PronType=Ind`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Fut\|Voice=Auto`, `Case=Gen\|Gender=Fem\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Pres\|Voice=Auto`, `POS=X`, `POS=PART\|PronType=Rel`, `Form=VF\|POS=AUX\|Tense=Pres\|VerbForm=Cop`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes`, `POS=AUX\|Polarity=Neg\|PronType=Rel\|Tense=Pres\|VerbForm=Cop`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Pres`, `Case=Gen\|Form=Ecl\|Gender=Fem\|NounType=Strong\|Number=Plur\|POS=NOUN`, `POS=PART\|PartType=Vb\|Polarity=Neg\|PronType=Rel`, `Number=Sing\|POS=PRON\|PronType=Int`, `Abbr=Yes\|POS=X`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=AUX\|Tense=Past\|VerbForm=Cop`, `Number=Sing\|POS=PRON\|Person=1`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Pres\|Voice=Auto`, `Case=NomAcc\|Form=HPref\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Fut`, `Case=Gen\|POS=NOUN\|VerbForm=Inf`, `Form=HPref\|POS=DET\|PronType=Ind`, `Case=NomAcc\|Form=Len\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=NomAcc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Case=Gen\|NounType=Strong\|Number=Plur\|POS=ADJ`, `Foreign=Yes\|POS=X`, `Mood=Ind\|POS=VERB\|Tense=Fut\|Voice=Auto`, `Number=Plur\|POS=ADP\|Person=3\|PronType=Emp`, `Mood=Ind\|POS=VERB\|Tense=Past`, `POS=PART\|PartType=Cmpl\|Polarity=Neg\|Tense=Past`, `Number=Plur\|POS=ADP\|Person=3\|Poss=Yes`, `Form=Ecl\|POS=NOUN\|VerbForm=Inf`, `Case=Gen\|Form=Len\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Len\|NumType=Card\|POS=NUM`, `Abbr=Yes\|POS=NUM`, `Case=NomAcc\|NounType=NotSlender\|Number=Plur\|POS=ADJ`, `Case=NomAcc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=PROPN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|Voice=Auto`, `POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Cop`, `Degree=Pos\|Form=Len\|POS=ADJ`, `Form=Len\|NumType=Ord\|POS=NUM`, `Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2`, `Form=Len\|Number=Plur\|POS=ADP\|Person=1`, `Case=NomAcc\|Form=Len\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=NomAcc\|Form=Len\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Degree=Pos\|Form=Ecl\|POS=ADJ`, `Mood=Imp\|POS=PART\|PartType=Vb`, `Mood=Cnd\|POS=VERB`, `Number=Sing\|POS=ADP\|Person=1\|Poss=Yes`, `Form=Ecl\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1`, `Form=Len\|Mood=Imp\|POS=VERB\|Tense=Past\|Voice=Auto`, `Case=Gen\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=NOUN`, `POS=PART\|PartType=Num`, `Form=HPref\|NumType=Card\|POS=NUM`, `Form=Len\|Mood=Sub\|POS=VERB\|Polarity=Neg\|Tense=Pres`, `Case=Gen\|Form=Len\|Gender=Masc\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADP\|Person=3`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Emp`, `POS=PART\|PartType=Vb\|Tense=Past`, `Case=NomAcc\|Form=Ecl\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Def\|Dialect=Ulster\|POS=X`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Fut`, `POS=PART\|PartType=Vb\|Polarity=Neg\|Tense=Past`, `POS=PART\|PartType=Cmpl\|Polarity=Neg`, `Case=NomAcc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=ADP\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Form=Len\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Form=Len\|Mood=Imp\|POS=VERB\|Voice=Auto`, `Definite=Def\|POS=DET`, `POS=AUX\|PronType=Rel\|Tense=Pres\|VerbForm=Cop`, `Case=NomAcc\|NounType=Slender\|Number=Plur\|POS=ADJ`, `POS=AUX\|Polarity=Neg\|PronType=Rel\|Tense=Past\|VerbForm=Cop`, `Form=Ecl\|Mood=Cnd\|POS=VERB`, `Case=Gen\|Form=Ecl\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=AUX\|Polarity=Neg\|Tense=Pres\|VerbForm=Cop`, `Form=Len\|Mood=Imp\|POS=VERB\|Tense=Past`, `Case=Gen\|Form=Ecl\|Gender=Masc\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADP\|Person=2`, `Degree=Pos\|Form=HPref\|POS=ADJ`, `Dialect=Munster\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=ADP\|Person=3\|Poss=Yes`, `Case=NomAcc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Number=Plur\|POS=ADP\|Person=1\|PronType=Emp`, `POS=PART\|PartType=Vb\|Polarity=Neg\|PronType=Rel\|Tense=Past`, `POS=PRON\|PronType=Ind`, `Number=Plur\|POS=ADP\|Person=1\|Poss=Yes`, `Gender=Fem\|Number=Sing\|POS=ADP\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|NounType=Weak\|Number=Plur\|POS=ADJ`, `Form=Emp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `Case=NomAcc\|Form=Len\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=VF\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Cop`, `Case=NomAcc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Fem\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3`, `Form=VF\|POS=AUX\|Polarity=Neg\|PronType=Rel\|Tense=Past\|VerbForm=Cop`, `Case=NomAcc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Form=Ecl\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=NomAcc\|Form=Emp\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Form=Ecl\|Gender=Masc\|Number=Plur\|POS=PROPN`, `POS=PROPN`, `Mood=Imp\|POS=PART\|PartType=Vb\|Polarity=Neg`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes`, `Form=Ecl\|NumType=Card\|POS=NUM`, `Case=Gen\|Form=Len\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Dialect=Munster\|Mood=Ind\|POS=X\|Tense=Past\|Voice=Auto`, `Number=Sing\|POS=DET\|Person=2\|Poss=Yes`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past\|Voice=Auto`, `Definite=Def\|NumType=Card\|POS=NUM`, `Form=Len\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1`, `Case=NomAcc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres`, `Form=Len\|Mood=Cnd\|POS=VERB\|Voice=Auto`, `Mood=Imp\|POS=VERB\|Tense=Past`, `Case=Gen\|Form=Ecl\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=ADP\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Past\|Voice=Auto`, `Definite=Def\|Form=Ecl\|POS=DET`, `Number=Plur\|POS=ADJ`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Fut\|Voice=Auto`, `Form=VF\|POS=AUX\|Tense=Past\|VerbForm=Cop`, `Form=Len\|Number=Sing\|POS=NOUN`, `POS=AUX`, `Gender=Masc\|POS=PRON\|Person=3`, `Case=NomAcc\|Form=Len\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Form=Len\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Int\|POS=PART\|PartType=Vb\|Polarity=Neg`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres`, `Form=Ecl\|Mood=Imp\|POS=VERB\|Tense=Past`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Emp`, `Case=NomAcc\|Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=X`, `Dialect=Munster\|Form=Len\|Mood=Ind\|Number=Sing\|POS=X\|Person=1\|Tense=Past`, `POS=PART\|PartType=Vb\|PronType=Rel\|Tense=Past`, `Form=Len\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2`, `POS=PART\|PartType=Voc`, `Form=HPref\|POS=NOUN\|VerbForm=Inf`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Degree=Cmp,Sup\|Form=Len\|POS=ADJ`, `POS=NOUN`, `Form=Ecl\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Case=NomAcc\|Form=Ecl\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Number=Plur\|POS=ADP\|Person=2`, `POS=SCONJ\|Tense=Past\|VerbForm=Cop`, `NumType=Ord\|POS=NUM`, `Mood=Int\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Cop`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Dialect=Ulster\|POS=X\|VerbForm=Cop`, `Mood=Int\|Number=Sing\|POS=AUX\|PronType=Art\|VerbForm=Cop`, `Case=NomAcc\|Definite=Def\|Gender=Fem\|POS=NOUN`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Form=Ecl\|POS=NOUN\|VerbForm=Vnoun`, `Case=NomAcc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Ecl\|Mood=Sub\|POS=VERB\|Tense=Pres`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=ADJ\|PartType=Voc`, `Form=Len\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Number=Sing\|POS=DET\|PronType=Int`, `Form=Len\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3`, `Dialect=Munster\|Form=Len\|Mood=Ind\|POS=VERB\|Tense=Past`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Case=NomAcc\|Gender=Masc\|POS=PROPN`, `Case=Gen\|Form=Len\|Gender=Masc\|POS=PROPN`, `Form=Ecl\|POS=VERB`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `Form=Ecl\|Number=Sing\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Fut\|Voice=Auto`, `POS=AUX\|PronType=Dem\|VerbForm=Cop`, `POS=AUX\|PronType=Rel\|Tense=Past\|VerbForm=Cop`, `Case=NomAcc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pres`, `Form=Ecl\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Form=Len\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past`, `Abbr=Yes\|POS=SYM`, `Case=Gen\|Form=Len\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|Voice=Auto`, `POS=PART\|PartType=Cop\|PronType=Rel`, `Form=VF\|POS=AUX\|PronType=Rel\|Tense=Past\|VerbForm=Cop`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Form=Len\|Number=Sing\|POS=PRON\|Person=2`, `Case=Voc\|Form=Len\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=ADJ\|PartType=Voc`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Voc\|Form=Len\|Gender=Fem\|POS=PROPN`, `Case=Gen\|Form=HPref\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Dialect=Ulster\|Gender=Masc\|Number=Sing\|POS=X\|Person=3`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Fut`, `Form=Len\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut`, `Case=NomAcc\|Form=HPref\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=ADV\|PronType=Int`, `Form=Ecl\|Mood=Cnd\|POS=VERB\|Voice=Auto`, `POS=ADP\|PronType=Art`, `Mood=Int\|POS=AUX\|Tense=Pres\|VerbForm=Cop`, `POS=PART\|PartType=Deg`, `Number=Sing\|POS=ADP\|Person=1\|PronType=Emp`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Emp`, `Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Cop`, `Foreign=Yes\|POS=ADJ`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=VERB`, `Foreign=Yes\|POS=ADP`, `Abbr=Yes\|POS=PROPN`, `Form=Len\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2`, `Case=Voc\|Form=Len\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past\|Voice=Auto`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past`, `Case=NomAcc\|Form=Ecl\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Len\|POS=ADV`, `Case=Voc\|Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=2`, `POS=DET`, `Number=Sing\|POS=ADP\|Person=3`, `Mood=Cnd\|POS=VERB\|Voice=Auto`, `Form=Len\|Number=Sing\|POS=ADP\|Person=1`, `Dialect=Munster\|Mood=Imp\|Number=Sing\|POS=X\|Person=2\|Polarity=Neg`, `Dialect=Munster\|POS=X\|PronType=Dem`, `Form=Len\|POS=VERB\|Polarity=Neg`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past`, `Case=Gen\|Gender=Masc\|POS=PROPN`, `Form=Ecl\|NumType=Ord\|POS=NUM`, `Mood=Ind\|POS=VERB\|PronType=Rel\|Tense=Fut`, `Form=Len\|Number=Plur\|POS=ADP\|Person=3`, `Case=NomAcc\|Form=HPref\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Fut\|Voice=Auto`, `Form=Len\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Form=Len\|Gender=Fem\|POS=PROPN`, `Form=Ecl\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Form=Len\|POS=NOUN\|VerbForm=Inf`, `Degree=Pos\|POS=NOUN`, `POS=AUX\|PartType=Comp\|Tense=Past\|VerbForm=Cop`, `Number=Plur\|POS=DET\|Person=1\|Poss=Yes`, `Case=Dat\|Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Form=HPref\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=ADP\|Person=3\|Poss=Yes`, `POS=NOUN\|Reflex=Yes`, `Dialect=Ulster\|POS=X\|PartType=Vb\|Polarity=Neg`, `Form=Emp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|PronType=Emp`, `Form=Ecl\|POS=PART\|PartType=Vb\|PronType=Rel`, `Form=Ecl\|Mood=Cnd\|POS=VERB\|Polarity=Neg`, `Case=Gen\|Form=Ecl\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Form=Len\|Mood=Cnd\|POS=VERB\|Polarity=Neg`, `Form=Len\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Len\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Len\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Form=HPref\|Gender=Fem\|POS=PROPN`, `Form=Len\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Form=Len\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=HPref\|Gender=Fem\|Number=Sing\|POS=NOUN`, `NounType=Slender\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=PRON`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=NOUN\|PartType=Comp`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Ecl\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PART\|PartType=Cmpl\|Tense=Past`, `Form=Ecl\|Mood=Int\|POS=VERB\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Art`, `NounType=NotSlender\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|POS=AUX\|VerbForm=Cop`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Form=Len\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Gender=Masc\|Number=Sing\|POS=INTJ`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Fem\|Number=Sing\|POS=SCONJ`, `POS=PART\|Tense=Pres\|VerbForm=Cop`, `Case=Gen\|Definite=Def\|Gender=Fem\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=ADJ`, `Form=Ecl\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=DET\|PronType=Art`, `Form=Ecl,Emp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Form=Ecl\|Mood=Cnd,Int\|POS=VERB`, `Definite=Def\|Dialect=Munster\|Gender=Fem\|Number=Sing\|POS=X`, `POS=AUX\|PronType=Dem`, `POS=AUX\|PartType=Cmpl\|Tense=Pres\|VerbForm=Cop`, `Form=Len\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `POS=PART\|PartType=Inf\|PronType=Rel`, `Form=Ecl\|Number=Plur\|POS=NOUN`, `Form=Len\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `POS=SCONJ\|Tense=Past`, `Form=HPref\|Gender=Masc\|Number=Sing\|POS=ADP\|Person=3`, `Form=Ecl\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Form=HPref\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `POS=INTJ`, `Form=HPref\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Form=Len\|Gender=Fem\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Mood=Sub\|POS=VERB\|Tense=Pres\|Voice=Auto`, `Number=Sing\|POS=VERB\|Person=1`, `Gender=Masc\|POS=PROPN`, `POS=ADP\|PronType=Rel`, `Mood=Ind\|POS=NOUN\|PronType=Rel\|Tense=Pres`, `Form=Ecl\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Form=Ecl\|Mood=Cnd,Int\|POS=VERB\|Voice=Auto`, `Form=Len\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|POS=PROPN`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2`, `Form=HPref\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Dialect=Ulster\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN` |
| **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `case`, `case:voc`, `cc`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj:cleft`, `csubj:cop`, `dep`, `det`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `list`, `mark`, `mark:prt`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:prep`, `obl:tmod`, `parataxis`, `punct`, `vocative`, `xcomp`, `xcomp:pred` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `7`, `10`, `11`, `13`, `15`, `16`, `17`, `19`, `21`, `25`, `27`, `28`, `30`, `32`, `34`, `36`, `37`, `40`, `42`, `44`, `46`, `51`, `54`, `56`, `59`, `62`, `64`, `66`, `68`, `70`, `72`, `73`, `74`, `77`, `81`, `83`, `85`, `88`, `89`, `91`, `93`, `96`, `99`, `100`, `102`, `104`, `108`, `114`, `116`, `119`, `120`, `121`, `123`, `126`, `127`, `128`, `131`, `133`, `135`, `137`, `138`, `139`, `142`, `144`, `145`, `147`, `149`, `151`, `153`, `157`, `159`, `161`, `164`, `165`, `169`, `171`, `173`, `176`, `181`, `183`, `185`, `186`, `188`, `189`, `191`, `193`, `194`, `195`, `197`, `199`, `201`, `202`, `205`, `207`, `209`, `210`, `213`, `216`, `217`, `220`, `221`, `223`, `225`, `227`, `228`, `230`, `232`, `233`, `236`, `238`, `240`, `241`, `242`, `244`, `246`, `247`, `249`, `251`, `252`, `254`, `256`, `257`, `259`, `264`, `267`, `268`, `271`, `273`, `275`, `276`, `278`, `279`, `280`, `282`, `283`, `285`, `286`, `289`, `291`, `293`, `295`, `296`, `299`, `301`, `302`, `303`, `304`, `305`, `306`, `308`, `310`, `311`, `312`, `315`, `318`, `319`, `320`, `321`, `323`, `325`, `327`, `328`, `332`, `334`, `336`, `339`, `341`, `343`, `346`, `348`, `350`, `353`, `355`, `358`, `359`, `361`, `363`, `365`, `366`, `367`, `368`, `370`, `371`, `373`, `376`, `378`, `380`, `381`, `384`, `385`, `386`, `389`, `390`, `392`, `396`, `398`, `400`, `401`, `402`, `405`, `407`, `409`, `410`, `411`, `413`, `415`, `416`, `419`, `421`, `422`, `423`, `426`, `427`, `428`, `429`, `430`, `431`, `432`, `433`, `434`, `437`, `438`, `439`, `440`, `441`, `442`, `443`, `446`, `449`, `453`, `455`, `457`, `458`, `459`, `461`, `462`, `464`, `466`, `469`, `471`, `473`, `475`, `478`, `479`, `480`, `482`, `483`, `485`, `487`, `490`, `491`, `492`, `495`, `496`, `497`, `500`, `502`, `505`, `507`, `509`, `512`, `513`, `515`, `516`, `518`, `520`, `522`, `523`, `525`, `527`, `530`, `531`, `532`, `534`, `536`, `537`, `538`, `540`, `541`, `542`, `545`, `546`, `548`, `549`, `551`, `554`, `557`, `560`, `562`, `564`, `565`, `567`, `570`, `571`, `573`, `574`, `578`, `579`, `581`, `585`, `587`, `590`, `591`, `592`, `596`, `597`, `598`, `599`, `600`, `602`, `604`, `605`, `606`, `608`, `609`, `611`, `613`, `614`, `616`, `618`, `619`, `621`, `623`, `624`, `627`, `628`, `629`, `630`, `632`, `633`, `635`, `636`, `637`, `640`, `642`, `644`, `646`, `648`, `649`, `651`, `653`, `655`, `656`, `657`, `659`, `660`, `663`, `665`, `667`, `669`, `673`, `675`, `676`, `678`, `682`, `683`, `686`, `688`, `690`, `691`, `693`, `696`, `698`, `702`, `705`, `708`, `710`, `711`, `712`, `714`, `715`, `717`, `719`, `721`, `722`, `724`, `725`, `727`, `729`, `734`, `736`, `738`, `739`, `742`, `743`, `744`, `746`, `750`, `751`, `753`, `755`, `756`, `758`, `759`, `760`, `761`, `762`, `764`, `766`, `767`, `769`, `770`, `771`, `772`, `773`, `774`, `777`, `778`, `780`, `781`, `783`, `784`, `785`, `787`, `789`, `790`, `793`, `794`, `796`, `798`, `800`, `802`, `803`, `805`, `808`, `809`, `810`, `811`, `813`, `815`, `816`, `817`, `820`, `822`, `827`, `828`, `830`, `833`, `836`, `837`, `838`, `841`, `842`, `843`, `845`, `847`, `849`, `850`, `852`, `24`, `854`, `856`, `859`, `860`, `861`, `862`, `863`, `864`, `866`, `868`, `869`, `870`, `873`, `874`, `877`, `878`, `879`, `881`, `884`, `886`, `888`, `889`, `890`, `893`, `894`, `897`, `898`, `900`, `902`, `905`, `908`, `909`, `910`, `911`, `912`, `913`, `915`, `916`, `917`, `919`, `921`, `924`, `926`, `927`, `928`, `929`, `930`, `932`, `935`, `937`, `941`, `943`, `945`, `946`, `948`, `950`, `951`, `953`, `954`, `955`, `958`, `960`, `963`, `965`, `966`, `967`, `968`, `969`, `971`, `974`, `976`, `978`, `979`, `981`, `982`, `983`, `984`, `985`, `986`, `988`, `990`, `992`, `994`, `997`, `998`, `999`, `1001`, `1003`, `1004`, `1006`, `1008`, `1010`, `1011`, `1012`, `1015`, `1017`, `1019`, `1020`, `1021`, `1022`, `1025`, `1028`, `1030`, `1032`, `1033`, `1035`, `1036`, `1039`, `1040`, `1041`, `1042`, `1044`, `1045`, `1046`, `1047`, `1048`, `1049`, `1051`, `1053`, `1055`, `1056`, `1057`, `1058`, `1061`, `1062`, `1064`, `1065`, `1068`, `1070`, `1071`, `1073`, `1074`, `1076`, `1078`, `1080`, `1082`, `1084`, `1086`, `1087`, `1088`, `1089`, `1090`, `1091`, `1092`, `1093`, `1095`, `1097`, `1100`, `1101`, `1103`, `1105`, `1106`, `1108`, `1110`, `1113`, `1114`, `1115`, `1117`, `1118`, `1120`, `1123`, `1127`, `1128`, `1129`, `1131`, `1135`, `1137`, `1138`, `1140`, `1141`, `1143`, `1144`, `1145`, `818`, `1146`, `1148`, `1149`, `1150`, `1152`, `1154`, `1157`, `1159`, `1160`, `1163`, `1166`, `1168`, `1170`, `1171`, `1173`, `1174`, `1176`, `1179`, `1180`, `1182`, `1183`, `1184`, `1186`, `1187`, `1188`, `1189`, `1191`, `1192`, `1195`, `1198`, `1199`, `1200`, `1201`, `1202`, `1205`, `1206`, `1208`, `1210`, `1212`, `1214`, `1215`, `1217`, `1218`, `1219`, `1220`, `1223`, `1227`, `1228`, `1230`, `1231`, `1233`, `1235`, `1236`, `1240`, `1242`, `1244`, `1245`, `1247`, `1248`, `1249`, `1251`, `1252`, `1253`, `1254`, `1255`, `1256`, `1259`, `1260`, `1263`, `1264`, `1267`, `1270`, `1272`, `1273`, `1275`, `1277`, `1279`, `1281`, `1282`, `1283`, `1285`, `1286`, `1288`, `1290`, `1292`, `1295`, `1297`, `1298`, `1299`, `1301`, `1302`, `1305`, `1306`, `1308`, `1309`, `1310`, `1311`, `1313`, `1315`, `1317`, `1318`, `1319`, `1321`, `1323`, `1325`, `1326`, `1327`, `1330`, `1333`, `1336`, `1338`, `1339`, `1340`, `1341`, `1343`, `0`, `1345`, `1347`, `1350`, `1352`, `1356`, `1359`, `1360`, `1361`, `1362`, `1365`, `1367`, `1368`, `1369`, `1371`, `1373`, `1375`, `1378`, `1379`, `1382`, `1384`, `1387`, `1390`, `1392`, `1395`, `1396`, `1397`, `1400`, `1403`, `1406`, `1407`, `1410`, `1411`, `1412`, `1414`, `1416`, `1418`, `1421`, `1422`, `1423`, `1424`, `1426`, `1429`, `1431`, `1433`, `1436`, `1437`, `1442`, `1443`, `1445`, `1446`, `1448`, `1449`, `1450`, `1451`, `1452`, `1453`, `1454`, `1457`, `1460`, `1462`, `1463`, `1466`, `1467`, `1470`, `1471`, `1473`, `1474`, `1477`, `1479`, `1480`, `1481`, `1484`, `1486`, `1489`, `1492`, `1495`, `1496`, `1497`, `1498`, `1501`, `1502`, `1505`, `1506`, `1508`, `1509`, `1510`, `1511`, `1513`, `1514`, `1516`, `1518`, `1521`, `1523`, `1527`, `1528`, `1531`, `1532`, `1534`, `1537`, `1540`, `1541`, `1544`, `1545`, `1547`, `1548`, `1549`, `1550`, `1551`, `1552`, `1553`, `1554`, `1555`, `1557`, `1558`, `1559`, `1560`, `1561`, `1563`, `1565`, `1566`, `1567`, `1569`, `1571`, `1573`, `1576`, `1578`, `1579`, `1580`, `1582`, `1583`, `1211`, `1585`, `1587`, `1588`, `1590`, `1593`, `1595`, `1596`, `1597`, `1598`, `1599`, `1602`, `1604`, `1606`, `1608`, `1610`, `1611`, `1612`, `1613`, `1615`, `1617`, `1618`, `1620`, `1622`, `1623`, `1624`, `1625`, `1626`, `1629`, `1630`, `1632`, `1633`, `1634`, `1637`, `1639`, `65`, `1641`, `1643`, `1644`, `1646`, `1648`, `1649`, `1650`, `1651`, `1652`, `1654`, `1655`, `1658`, `1660`, `1661`, `1662`, `1663`, `1665`, `1666`, `1668`, `1669`, `1671`, `1672`, `1675`, `1676`, `1680`, `1681`, `1682`, `1684`, `1687`, `1689`, `1690`, `1691`, `1692`, `1693`, `1695`, `1696`, `1698`, `1699`, `1700`, `1702`, `1703`, `1704`, `1706`, `1707`, `1708`, `1709`, `1712`, `1715`, `1716`, `1719`, `1722`, `1724`, `1725`, `1726`, `1727`, `1729`, `1730`, `1731`, `1733`, `1736`, `1738`, `1739`, `1742`, `1745`, `1746`, `1747`, `1749`, `1750`, `1752`, `1753`, `1754`, `1757`, `1758`, `1761`, `1764`, `1765`, `1766`, `1767`, `1768`, `1769`, `1771`, `1772`, `1774`, `1776`, `1777`, `1780`, `1783`, `1784`, `1787`, `1789`, `1791`, `1792`, `1794`, `1797`, `1798`, `1800`, `1803`, `1804`, `1807`, `1808`, `1810`, `1812`, `1814`, `1815`, `1817`, `1819`, `1820`, `1822`, `1824`, `1825`, `1826`, `1827`, `1830`, `1832`, `1833`, `1836`, `1840`, `1843`, `1844`, `1846`, `1849`, `1851`, `1853`, `1854`, `1857`, `1859`, `1860`, `1861`, `1862`, `1863`, `1864`, `1865`, `1868`, `1869`, `1872`, `1873`, `1875`, `1877`, `1878`, `1879`, `1882`, `1884`, `1886`, `1888`, `1889`, `1892`, `1895`, `1898`, `1899`, `1901`, `1903`, `1904`, `1905`, `1907`, `1910`, `1912`, `1913`, `1914`, `1917`, `1919`, `1921`, `1924`, `1925`, `1926`, `1928`, `1931`, `1934`, `1936`, `1938`, `1939`, `1636`, `1942`, `1945`, `1947`, `1948`, `1949`, `1950`, `1952`, `1954`, `1956`, `1957`, `1959`, `1961`, `1963`, `1964`, `1965`, `1968`, `1969`, `1970`, `1971`, `1973`, `1974`, `1978`, `1980`, `1981`, `1983`, `1984`, `1987`, `1990`, `1991`, `1994`, `1995`, `1996`, `1997`, `1998`, `1999`, `2001`, `2003`, `2004`, `2006`, `2008`, `2010` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.74 |
| `TOKEN_P` | 99.73 |
| `TOKEN_R` | 99.74 |
| `TOKEN_ACC` | 99.95 |
| `SENTS_F` | 97.57 |
| `SENTS_P` | 97.35 |
| `SENTS_R` | 97.78 |
| `TAG_ACC` | 93.34 |
| `POS_ACC` | 92.17 |
| `MORPH_ACC` | 68.98 |
| `DEP_UAS` | 83.61 |
| `DEP_LAS` | 74.65 |
| `LEMMA_ACC` | 89.81 |
|
{"language": ["ga"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/ga_udv25_irishidt_trf
|
[
"spacy",
"token-classification",
"ga",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ga"
] |
TAGS
#spacy #token-classification #ga #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Irish-IDT
### Label Scheme
View label scheme (1662 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1662 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #ga #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1662 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #ga #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1662 labels for 6 components)### Accuracy"
] |
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] |
null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Croatian-SET
| Feature | Description |
| --- | --- |
| **Name** | `hr_udv25_croatianset_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (3855 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `Agcfpay`, `Agcfpdy`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmply`, `Agcmpny`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnpdy`, `Agcnpgy`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpfsvy`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmpvy`, `Agpmsann`, `Agpmsany`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsln`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpmsvy`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsln`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpdy`, `Agsfpgy`, `Agsfpiy`, `Agsfply`, `Agsfpny`, `Agsfsay`, `Agsfsdy`, `Agsfsgy`, `Agsfsiy`, `Agsfsly`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmpiy`, `Agsmply`, `Agsmpny`, `Agsmpvy`, `Agsmsayn`, `Agsmsayy`, `Agsmsdy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnply`, `Agsnpny`, `Agsnsay`, `Agsnsdy`, `Agsnsiy`, `Agsnsly`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpdy`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsany`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgn`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpdy`, `Appnpgy`, `Appnpiy`, `Appnply`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsiy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmply`, `Aspmpny`, `Aspmsann`, `Aspmsdy`, `Aspmsgn`, `Aspmsgy`, `Aspmsiy`, `Aspmsln`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnpgy`, `Aspnpny`, `Aspnsay`, `Aspnsdn`, `Aspnsgn`, `Aspnsgy`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--g`, `Mlc--i`, `Mlc--l`, `Mlcf-a`, `Mlcf-d`, `Mlcf-g`, `Mlcf-n`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-g`, `Mlcm-l`, `Mlcm-n`, `Mlcmpl`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsg`, `Mlcnsl`, `Mlcnsn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompd`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsan`, `Mlomsay`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlomsv`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsd`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-d`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-g`, `Mlsm-l`, `Mlsm-n`, `Mlsn-n`, `Mro`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfpv`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncfsv`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmpv`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Ncnsv`, `Npfpa`, `Npfpg`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npmsv`, `Npnpg`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msan`, `Pd-msay`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npi`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pi-fpa`, `Pi-fpd`, `Pi-fpg`, `Pi-fpi`, `Pi-fpl`, `Pi-fpn`, `Pi-fsa`, `Pi-fsd`, `Pi-fsg`, `Pi-fsi`, `Pi-fsl`, `Pi-fsn`, `Pi-mpa`, `Pi-mpd`, `Pi-mpg`, `Pi-mpi`, `Pi-mpl`, `Pi-mpn`, `Pi-msan`, `Pi-msay`, `Pi-msd`, `Pi-msg`, `Pi-msi`, `Pi-msl`, `Pi-msn`, `Pi-npa`, `Pi-npd`, `Pi-npg`, `Pi-npi`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsd`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-i`, `Pi3m-n`, `Pi3n-a`, `Pi3n-d`, `Pi3n-g`, `Pi3n-i`, `Pi3n-l`, `Pi3n-n`, `Pp1-pa`, `Pp1-pd`, `Pp1-pg`, `Pp1-pi`, `Pp1-pl`, `Pp1-pn`, `Pp1-sa`, `Pp1-sd`, `Pp1-si`, `Pp1-sl`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pg`, `Pp2-pn`, `Pp2-sa`, `Pp2-sd`, `Pp2-sg`, `Pp2-sl`, `Pp2-sn`, `Pp2-sv`, `Pp3-pa`, `Pp3-pd`, `Pp3-pg`, `Pp3-pi`, `Pp3-pl`, `Pp3fpn`, `Pp3fsa`, `Pp3fsd`, `Pp3fsg`, `Pp3fsi`, `Pp3fsl`, `Pp3fsn`, `Pp3mpn`, `Pp3msa`, `Pp3msd`, `Pp3msg`, `Pp3msi`, `Pp3msl`, `Pp3msn`, `Pp3npn`, `Pp3nsa`, `Pp3nsi`, `Pp3nsn`, `Pq-fpa`, `Pq-fpn`, `Pq-fsa`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3m-d`, `Pq3m-n`, `Pq3n-a`, `Pq3n-l`, `Pq3n-n`, `Ps1fpa`, `Ps1fpd`, `Ps1fpg`, `Ps1fpl`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsi`, `Ps1fsl`, `Ps1fsn`, `Ps1fsv`, `Ps1mpa`, `Ps1mpd`, `Ps1mpg`, `Ps1mpi`, `Ps1mpl`, `Ps1mpn`, `Ps1mpv`, `Ps1msan`, `Ps1msay`, `Ps1msd`, `Ps1msg`, `Ps1msi`, `Ps1msl`, `Ps1msn`, `Ps1msv`, `Ps1npn`, `Ps1nsa`, `Ps1nsg`, `Ps1nsi`, `Ps1nsl`, `Ps1nsn`, `Ps2fpa`, `Ps2fpl`, `Ps2fpn`, `Ps2fsa`, `Ps2fsd`, `Ps2fsg`, `Ps2fsn`, `Ps2mpa`, `Ps2mpg`, `Ps2mpl`, `Ps2mpn`, `Ps2msan`, `Ps2msd`, `Ps2msg`, `Ps2msi`, `Ps2msl`, `Ps2msn`, `Ps2npn`, `Ps2nsa`, `Ps2nsg`, `Ps2nsi`, `Ps2nsl`, `Ps2nsn`, `Ps3fpa`, `Ps3fpg`, `Ps3fpl`, `Ps3fpn`, `Ps3fsa`, `Ps3fsd`, `Ps3fsg`, `Ps3fsi`, `Ps3fsl`, `Ps3fsn`, `Ps3mpa`, `Ps3mpd`, `Ps3mpg`, `Ps3mpi`, `Ps3mpl`, `Ps3mpn`, `Ps3msan`, `Ps3msay`, `Ps3msd`, `Ps3msg`, `Ps3msi`, `Ps3msl`, `Ps3msn`, `Ps3npa`, `Ps3npg`, `Ps3npl`, `Ps3npn`, `Ps3nsa`, `Ps3nsg`, `Ps3nsi`, `Ps3nsl`, `Ps3nsn`, `Px--sa`, `Px--sd`, `Px--sg`, `Px--si`, `Px--sl`, `Px-fpa`, `Px-fpg`, `Px-fpi`, `Px-fpl`, `Px-fsa`, `Px-fsd`, `Px-fsg`, `Px-fsi`, `Px-fsl`, `Px-mpa`, `Px-mpd`, `Px-mpg`, `Px-mpi`, `Px-mpl`, `Px-msan`, `Px-msay`, `Px-msd`, `Px-msg`, `Px-msi`, `Px-msl`, `Px-npa`, `Px-npg`, `Px-npi`, `Px-npl`, `Px-nsa`, `Px-nsg`, `Px-nsi`, `Px-nsl`, `Qo`, `Qq`, `Qr`, `Qz`, `Rgc`, `Rgp`, `Rgs`, `Rr`, `Sa`, `Sd`, `Sg`, `Si`, `Sl`, `Vaa1p`, `Vaa1s`, `Vaa2p`, `Vaa2s`, `Vaa3p`, `Vaa3s`, `Vae3s`, `Vam2p`, `Van`, `Vap-pf`, `Vap-pm`, `Vap-pn`, `Vap-sf`, `Vap-sm`, `Vap-sn`, `Var1p`, `Var1s`, `Var2p`, `Var2s`, `Var3p`, `Var3s`, `Vma3s`, `Vmm1p`, `Vmm2p`, `Vmm2s`, `Vmn`, `Vmp-pf`, `Vmp-pm`, `Vmp-pn`, `Vmp-sf`, `Vmp-sm`, `Vmp-sn`, `Vmr1p`, `Vmr1s`, `Vmr2p`, `Vmr2s`, `Vmr3p`, `Vmr3s`, `X`, `Xf`, `Y`, `Z` |
| **`morphologizer`** | `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=X`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `NumType=Ord\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `POS=PART\|Polarity=Neg`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|POS=ADP`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Degree=Pos\|POS=ADV\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=PART`, `Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADV\|Tense=Past\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=ADV\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|POS=ADV\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=ADV\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=NOUN`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=ADV\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=DET\|Polarity=Neg`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `NumType=Ord\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Ins\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|POS=ADP`, `Degree=Sup\|POS=ADV`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `POS=ADV\|Tense=Pres\|VerbForm=Conv`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `NumType=Mult\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `NumType=Mult\|POS=SYM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `POS=DET`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=DET`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Loc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Tot`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=INTJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=PART\|Polarity=Pos`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|NumType=Mult\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=DET\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Gender=Neut\|Number=Sing\|POS=DET\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Voc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Neg`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `expl`, `expl:pv`, `fixed`, `flat`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `3`, `5`, `7`, `9`, `10`, `12`, `14`, `16`, `17`, `19`, `21`, `23`, `25`, `27`, `29`, `31`, `33`, `35`, `38`, `40`, `41`, `43`, `45`, `47`, `49`, `51`, `53`, `56`, `58`, `60`, `62`, `64`, `65`, `67`, `69`, `71`, `73`, `75`, `76`, `79`, `82`, `84`, `86`, `88`, `90`, `92`, `94`, `96`, `98`, `100`, `102`, `104`, `106`, `110`, `112`, `114`, `116`, `118`, `120`, `124`, `126`, `128`, `129`, `130`, `132`, `134`, `136`, `139`, `141`, `142`, `145`, `148`, `150`, `151`, `153`, `154`, `156`, `158`, `159`, `161`, `162`, `164`, `165`, `166`, `168`, `170`, `172`, `175`, `176`, `177`, `179`, `181`, `186`, `188`, `189`, `192`, `194`, `197`, `199`, `201`, `202`, `204`, `205`, `208`, `210`, `212`, `214`, `217`, `219`, `221`, `223`, `225`, `227`, `229`, `231`, `233`, `235`, `237`, `241`, `243`, `245`, `247`, `249`, `251`, `253`, `255`, `173`, `256`, `257`, `259`, `262`, `264`, `266`, `269`, `270`, `271`, `273`, `275`, `276`, `279`, `280`, `281`, `282`, `283`, `284`, `286`, `288`, `290`, `291`, `293`, `295`, `296`, `298`, `300`, `302`, `303`, `304`, `305`, `307`, `308`, `309`, `311`, `313`, `315`, `317`, `318`, `321`, `323`, `325`, `326`, `329`, `331`, `333`, `335`, `336`, `337`, `338`, `339`, `340`, `341`, `343`, `226`, `346`, `347`, `349`, `350`, `351`, `353`, `355`, `358`, `360`, `363`, `365`, `366`, `368`, `371`, `374`, `376`, `379`, `381`, `382`, `384`, `385`, `387`, `389`, `391`, `393`, `396`, `398`, `400`, `402`, `403`, `405`, `406`, `408`, `410`, `412`, `415`, `418`, `419`, `421`, `425`, `426`, `428`, `429`, `431`, `432`, `433`, `435`, `436`, `438`, `439`, `440`, `441`, `442`, `444`, `446`, `448`, `450`, `452`, `454`, `456`, `457`, `459`, `461`, `462`, `465`, `466`, `468`, `469`, `471`, `473`, `475`, `477`, `479`, `481`, `483`, `484`, `485`, `487`, `488`, `489`, `492`, `493`, `494`, `495`, `497`, `500`, `501`, `502`, `507`, `508`, `512`, `513`, `516`, `517`, `519`, `520`, `521`, `524`, `525`, `526`, `528`, `529`, `531`, `532`, `534`, `535`, `536`, `538`, `539`, `543`, `545`, `546`, `547`, `548`, `550`, `552`, `554`, `555`, `557`, `559`, `561`, `564`, `566`, `567`, `570`, `571`, `572`, `573`, `575`, `576`, `579`, `583`, `584`, `585`, `586`, `588`, `590`, `592`, `594`, `597`, `598`, `601`, `603`, `605`, `606`, `608`, `610`, `613`, `614`, `617`, `619`, `622`, `623`, `624`, `627`, `629`, `631`, `633`, `635`, `637`, `639`, `641`, `643`, `644`, `646`, `647`, `648`, `650`, `652`, `656`, `657`, `661`, `663`, `664`, `666`, `669`, `670`, `672`, `674`, `676`, `678`, `680`, `681`, `682`, `684`, `685`, `686`, `687`, `688`, `689`, `690`, `692`, `693`, `695`, `696`, `698`, `699`, `701`, `702`, `705`, `706`, `707`, `708`, `710`, `712`, `714`, `715`, `716`, `717`, `718`, `719`, `721`, `722`, `723`, `724`, `727`, `728`, `729`, `732`, `733`, `737`, `738`, `739`, `740`, `741`, `742`, `744`, `745`, `748`, `750`, `752`, `754`, `756`, `758`, `760`, `762`, `763`, `764`, `765`, `768`, `770`, `772`, `773`, `774`, `775`, `777`, `778`, `780`, `782`, `783`, `786`, `788`, `789`, `790`, `792`, `795`, `796`, `798`, `801`, `802`, `805`, `807`, `809`, `811`, `813`, `816`, `818`, `820`, `822`, `824`, `825`, `826`, `829`, `833`, `835`, `838`, `840`, `841`, `842`, `843`, `845`, `847`, `849`, `851`, `854`, `855`, `857`, `859`, `860`, `861`, `863`, `865`, `866`, `867`, `868`, `869`, `872`, `874`, `876`, `878`, `879`, `882`, `884`, `885`, `886`, `887`, `890`, `891`, `892`, `893`, `894`, `895`, `896`, `898`, `899`, `901`, `903`, `904`, `906`, `907`, `909`, `910`, `911`, `912`, `913`, `915`, `916`, `918`, `920`, `922`, `923`, `924`, `925`, `926`, `927`, `929`, `930`, `933`, `934`, `935`, `937`, `938`, `940`, `942`, `944`, `945`, `946`, `947`, `948`, `949`, `951`, `953`, `955`, `958`, `959`, `960`, `961`, `962`, `964`, `965`, `967`, `969`, `974`, `976`, `977`, `979`, `980`, `981`, `983`, `985`, `986`, `988`, `989`, `990`, `991`, `993`, `994`, `996`, `998`, `1001`, `1002`, `1004`, `1007`, `1008`, `1010`, `1011`, `1014`, `1015`, `1017`, `1018`, `383`, `1020`, `1021`, `1022`, `1023`, `1024`, `1025`, `1027`, `1030`, `1032`, `1033`, `1035`, `1037`, `1038`, `1039`, `1041`, `1043`, `1044`, `1045`, `1046`, `1047`, `1048`, `1049`, `1051`, `1053`, `1055`, `1056`, `1059`, `1060`, `1063`, `1065`, `1066`, `1068`, `1069`, `1070`, `1072`, `1073`, `1074`, `1075`, `1077`, `1080`, `1081`, `1084`, `1085`, `1087`, `1089`, `1091`, `1092`, `1093`, `1094`, `1096`, `1097`, `1099`, `1101`, `1102`, `1103`, `1104`, `1105`, `1107`, `1109`, `1110`, `1113`, `1114`, `1115`, `1117`, `1118`, `1119`, `1120`, `1121`, `1122`, `1123`, `1124`, `1125`, `1126`, `1128`, `1131`, `1133`, `1134`, `1135`, `1137`, `1138`, `1140`, `1142`, `1143`, `1144`, `1146`, `1148`, `1149`, `1150`, `1151`, `1153`, `1154`, `1156`, `1158`, `1159`, `1160`, `1163`, `1165`, `1169`, `1172`, `1174`, `1177`, `1179`, `1181`, `1182`, `1184`, `1187`, `1189`, `1190`, `1193`, `1195`, `1196`, `1198`, `1199`, `1200`, `1203`, `1204`, `1205`, `1206`, `1207`, `1208`, `1209`, `1210`, `1211`, `1213`, `1214`, `1215`, `1216`, `1217`, `1218`, `1220`, `1222`, `1223`, `1225`, `1229`, `1230`, `1232`, `1233`, `1237`, `1238`, `1239`, `1242`, `1243`, `1244`, `1246`, `1249`, `1250`, `1251`, `1252`, `1255`, `1256`, `1257`, `1260`, `1262`, `1264`, `1266`, `1268`, `1270`, `1272`, `1274`, `1276`, `1277`, `1278`, `1279`, `1281`, `1282`, `1283`, `1285`, `1288`, `1290`, `1292`, `1294`, `1296`, `1298`, `1299`, `1300`, `1302`, `1303`, `1304`, `1306`, `1307`, `1309`, `1311`, `1313`, `1314`, `1315`, `1316`, `1318`, `1319`, `1320`, `1321`, `1322`, `1323`, `1325`, `1326`, `1327`, `1329`, `1331`, `1333`, `1334`, `1335`, `1337`, `1338`, `1340`, `1341`, `1342`, `1343`, `1345`, `1346`, `1349`, `1351`, `1353`, `1355`, `1357`, `1358`, `1361`, `1362`, `1363`, `1364`, `1365`, `1366`, `1367`, `1368`, `1369`, `1372`, `1324`, `1373`, `1374`, `1375`, `1376`, `1378`, `1380`, `1382`, `1383`, `1386`, `1388`, `1390`, `1392`, `1393`, `1394`, `1395`, `1396`, `1397`, `1398`, `1400`, `1401`, `1403`, `1404`, `1406`, `1407`, `1410`, `1411`, `1412`, `1416`, `1418`, `1419`, `1421`, `1422`, `1423`, `1425`, `1426`, `1427`, `1430`, `1431`, `1433`, `1434`, `1435`, `1436`, `1437`, `1438`, `1439`, `1441`, `1442`, `1443`, `1444`, `1445`, `1447`, `1448`, `1451`, `1452`, `1453`, `1454`, `1456`, `1457`, `1458`, `1460`, `1461`, `1463`, `1464`, `1467`, `1469`, `1471`, `1473`, `1475`, `1476`, `1478`, `1479`, `1480`, `1482`, `1484`, `1485`, `1488`, `1489`, `1490`, `1491`, `1494`, `1495`, `1496`, `1498`, `1499`, `1501`, `1503`, `1506`, `1507`, `1508`, `1510`, `1512`, `1514`, `1516`, `1519`, `1520`, `1523`, `1524`, `1526`, `1527`, `1528`, `1529`, `1530`, `1531`, `1532`, `1533`, `1534`, `1536`, `1537`, `1538`, `1540`, `1541`, `1542`, `1543`, `1544`, `1545`, `1546`, `1549`, `1550`, `1552`, `1553`, `1554`, `1556`, `1557`, `1558`, `1559`, `1560`, `1561`, `1562`, `1564`, `1566`, `1568`, `1569`, `1570`, `1573`, `1575`, `1577`, `1578`, `1579`, `1581`, `1582`, `1583`, `1584`, `1585`, `1587`, `1589`, `1591`, `1592`, `1593`, `1595`, `1597`, `1599`, `1601`, `1603`, `1605`, `1606`, `1608`, `1609`, `1611`, `1612`, `1613`, `1614`, `1615`, `1616`, `1617`, `1618`, `1620`, `1623`, `1624`, `1625`, `1627`, `1629`, `1630`, `1631`, `1632`, `1633`, `1635`, `1637`, `1640`, `1641`, `1642`, `1643`, `1644`, `1645`, `1647`, `1648`, `1650`, `1651`, `1652`, `1653`, `1654`, `1655`, `1657`, `1658`, `1659`, `1660`, `1661`, `1663`, `1666`, `1667`, `1668`, `1669`, `1670`, `1671`, `1672`, `1673`, `1674`, `1675`, `1676`, `1677`, `1679`, `1680`, `1683`, `1685`, `1686`, `160`, `1687`, `1689`, `1691`, `1693`, `1694`, `1695`, `1696`, `1697`, `1698`, `1699`, `1700`, `1702`, `1704`, `1705`, `1707`, `1708`, `1709`, `1710`, `1712`, `1713`, `1714`, `1716`, `1718`, `1720`, `1722`, `1724`, `1725`, `1726`, `1417`, `1727`, `1728`, `1729`, `1730`, `1732`, `1734`, `1735`, `1736`, `1738`, `1740`, `1741`, `1743`, `1744`, `1745`, `1747`, `1749`, `1752`, `1754`, `1756`, `1759`, `1761`, `1764`, `1766`, `1768`, `1770`, `1772`, `1774`, `1775`, `1776`, `1778`, `1779`, `1781`, `1783`, `1784`, `1786`, `1788`, `1789`, `1790`, `1792`, `1794`, `1795`, `1797`, `1798`, `1799`, `1801`, `1802`, `1805`, `1807`, `1809`, `1810`, `1811`, `1812`, `1814`, `1815`, `1816`, `1818`, `1819`, `1820`, `1821`, `1823`, `1824`, `1825`, `1826`, `1827`, `1828`, `1829`, `1831`, `1833`, `1834`, `1836`, `1838`, `1839`, `1841`, `1842`, `1845`, `1847`, `1850`, `1851`, `1853`, `1854`, `1856`, `1857`, `1858`, `1860`, `1861`, `1862`, `1864`, `1865`, `1866`, `1867`, `1869`, `1870`, `1871`, `1872`, `1873`, `1875`, `1878`, `1879`, `1880`, `1881`, `1883`, `1885`, `1886`, `1888`, `1890`, `1891`, `1892`, `1893`, `1894`, `1895`, `1896`, `1898`, `1900`, `1901`, `1908`, `1910`, `1911`, `1912`, `1913`, `1915`, `1916`, `1917`, `1919`, `1920`, `1921`, `1922`, `1924`, `1925`, `1926`, `1927`, `1928`, `1930`, `1931`, `1932`, `1934`, `1935`, `1936`, `1937`, `1938`, `1939`, `1941`, `1942`, `1944`, `542`, `1946`, `1947`, `1949`, `1951`, `1952`, `1953`, `1954`, `1955`, `1957`, `1959`, `1960`, `1963`, `1964`, `1965`, `1966`, `1967`, `1969`, `1971`, `1973`, `1974`, `1975`, `1977`, `1979`, `1981`, `1982`, `1984`, `1985`, `1986`, `1988`, `1989`, `1990`, `1991`, `1993`, `1994`, `1996`, `1998`, `1999`, `2000`, `2001`, `2003`, `2005`, `2006`, `2007`, `2009`, `2010`, `2012`, `2013`, `314`, `2015`, `2016`, `2017`, `2019`, `2021`, `2023`, `2025`, `2026`, `2028`, `2029`, `2031`, `2034`, `2036`, `2038`, `2039`, `2041`, `1565`, `2043`, `2045`, `2046`, `2047`, `2049`, `2051`, `2053`, `2054`, `2055`, `2057`, `2059`, `2060`, `2062`, `2064`, `2065`, `2067`, `2068`, `2070`, `2071`, `2072`, `2073`, `2074`, `2075`, `2078`, `2079`, `2080`, `2082`, `2085`, `2086`, `2089`, `2091`, `2092`, `2096`, `2098`, `2100`, `2102`, `2103`, `2104`, `2105`, `2106`, `2109`, `2110`, `2112`, `133`, `2113`, `2115`, `2117`, `2120`, `2121`, `2122`, `2126`, `2127`, `2129`, `2130`, `2132`, `2134`, `2135`, `2137`, `2138`, `2139`, `2141`, `2143`, `2145`, `2147`, `2148`, `2149`, `2151`, `2152`, `2154`, `1976`, `2156`, `2157`, `2158`, `2159`, `2160`, `2161`, `2162`, `2163`, `2164`, `2165`, `2168`, `2170`, `2171`, `2173`, `2174`, `2177`, `2178`, `2180`, `2181`, `2184`, `2188`, `2189`, `2190`, `2191`, `2192`, `2194`, `2195`, `2196`, `2197`, `2198`, `2199`, `2200`, `2201`, `2204`, `2207`, `2208`, `2211`, `2213`, `2214`, `2215`, `2216`, `2218`, `2221`, `2222`, `2223`, `2225`, `2227`, `2229`, `2231`, `2232`, `2233`, `2235`, `2236`, `2237`, `2238`, `2239`, `2240`, `2241`, `2243`, `2245`, `2247`, `2249`, `2250`, `2252`, `2254`, `2256`, `2258`, `2259`, `2261`, `2264`, `2266`, `2268`, `2269`, `2270`, `2272`, `2273`, `2275`, `2276`, `2277`, `2279`, `2281`, `2283`, `2284`, `2286`, `2287`, `2289`, `2291`, `2292`, `2293`, `2295`, `2296`, `2297`, `2299`, `2300`, `2302`, `2305`, `2306`, `2307`, `2308`, `2309`, `2310`, `2311`, `2312`, `2314`, `2315`, `2316`, `2318`, `2319`, `2320`, `2321`, `2322`, `2324`, `2326`, `2327`, `2328`, `2330`, `2331`, `2332`, `2334`, `2336`, `2339`, `2340`, `2341`, `2343`, `2344`, `2346`, `2348`, `2350`, `2352`, `2355`, `2356`, `2357`, `2358`, `2361`, `2363`, `2365`, `2367`, `2369`, `2370`, `2371`, `2374`, `2375`, `2376`, `2377`, `2378`, `2380`, `2381`, `2382`, `2383`, `2384`, `2386`, `2387`, `2390`, `2392`, `2395`, `2396`, `2399`, `2401`, `2403`, `2404`, `2405`, `2406`, `2407`, `2408`, `2409`, `2410`, `2412`, `2413`, `2416`, `2417`, `2418`, `2419`, `2420`, `2423`, `2424`, `2426`, `2428`, `2429`, `2430`, `2431`, `2432`, `2433`, `2435`, `2436`, `2437`, `2440`, `2441`, `2442`, `2443`, `2447`, `2448`, `2450`, `2451`, `2452`, `2454`, `2458`, `2460`, `2461`, `2463`, `2465`, `2467`, `2471`, `2473`, `2475`, `2477`, `2479`, `2481`, `2482`, `2484`, `2485`, `2487`, `2488`, `2490`, `2491`, `2493`, `2495`, `2496`, `2497`, `2498`, `2499`, `2500`, `2501`, `2502`, `2505`, `2506`, `2507`, `2509`, `2511`, `2513`, `2515`, `2516`, `2517`, `2519`, `2520`, `2522`, `2524`, `2525`, `2526`, `2527`, `2528`, `2530`, `2532`, `2534`, `2535`, `2537`, `2538`, `2539`, `2541`, `2542`, 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`3433`, `3434`, `3435`, `3437`, `3439`, `3440`, `3442`, `3444`, `3445`, `3446`, `3447`, `3449`, `3451`, `3452`, `3453`, `3456`, `3457`, `3458`, `3459`, `3460`, `85`, `3461`, `3463`, `3464`, `3465`, `3467`, `3469`, `3471`, `3473`, `3475`, `3477`, `3478`, `3480`, `3481`, `3482`, `3483`, `3484`, `3485`, `3486`, `3487`, `3489`, `3491`, `3494`, `3496`, `3497`, `3498`, `3499`, `3501`, `3502`, `3504`, `3505`, `3506`, `3508`, `3509`, `3510`, `3512`, `3513`, `3517`, `3518`, `3519`, `3520`, `3521`, `3522`, `3524`, `3525`, `3526`, `3527`, `3529`, `3532`, `3533`, `3535`, `3536`, `3537`, `3539`, `3541`, `3542`, `3543` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.97 |
| `TOKEN_P` | 99.97 |
| `TOKEN_R` | 99.96 |
| `TOKEN_ACC` | 99.99 |
| `SENTS_F` | 98.90 |
| `SENTS_P` | 99.06 |
| `SENTS_R` | 98.75 |
| `TAG_ACC` | 96.40 |
| `POS_ACC` | 98.50 |
| `MORPH_ACC` | 96.78 |
| `DEP_UAS` | 92.41 |
| `DEP_LAS` | 87.03 |
| `LEMMA_ACC` | 96.35 |
|
{"language": ["hr"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/hr_udv25_croatianset_trf
|
[
"spacy",
"token-classification",
"hr",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hr"
] |
TAGS
#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Croatian-SET
### Label Scheme
View label scheme (3855 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (3855 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3855 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (3855 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Indonesian-GSD
| Feature | Description |
| --- | --- |
| **Name** | `id_udv25_indonesiangsd_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1325 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `APP`, `ASP`, `ASP+PS2`, `ASP+PS3`, `ASP+T--`, `ASS`, `ASS+PS3`, `B--`, `B--+PS3`, `B--+T--`, `CC-`, `CC-+PS3`, `CC-+T--`, `CD-`, `CD-+PS3`, `CO-`, `CO-+PS3`, `D--`, `D--+PS2`, `D--+PS3`, `D--+T--`, `F--`, `F--+PS1`, `F--+PS2`, `F--+PS3`, `F--+T--`, `G--`, `G--+PS3`, `G--+T--`, `H--`, `H--+T--`, `I--`, `M--`, `M--+PS3`, `M--+T--`, `NOUN`, `NPD`, `NPD+PS2`, `NPD+PS3`, `NSD`, `NSD+PS1`, `NSD+PS2`, `NSD+PS3`, `NSD+T--`, `NSF`, `NSM`, `NSM+PS3`, `NUM`, `O--`, `PP1`, `PP1+T--`, `PP2`, `PP3`, `PP3+T--`, `PROPN`, `PS1`, `PS1+VSA`, `PS1+VSA+T--`, `PS2`, `PS2+VSA`, `PS3`, `PUNCT`, `R--`, `R--+PS1`, `R--+PS2`, `R--+PS3`, `S--`, `S--+PS3`, `T--`, `VERB`, `VPA`, `VSA`, `VSA+PS1`, `VSA+PS2`, `VSA+PS3`, `VSA+T--`, `VSP`, `VSP+PS3`, `VSP+T--`, `W--`, `W--+T--`, `X`, `X--`, `Z--` |
| **`morphologizer`** | `POS=PROPN`, `POS=AUX`, `POS=DET\|PronType=Ind`, `Number=Sing\|POS=NOUN`, `POS=PRON\|PronType=Rel`, `Number=Sing\|POS=VERB\|Voice=Pass`, `POS=ADP`, `POS=PUNCT`, `Number=Sing\|POS=PROPN`, `POS=NOUN`, `POS=ADV`, `POS=CCONJ`, `Number=Sing\|POS=VERB\|Voice=Act`, `POS=VERB`, `POS=DET\|PronType=Tot`, `Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `POS=SCONJ`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=DET\|PronType=Dem`, `NumType=Card\|POS=NUM`, `Degree=Pos\|Number=Sing\|POS=NOUN`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `NumType=Card\|POS=DET\|PronType=Ind`, `Degree=Pos\|Number=Sing\|POS=ADP`, `Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Number=Sing\|POS=VERB`, `POS=PRON\|PronType=Int`, `Number=Sing\|POS=ADV\|Voice=Act`, `Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Voice=Act`, `Number=Sing\|POS=ADP\|Voice=Act`, `POS=ADJ`, `Number[psor]=Sing\|POS=ADP\|Person[psor]=3`, `Degree=Pos\|Number=Sing\|POS=DET`, `Degree=Pos\|Number=Sing\|POS=VERB`, `POS=PRON\|PronType=Dem`, `POS=PART\|Polarity=Neg`, `Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Number=Sing\|POS=PRON\|Person=1\|Polite=Form\|PronType=Prs`, `Number=Sing\|POS=ADJ`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SYM`, `POS=ADV\|PronType=Int`, `Clusivity=In\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=ADJ\|Voice=Act`, `Degree=Pos\|Number=Sing\|POS=PROPN`, `Degree=Pos\|Number=Sing\|POS=ADV`, `Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Voice=Pass`, `Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3\|Voice=Act`, `Number=Sing\|POS=PROPN\|Voice=Act`, `Number=Sing\|POS=NOUN\|Voice=Act`, `POS=DET`, `Number=Sing\|POS=DET\|Voice=Act`, `NumType=Card\|POS=PRON\|PronType=Ind`, `Number=Sing\|Number[psor]=Sing\|POS=ADV\|Person[psor]=3`, `Number=Sing\|POS=DET`, `Number=Sing\|POS=ADJ\|Voice=Pass`, `POS=CCONJ\|PronType=Dem`, `Number=Sing\|POS=ADP`, `Number=Sing\|POS=ADV`, `Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PronType=Prs`, `Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Number=Sing\|POS=PRON`, `POS=PRON`, `NumType=Card\|POS=ADV\|PronType=Ind`, `NumType=Card\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Number=Sing\|POS=PRON\|Person=3\|Polite=Form\|PronType=Prs`, `POS=DET\|PronType=Int`, `Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=1`, `Degree=Pos\|Number=Sing\|POS=SCONJ`, `POS=PRON\|PronType=Ind`, `Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3\|Voice=Pass`, `POS=VERB\|PronType=Ind`, `Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Number=Sing\|POS=SCONJ`, `Degree=Sup\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person[psor]=3`, `Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Number=Plur\|POS=NOUN`, `POS=ADV\|PronType=Dem`, `Number=Sing\|POS=VERB\|Person=1\|Voice=Act`, `Degree=Sup\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=ADP\|Voice=Pass`, `Number[psor]=Sing\|POS=PART\|Person[psor]=3`, `Number=Sing\|POS=NOUN\|Voice=Pass`, `Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=CCONJ\|Person[psor]=3`, `POS=PART`, `Number=Sing\|Number[psor]=Sing\|POS=PART\|Person[psor]=3\|Voice=Pass`, `Degree=Sup\|Number=Sing\|POS=ADV`, `Number=Sing\|POS=PRON\|Voice=Act`, `Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3\|Voice=Act`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Number[psor]=Sing\|POS=PRON\|Person[psor]=3\|PronType=Tot`, `Degree=Pos\|Number=Sing\|POS=X`, `POS=PRON\|PronType=Tot`, `Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADV\|Person[psor]=3`, `Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3`, `Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person[psor]=3`, `Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `POS=SCONJ\|PronType=Int`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Voice=Act`, `Number[psor]=Sing\|POS=DET\|Person[psor]=3`, `Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person[psor]=3`, `Clusivity=Ex\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=VERB\|Voice=Act`, `Number=Sing\|Number[psor]=Sing\|POS=ADV\|Person[psor]=3\|Voice=Act`, `Degree=Pos\|Number=Sing\|POS=NOUN\|Polarity=Neg`, `POS=X`, `Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3`, `Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|Polite=Infm\|PronType=Prs`, `Number=Sing\|POS=PROPN\|Voice=Pass`, `POS=ADV\|Polarity=Neg`, `NumType=Card\|Number=Sing\|POS=NUM`, `Number[psor]=Sing\|POS=ADV\|Person[psor]=2`, `Number[psor]=Sing\|POS=ADV\|Person[psor]=3`, `Degree=Sup\|Number=Sing\|POS=PROPN`, `POS=PROPN\|Polarity=Neg`, `Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Voice=Act`, `Number=Sing\|POS=PROPN\|Person=1\|Voice=Act`, `POS=SCONJ\|PronType=Dem`, `Number=Sing\|Number[psor]=Sing\|POS=ADV\|Person[psor]=2\|Voice=Act`, `Number=Sing\|POS=CCONJ`, `Degree=Sup\|Number=Sing\|POS=VERB`, `Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3\|Voice=Act`, `Degree=Pos\|Number=Sing\|POS=PRON`, `Number=Sing\|POS=ADV\|Voice=Pass`, `Number[psor]=Sing\|POS=ADP\|Person[psor]=2`, `Number=Sing\|POS=SYM`, `POS=ADJ\|Polarity=Neg`, `Degree=Pos\|NumType=Card\|Number=Sing\|POS=NUM`, `Number=Sing\|Number[psor]=Sing\|POS=SCONJ\|Person[psor]=3`, `Degree=Pos\|Number=Sing\|POS=CCONJ`, `Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Number=Sing\|POS=CCONJ\|Voice=Act`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person[psor]=3\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=VERB\|PronType=Dem`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Number=Sing\|POS=PART\|Voice=Act`, `Degree=Sup\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `POS=ADP\|PronType=Int`, `Number[psor]=Sing\|POS=VERB\|Person[psor]=3`, `Number[psor]=Sing\|POS=PRON\|Person[psor]=3\|PronType=Rel`, `Degree=Pos\|Number=Sing\|POS=AUX`, `Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Number=Sing\|POS=SCONJ\|Voice=Pass`, `Degree=Sup\|Number=Sing\|POS=ADP`, `Number=Sing\|POS=SCONJ\|Voice=Act`, `NumType=Card\|POS=DET\|PronType=Int`, `Degree=Pos\|Number=Sing\|POS=PART\|Polarity=Neg`, `Degree=Sup\|Number=Sing\|POS=SCONJ`, `Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1\|Voice=Act`, `Number=Plur\|POS=ADJ`, `POS=VERB\|PronType=Int`, `Number=Sing\|POS=VERB\|Person=2\|Voice=Act`, `Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=2`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Number[psor]=Sing\|POS=ADV\|Person[psor]=3\|PronType=Tot`, `POS=DET\|PronType=Rel`, `Number=Sing\|POS=NOUN\|Polarity=Neg`, `Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=2`, `NumType=Card\|Number=Sing\|POS=NUM\|Voice=Act`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Number[psor]=Sing\|POS=DET\|Person[psor]=3\|PronType=Tot`, `Number[psor]=Sing\|POS=PROPN\|Person[psor]=1`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=VERB\|Person=1`, `Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `NumType=Card\|Number[psor]=Sing\|POS=DET\|Person[psor]=3\|PronType=Ind`, `POS=ADV\|PronType=Tot`, `Degree=Pos\|Number=Plur\|POS=ADV`, `Number=Plur\|POS=ADV\|Voice=Act`, `POS=CCONJ\|PronType=Int`, `Degree=Pos\|Number=Sing\|POS=PART`, `Number[psor]=Sing\|POS=PRON\|Person[psor]=2`, `Number=Plur\|POS=VERB`, `Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3\|Voice=Pass`, `Degree=Pos\|Number=Sing\|POS=PUNCT`, `Number[psor]=Sing\|POS=ADP\|Person[psor]=1`, `Degree=Sup\|Number=Sing\|POS=NOUN`, `Number[psor]=Sing\|POS=PART\|Person[psor]=3\|Polarity=Neg`, `Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person[psor]=3\|Voice=Act`, `POS=NOUN\|Polarity=Neg`, `Number[psor]=Sing\|POS=PROPN\|Person[psor]=2`, `Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2\|Voice=Act` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `case`, `cc`, `ccomp`, `compound`, `compound:plur`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `6`, `8`, `10`, `12`, `15`, `19`, `22`, `24`, `26`, `27`, `29`, `30`, `31`, `34`, `36`, `37`, `40`, `42`, `44`, `46`, `48`, `50`, `51`, `53`, `54`, `56`, `58`, `47`, `59`, `62`, `64`, `66`, `68`, `70`, `71`, `3`, `72`, `74`, `75`, `77`, `78`, `79`, `81`, `84`, `86`, `87`, `88`, `89`, `92`, `11`, `93`, `95`, `96`, `97`, `98`, `99`, `101`, `103`, `105`, `106`, `107`, `108`, `110`, `111`, `113`, `115`, `116`, `118`, `120`, `122`, `123`, `124`, `125`, `126`, `127`, `128`, `130`, `131`, `132`, `134`, `135`, `137`, `140`, `142`, `143`, `144`, `146`, `147`, `148`, `149`, `150`, `151`, `152`, `153`, `43`, `155`, `157`, `158`, `160`, `161`, `162`, `163`, `164`, `165`, `166`, `167`, `168`, `170`, `171`, `172`, `174`, `175`, `177`, `178`, `179`, `180`, `181`, `182`, `183`, `25`, `184`, `185`, `186`, `187`, `188`, `190`, `192`, `193`, `194`, `196`, `57`, `197`, `198`, `199`, `201`, `203`, `204`, `206`, `207`, `208`, `209`, `210`, `211`, `212`, `213`, `214`, `215`, `217`, `218`, `219`, `220`, `221`, `223`, `225`, `227`, `228`, `230`, `232`, `234`, `236`, `237`, `238`, `240`, `242`, `243`, `244`, `246`, `247`, `248`, `249`, `250`, `251`, `252`, `253`, `254`, `256`, `257`, `258`, `260`, `261`, `262`, `263`, `264`, `266`, `267`, `268`, `269`, `270`, `272`, `41`, `273`, `274`, `275`, `276`, `277`, `278`, `280`, `281`, `282`, `283`, `284`, `285`, `286`, `287`, `288`, `289`, `290`, `291`, `292`, `293`, `294`, `295`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `304`, `306`, `307`, `308`, `309`, `310`, `312`, `313`, `314`, `317`, `315`, `318`, `320`, `321`, `322`, `323`, `324`, `9`, `325`, `326`, `327`, `329`, `330`, `331`, `332`, `333`, `334`, `336`, `337`, `339`, `341`, `342`, `343`, `345`, `346`, `347`, `348`, `80`, `241`, `349`, `350`, `351`, `353`, `354`, `355`, `356`, `357`, `358`, `359`, `360`, `361`, `363`, `49`, `364`, `365`, `366`, `23`, `367`, `368`, `369`, `370`, `371`, `372`, `373`, `374`, `375`, `376`, `378`, `379`, `380`, `381`, `382`, `383`, `385`, `386`, `387`, `388`, `389`, `390`, `391`, `393`, `394`, `45`, `35`, `395`, `396`, `63`, `397`, `398`, `399`, `400`, `401`, `402`, `403`, `404`, `405`, `406`, `407`, `408`, `409`, `410`, `412`, `413`, `415`, `416`, `417`, `419`, `421`, `422`, `173`, `28`, `424`, `425`, `426`, `427`, `428`, `429`, `430`, `431`, `432`, `434`, `435`, `437`, `439`, `440`, `441`, `442`, `443`, `444`, `445`, `446`, `447`, `448`, `450`, `451`, `453`, `454`, `455`, `457`, `459`, `461`, `463`, `464`, `465`, `466`, `467`, `469`, `470`, `0`, `471`, `472`, `473`, `474`, `475`, `477`, `478`, `479`, `480`, `481`, `482`, `483`, `484`, `485`, `486`, `487`, `489`, `490`, `491`, `493`, `495`, `496`, `497`, `498`, `499`, `500`, `501`, `502`, `503`, `504`, `52`, `506`, `507`, `508`, `509`, `510`, `511`, `512`, `514`, `515`, `516`, `519`, `520`, `67`, `522`, `523`, `525`, `526`, `527`, `528`, `529`, `530`, `531`, `533`, `534`, `535`, `536`, `537`, `538`, `539`, `540`, `541`, `542`, `543`, `544`, `545`, `546`, `548`, `549`, `551`, `553`, `554`, `555`, `556`, `557`, `559`, `560`, `561`, `562`, `563`, `564`, `565`, `566`, `568`, `569`, `570`, `571`, `572`, `573`, `575`, `576`, `577`, `578`, `579`, `513`, `580`, `582`, `583`, `584`, `586`, `587`, `588`, `589`, `591`, `592`, `593`, `594`, `595`, `597`, `599`, `600`, `602`, `607`, `608`, `609`, `610`, `611`, `612`, `613`, `614`, `615`, `616`, `617`, `618`, `619`, `620`, `621`, `623`, `625`, `626`, `627`, `628`, `629`, `630`, `631`, `632`, `633`, `634`, `635`, `636`, `637`, `638`, `639`, `640`, `641`, `642`, `644`, `645`, `646`, `647`, `648`, `649`, `651`, `652`, `653`, `655`, `656`, `657`, `658`, `659`, `660`, `661`, `662`, `664`, `665`, `666`, `667`, `668`, `669`, `670`, `672`, `674`, `675`, `676`, `677`, `169`, `678`, `679`, `680`, `681`, `682`, `683`, `684`, `685`, `686`, `687`, `688`, `689`, `690`, `7`, `691`, `692`, `693`, `694`, `695`, `696`, `697`, `698`, `699`, `701`, `702`, `703`, `704`, `705`, `706`, `708`, `709`, `710`, `711`, `712`, `713`, `715`, `717`, `719`, `720`, `721`, `722`, `723`, `724`, `725`, `726`, `727`, `728`, `729`, `730`, `731`, `732`, `733`, `735`, `736`, `737`, `738`, `740`, `741`, `742`, `743`, `744`, `745`, `746`, `747`, `748`, `749`, `750`, `752`, `753`, `754`, `755`, `756`, `757`, `758`, `760`, `761`, `763`, `764`, `765`, `766`, `767`, `768`, `769`, `770`, `771`, `772`, `773`, `774`, `775`, `776`, `65`, `777`, `778`, `779`, `780`, `781`, `782`, `783`, `784`, `785`, `786`, `788`, `790`, `791`, `792`, `793`, `794`, `795`, `796`, `797`, `798`, `799`, `145`, `800`, `801`, `802`, `803`, `804`, `805`, `806`, `807`, `808`, `809`, `810`, `811`, `812`, `813`, `815`, `817`, `818`, `819`, `820`, `821`, `822`, `823`, `824`, `826`, `829`, `830`, `831`, `832`, `833`, `834`, `835`, `836`, `837`, `838`, `839`, `840`, `841`, `843`, `845`, `847`, `849`, `850`, `851`, `852`, `853`, `854`, `855`, `856`, `857`, `858`, `5`, `859`, `860`, `861`, `862`, `863`, `864`, `865`, `866`, `867`, `868`, `869`, `871`, `872`, `873`, `874`, `875`, `876`, `877`, `878`, `879`, `880`, `881`, `882`, `884`, `885`, `887`, `888`, `889`, `891`, `892`, `893`, `894`, `896`, `897`, `898`, `899`, `900`, `901`, `902`, `903`, `904`, `905`, `906`, `907`, `908`, `69`, `909`, `910`, `912`, `913`, `914`, `915`, `916`, `917`, `919`, `920`, `921`, `922`, `923`, `924`, `925`, `926`, `927`, `929`, `229`, `930`, `931`, `932`, `933`, `934`, `935`, `936`, `937`, `938`, `939`, `940`, `941`, `942`, `944`, `945`, `946`, `947`, `948`, `949`, `950`, `951`, `953`, `954`, `955`, `956`, `957`, `958`, `959`, `960`, `962`, `963`, `964`, `965`, `967`, `968`, `969`, `970`, `971`, `972`, `973`, `974`, `976`, `977`, `978`, `979`, `980`, `981`, `982`, `983`, `984`, `986`, `987`, `988`, `990`, `993`, `994`, `995`, `996`, `997`, `998`, `999`, `1000`, `1001`, `1002`, `1003`, `1004`, `1005`, `1006`, `1007`, `1008`, `1009`, `1012`, `1014`, `1015`, `1016`, `1019`, `1020`, `1021`, `1022`, `1023`, `1024`, `1025`, `1026`, `1027`, `1028`, `1029`, `1030`, `1031`, `1032`, `1033`, `1034`, `1035`, `1036`, `1037`, `1038`, `1039`, `1040`, `1041`, `1042`, `1043`, `1044`, `1045`, `1046`, `1047`, `1048`, `1049`, `1051`, `1052`, `1053`, `1054`, `1055`, `1056`, `1057`, `1058`, `1059`, `1060`, `1062`, `1063`, `1064`, `1065`, `1066`, `1068`, `1069`, `1070`, `1072`, `1074`, `1075`, `1076`, `1077`, `1078`, `1079`, `1080`, `1082`, `1083`, `1085`, `1086`, `1087`, `1088`, `1090`, `1091`, `1092`, `1093`, `1094`, `1095`, `1096`, `1097`, `1098`, `1099`, `1100`, `1101`, `673`, `1102`, `1103`, `1104`, `1106`, `1108`, `1109`, `1110`, `1111`, `1115`, `1116`, `1119`, `1120`, `1089`, `418`, `1121`, `1122`, `1123`, `1124`, `1125`, `1126`, `1127`, `1128`, `1129`, `1130`, `1131`, `1132`, `1134`, `1136`, `1137`, `1138`, `1139`, `1140`, `1141`, `1133`, `1142`, `1143`, `1144`, `1145`, `1146`, `1147`, `1148`, `1149`, `1150`, `1151`, `1153`, `1154`, `1156`, `1157`, `1158`, `1159`, `1160`, `1162`, `1164`, `1165`, `377`, `1166`, `1167`, `1168`, `1169`, `1170`, `1171`, `1172`, `1173`, `1174`, `1175`, `1176`, `1177`, `1179`, `1180`, `1181`, `1182`, `191`, `1183`, `1184`, `1185`, `1186`, `1187`, `1188`, `1190`, `1191`, `1192`, `1194`, `1195`, `1196`, `1197`, `1198`, `1199`, `1200`, `1201` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.99 |
| `TOKEN_P` | 99.98 |
| `TOKEN_R` | 99.99 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 92.98 |
| `SENTS_P` | 92.40 |
| `SENTS_R` | 93.56 |
| `TAG_ACC` | 94.79 |
| `POS_ACC` | 93.17 |
| `MORPH_ACC` | 95.90 |
| `DEP_UAS` | 86.16 |
| `DEP_LAS` | 78.38 |
| `LEMMA_ACC` | 98.05 |
|
{"language": ["id"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/id_udv25_indonesiangsd_trf
|
[
"spacy",
"token-classification",
"id",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#spacy #token-classification #id #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Indonesian-GSD
### Label Scheme
View label scheme (1325 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1325 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #id #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1325 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #id #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1325 labels for 6 components)### Accuracy"
] |
[
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0.04791069030761719,
0.10167442262172699,
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0.2502739131450653,
0.06427426636219025,
0.2308143973350525,
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0.0405033640563488,
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0.09719479084014893,
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0.05622487887740135,
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0.16925078630447388,
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0.057322435081005096,
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0.026614360511302948,
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0.15930981934070587,
0.11155027151107788,
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Korean-GSD
| Feature | Description |
| --- | --- |
| **Name** | `ko_udv25_koreangsd_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (2415 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `+SW`, `??`, `EC`, `EC+JX`, `ETM`, `IC`, `IC+VCP+ETM`, `IC+VV+EC`, `JC`, `JC+SN`, `JKB`, `JKB+JKG`, `JKB+JX`, `JKC`, `JKG`, `JKO`, `JKQ`, `JKQ+JX`, `JKS`, `JX`, `JX+JKO`, `JX+JX`, `JX+SN+NNB`, `MAG`, `MAG+JKB`, `MAG+JKB+JX`, `MAG+JKS`, `MAG+JX`, `MAG+JX+JX`, `MAG+MM`, `MAG+NNG`, `MAG+NNG+JKB`, `MAG+VA+EF`, `MAG+VCP+EC`, `MAG+VCP+EF`, `MAG+VCP+EP+EC`, `MAG+VCP+ETM`, `MAG+VCP+ETN`, `MAG+VV+EC`, `MAG+VV+EC+NNP+NNP`, `MAG+VV+EC+VX+EP+EC`, `MAG+VV+EF`, `MAG+VV+EP+EC`, `MAG+VV+EP+EF`, `MAG+VV+EP+ETM`, `MAG+VV+ETM`, `MAG+VV+ETN`, `MAG+XSA+EC`, `MAG+XSA+ETM`, `MAG+XSN`, `MAG+XSV+EC`, `MAG+XSV+EC+JKS`, `MAG+XSV+EC+JX`, `MAG+XSV+EC+VX+EC`, `MAG+XSV+EC+VX+EP+EC`, `MAG+XSV+EC+VX+EP+EF`, `MAG+XSV+EC+VX+EP+EP+EC`, `MAG+XSV+EF`, `MAG+XSV+EP+EC`, `MAG+XSV+EP+EF`, `MAG+XSV+EP+ETM`, `MAG+XSV+ETM`, `MAJ`, `MM`, `MM+NNB`, `MM+NNB+JKB`, `MM+NNB+JKG`, `MM+NNB+JX`, `MM+NNB+NNB+JKG`, `MM+NNB+VCP+EC`, `MM+NNB+VCP+ETM`, `MM+NNB+XSN`, `MM+NNB+XSN+JKB`, `MM+NNG`, `MM+NNG+JC`, `MM+NNG+JKB`, `MM+NNG+JKG`, `MM+NNG+JKO`, `MM+NNG+JKS`, `MM+NNG+JX`, `MM+NNG+NNG+JKG`, `MM+NNG+VCP+ETM`, `MM+NNG+XSN+VCP+ETM`, `MM+NNP`, `MM+NNP+JKS`, `MM+NNP+JX+JX`, `MM+SN+NR+NNB+XSN`, `NA`, `NNB`, `NNB+JC`, `NNB+JKB`, `NNB+JKB+JX`, `NNB+JKC`, `NNB+JKG`, `NNB+JKO`, `NNB+JKS`, `NNB+JX`, `NNB+JX+JKB`, `NNB+JX+JKO`, `NNB+JX+JKS`, `NNB+JX+VV+EF`, `NNB+NNB`, `NNB+NNB+JKG`, `NNB+NNB+JX`, `NNB+NNB+NNG+JKG`, `NNB+NNG`, `NNB+NNG+JKB`, `NNB+NNG+JKO`, `NNB+NNG+JX`, `NNB+NNG+XSN`, `NNB+NNP+JKB+JX`, `NNB+NNP+JKB+VCP+EF`, `NNB+NNP+JKG`, `NNB+VCP+EC`, `NNB+VCP+EC+JX`, `NNB+VCP+EF`, `NNB+VCP+EP+EF`, `NNB+VCP+EP+ETM`, `NNB+VCP+EP+ETN`, `NNB+VCP+ETM`, `NNB+VCP+ETM+NNG`, `NNB+VCP+ETM+NNG+JKB`, `NNB+VCP+ETN`, `NNB+XSA+EC`, `NNB+XSA+EP+EC`, `NNB+XSA+EP+EF`, `NNB+XSA+ETM`, `NNB+XSA+ETN`, `NNB+XSN`, `NNB+XSN+JC`, `NNB+XSN+JKB`, `NNB+XSN+JKG`, `NNB+XSN+JKS`, `NNB+XSN+JX`, `NNB+XSN+VCP+EF`, `NNG`, `NNG+EC`, `NNG+EC+EF`, `NNG+EF`, `NNG+JC`, `NNG+JKB`, `NNG+JKB+JC`, `NNG+JKB+JKB`, `NNG+JKB+JKG`, `NNG+JKB+JX`, `NNG+JKB+NNG+NNG+NNG+XSN+SL`, `NNG+JKB+VCP+EC`, `NNG+JKB+VCP+ETM`, `NNG+JKC`, `NNG+JKG`, `NNG+JKO`, `NNG+JKO+VV+EC`, `NNG+JKS`, `NNG+JKS+JX`, `NNG+JKS+VA+EC`, `NNG+JX`, `NNG+JX+JKB`, `NNG+JX+JKG`, `NNG+JX+JKO`, `NNG+JX+JKS`, `NNG+JX+JX`, `NNG+JX+VCP+EC`, `NNG+JX+VCP+EP+EF`, `NNG+JX+VV+EC`, `NNG+JX+VV+ETM`, `NNG+MAG`, `NNG+NA`, `NNG+NNB`, `NNG+NNB+JKB`, `NNG+NNB+JKB+JX`, `NNG+NNB+JKG`, `NNG+NNB+JKO`, `NNG+NNB+JKS`, `NNG+NNB+JX`, `NNG+NNB+NNG`, `NNG+NNB+NNP+JKB`, `NNG+NNB+VCP+EC`, `NNG+NNB+VCP+EF`, `NNG+NNB+VCP+ETM`, `NNG+NNB+VCP+ETM+NNG+JKG`, `NNG+NNG`, `NNG+NNG+ETN+JKB+JX`, `NNG+NNG+JC`, `NNG+NNG+JKB`, `NNG+NNG+JKB+JKG`, `NNG+NNG+JKB+JX`, `NNG+NNG+JKC`, `NNG+NNG+JKG`, `NNG+NNG+JKO`, `NNG+NNG+JKS`, `NNG+NNG+JX`, `NNG+NNG+JX+JKS`, `NNG+NNG+JX+JX`, `NNG+NNG+JX+JX+VV+EC`, `NNG+NNG+JX+NNG`, `NNG+NNG+MAG`, `NNG+NNG+NNB`, `NNG+NNG+NNB+JKB`, `NNG+NNG+NNB+JKO`, `NNG+NNG+NNB+JKS`, `NNG+NNG+NNB+JX`, `NNG+NNG+NNB+VCP+EC`, `NNG+NNG+NNB+VCP+ETM`, `NNG+NNG+NNG`, `NNG+NNG+NNG+JC`, `NNG+NNG+NNG+JKB`, `NNG+NNG+NNG+JKB+JKG`, `NNG+NNG+NNG+JKB+JX`, `NNG+NNG+NNG+JKG`, `NNG+NNG+NNG+JKO`, `NNG+NNG+NNG+JKS`, `NNG+NNG+NNG+JX`, `NNG+NNG+NNG+NNG`, `NNG+NNG+NNG+NNG+JC`, `NNG+NNG+NNG+NNG+JKB`, `NNG+NNG+NNG+NNG+JKO`, `NNG+NNG+NNG+NNG+JKS`, `NNG+NNG+NNG+NNG+JX`, `NNG+NNG+NNG+NNG+NNG`, `NNG+NNG+NNG+NNG+NNG+JKO`, `NNG+NNG+NNG+NNG+NNG+JKS`, `NNG+NNG+NNG+NNG+NNG+JX`, `NNG+NNG+NNG+NNG+VCP+EF`, `NNG+NNG+NNG+NNG+VCP+ETM`, `NNG+NNG+NNG+VCP+EC`, `NNG+NNG+NNG+VCP+EF`, `NNG+NNG+NNG+VCP+ETM`, `NNG+NNG+NNG+XSN`, `NNG+NNG+NNG+XSN+JKB`, `NNG+NNG+NNG+XSN+NNG+VCP+EC`, `NNG+NNG+NNP`, `NNG+NNG+SN+NNB+JX`, `NNG+NNG+SN+NNG`, `NNG+NNG+VCP+EC`, `NNG+NNG+VCP+EC+JX`, `NNG+NNG+VCP+EF`, `NNG+NNG+VCP+EP+EF`, `NNG+NNG+VCP+EP+ETM`, `NNG+NNG+VCP+ETM`, `NNG+NNG+VCP+ETN`, `NNG+NNG+VCP+ETN+JKB`, `NNG+NNG+VCP+ETN+JX`, `NNG+NNG+VV+ETN`, `NNG+NNG+XSN`, `NNG+NNG+XSN+JC`, `NNG+NNG+XSN+JKB`, `NNG+NNG+XSN+JKB+JX`, `NNG+NNG+XSN+JKG`, `NNG+NNG+XSN+JKO`, `NNG+NNG+XSN+JKS`, `NNG+NNG+XSN+JX`, `NNG+NNG+XSN+NNG+JX`, `NNG+NNG+XSN+VCP+EF`, `NNG+NNG+XSV+EC`, `NNG+NNG+XSV+EF`, `NNG+NNG+XSV+EP+EC`, `NNG+NNG+XSV+EP+EF`, `NNG+NNG+XSV+ETM`, `NNG+NNG+XSV+ETN`, `NNG+NNG+XSV+ETN+JKO`, `NNG+NNP`, `NNG+NNP+JKB`, `NNG+NNP+JKS`, `NNG+NNP+JX`, `NNG+NNP+JX+JKG`, `NNG+NNP+NNG`, `NNG+NNP+NNG+JKB`, `NNG+NNP+NNG+NNG`, `NNG+NNP+NNG+NNG+NNG+JKB+JX`, `NNG+NNP+NNP`, `NNG+SL`, `NNG+SL+JKS`, `NNG+SL+JX`, `NNG+SN`, `NNG+SN+JKB+JX`, `NNG+SN+JKG`, `NNG+SN+JKO`, `NNG+SN+NNB`, `NNG+SN+NNB+JKB`, `NNG+SN+NNG`, `NNG+SN+NNG+JX`, `NNG+SN+NNG+NNG+JKG`, `NNG+SN+SL+JX`, `NNG+VA+EC`, `NNG+VA+EF`, `NNG+VA+ETM`, `NNG+VA+ETN`, `NNG+VCN+EP+EC`, `NNG+VCP+EC`, `NNG+VCP+EC+JKO`, `NNG+VCP+EC+JKS`, `NNG+VCP+EC+JX`, `NNG+VCP+EF`, `NNG+VCP+EP+EC`, `NNG+VCP+EP+EC+JX`, `NNG+VCP+EP+EF`, `NNG+VCP+EP+ETM`, `NNG+VCP+EP+ETN`, `NNG+VCP+ETM`, `NNG+VCP+ETM+NNB`, `NNG+VCP+ETN`, `NNG+VCP+ETN+JKB`, `NNG+VCP+ETN+JKB+JX`, `NNG+VCP+ETN+JKO`, `NNG+VCP+ETN+JKS`, `NNG+VCP+ETN+JX`, `NNG+VV`, `NNG+VV+EC`, `NNG+VV+EC+VCP+EC`, `NNG+VV+EC+VX+EC`, `NNG+VV+EC+VX+ETM`, `NNG+VV+EF`, `NNG+VV+EP+EC`, `NNG+VV+EP+EF`, `NNG+VV+EP+ETM`, `NNG+VV+ETM`, `NNG+VV+ETN`, `NNG+VV+ETN+JKS`, `NNG+VV+ETN+NNG`, `NNG+XPN+NNG`, `NNG+XPN+NNG+JKO`, `NNG+XPN+NNP+JKG`, `NNG+XSA+EC`, `NNG+XSA+EC+VX+EC`, `NNG+XSA+EF`, `NNG+XSA+EP+EF`, `NNG+XSA+ETM`, `NNG+XSA+ETN`, `NNG+XSA+ETN+JC`, `NNG+XSA+ETN+JKO`, `NNG+XSA+ETN+JKS`, `NNG+XSN`, `NNG+XSN+JC`, `NNG+XSN+JKB`, `NNG+XSN+JKB+JKB`, `NNG+XSN+JKB+JKG`, `NNG+XSN+JKB+JX`, `NNG+XSN+JKG`, `NNG+XSN+JKO`, `NNG+XSN+JKS`, `NNG+XSN+JKS+JX`, `NNG+XSN+JX`, `NNG+XSN+JX+JKO`, `NNG+XSN+MAG`, `NNG+XSN+NNG`, `NNG+XSN+NNG+JKG`, `NNG+XSN+NNG+JKO`, `NNG+XSN+NNG+JX`, `NNG+XSN+NNG+NNG+JC`, `NNG+XSN+VCP+EC`, `NNG+XSN+VCP+EF`, `NNG+XSN+VCP+EP+EC`, `NNG+XSN+VCP+EP+ETM`, `NNG+XSN+VCP+EP+ETN`, `NNG+XSN+VCP+ETM`, `NNG+XSN+XSN`, `NNG+XSN+XSN+JC`, `NNG+XSN+XSN+JKB`, `NNG+XSN+XSN+JKG`, `NNG+XSN+XSN+JKO`, `NNG+XSN+XSN+JKS`, `NNG+XSN+XSN+JX`, `NNG+XSN+XSN+VCP+EC`, `NNG+XSN+XSV+EC`, `NNG+XSN+XSV+EF`, `NNG+XSN+XSV+EP+EC`, `NNG+XSN+XSV+EP+EF`, `NNG+XSN+XSV+ETM`, `NNG+XSN+XSV+ETN`, `NNG+XSV+EC`, `NNG+XSV+EC+JKO`, `NNG+XSV+EC+JX`, `NNG+XSV+EC+NP+JKB`, `NNG+XSV+EC+VX+EC`, `NNG+XSV+EC+VX+EF`, `NNG+XSV+EC+VX+EP+EC`, `NNG+XSV+EC+VX+EP+EF`, `NNG+XSV+EC+VX+EP+ETM`, `NNG+XSV+EC+VX+ETM`, `NNG+XSV+EC+VX+ETN`, `NNG+XSV+EC+VX+ETN+JKO`, `NNG+XSV+EF`, `NNG+XSV+EP+EC`, `NNG+XSV+EP+EC+JKB`, `NNG+XSV+EP+EC+JX`, `NNG+XSV+EP+EF`, `NNG+XSV+EP+EP+EC`, `NNG+XSV+EP+EP+ETM`, `NNG+XSV+EP+ETM`, `NNG+XSV+EP+ETN`, `NNG+XSV+EP+ETN+JKO`, `NNG+XSV+ETM`, `NNG+XSV+ETM+NNB`, `NNG+XSV+ETM+NNB+XSA+ETM`, `NNG+XSV+ETM+NNG`, `NNG+XSV+ETM+NNG+JX`, `NNG+XSV+ETN`, `NNG+XSV+ETN+JC`, `NNG+XSV+ETN+JKB`, `NNG+XSV+ETN+JKB+JX`, `NNG+XSV+ETN+JKO`, `NNG+XSV+ETN+JKS`, `NNG+XSV+ETN+JX`, `NNP`, `NNP+JC`, `NNP+JKB`, `NNP+JKB+JKG`, `NNP+JKB+JKO`, `NNP+JKB+JX`, `NNP+JKC`, `NNP+JKG`, `NNP+JKG+NNG`, `NNP+JKO`, `NNP+JKS`, `NNP+JX`, `NNP+JX+JKG`, `NNP+NNB`, `NNP+NNB+JC`, `NNP+NNB+JKB`, `NNP+NNB+JKB+JX`, `NNP+NNB+JKG`, `NNP+NNB+JKO`, `NNP+NNB+JKS`, `NNP+NNB+JX`, `NNP+NNB+NNG+NNG+JKB+JX`, `NNP+NNB+XSN`, `NNP+NNB+XSN+JKO`, `NNP+NNG`, `NNP+NNG+JC`, `NNP+NNG+JKB`, `NNP+NNG+JKB+JKG`, `NNP+NNG+JKB+JX`, `NNP+NNG+JKG`, `NNP+NNG+JKO`, `NNP+NNG+JKS`, `NNP+NNG+JX`, `NNP+NNG+JX+JKB`, `NNP+NNG+JX+JX`, `NNP+NNG+NNB`, `NNP+NNG+NNB+JKS`, `NNP+NNG+NNB+NNP+NNG+JKB`, `NNP+NNG+NNG`, `NNP+NNG+NNG+JC`, `NNP+NNG+NNG+JKB`, `NNP+NNG+NNG+JKB+JKG`, `NNP+NNG+NNG+JKB+JX`, `NNP+NNG+NNG+JKG`, `NNP+NNG+NNG+JKO`, `NNP+NNG+NNG+JKS`, `NNP+NNG+NNG+JX`, `NNP+NNG+NNG+MM`, `NNP+NNG+NNG+NNG`, `NNP+NNG+NNG+NNG+JC`, `NNP+NNG+NNG+NNG+JKG`, `NNP+NNG+NNG+NNG+JKO`, `NNP+NNG+NNG+NNG+JKS`, `NNP+NNG+NNG+NNG+NNG`, `NNP+NNG+NNG+NNP`, `NNP+NNG+VCP+EF`, `NNP+NNG+VCP+ETM`, `NNP+NNG+VV+ETN`, `NNP+NNG+XSN`, `NNP+NNG+XSN+JKB`, `NNP+NNG+XSN+JKG`, `NNP+NNG+XSN+JKO`, `NNP+NNG+XSN+JKS`, `NNP+NNG+XSV+ETN+JKS`, `NNP+NNP`, `NNP+NNP+JKB`, `NNP+NNP+JKG`, `NNP+NNP+JKS`, `NNP+NNP+NNB`, `NNP+NNP+NNG`, `NNP+NNP+NNG+NNG+NNG`, `NNP+NNP+NNG+NNP+NNB+JKO`, `NNP+NP`, `NNP+NP+JC`, `NNP+NP+NNB+JKS`, `NNP+SL`, `NNP+SL+JKB`, `NNP+SL+JKO`, `NNP+SL+JKS`, `NNP+SL+JX`, `NNP+SL+NNG+JKB`, `NNP+SN`, `NNP+SN+NNG`, `NNP+VA+ETM`, `NNP+VCP+EC`, `NNP+VCP+EF`, `NNP+VCP+EP+EC`, `NNP+VCP+ETM`, `NNP+VV+ETM`, `NNP+VV+NNP+NNG+NNG+JKG`, `NNP+XSN`, `NNP+XSN+JKB`, `NNP+XSN+JKB+JX`, `NNP+XSN+JKG`, `NNP+XSN+JKO`, `NNP+XSN+JKS`, `NNP+XSN+VCP+EC`, `NNP+XSN+VCP+EF`, `NNP+XSN+XSN+JX`, `NP`, `NP+EF`, `NP+JKB`, `NP+JKB+JX`, `NP+JKB+VCP+EC`, `NP+JKG`, `NP+JKO`, `NP+JKS`, `NP+JX`, `NP+JX+JKG`, `NP+JX+JX`, `NP+JX+VV+ETM`, `NP+NNB`, `NP+NNB+JKG`, `NP+NNB+JKO`, `NP+NNG`, `NP+NNG+JKB`, `NP+NNG+JKG`, `NP+NNG+XSN+JKG`, `NP+NP`, `NP+VA+EC+JX`, `NP+VA+ETM`, `NP+VCP+EC`, `NP+VCP+EC+JKB`, `NP+VCP+EF`, `NP+VCP+EP+EC`, `NP+VCP+ETN`, `NP+VV+EC`, `NP+XSN`, `NP+XSN+JKB`, `NP+XSN+JKC`, `NP+XSN+JKG`, `NP+XSN+JKO`, `NP+XSN+JKS`, `NP+XSN+JX`, `NP+XSN+XSN`, `NP+XSV+EC`, `NR`, `NR+JC`, `NR+JKB`, `NR+JKG`, `NR+JKO`, `NR+JKS`, `NR+JX`, `NR+JX+JKO`, `NR+NNB`, `NR+NNB+JKB`, `NR+NNB+JKB+JX`, `NR+NNB+JKG`, `NR+NNB+JKO`, `NR+NNB+JKS`, `NR+NNB+JX`, `NR+NNB+VCP+EP+EC`, `NR+NNG`, `NR+NNG+JKB`, `NR+NNG+JKB+JX`, `NR+NNG+JKG`, `NR+NR+JC`, `NR+NR+NNG+JKO`, `NR+SN+NNB`, `NR+SN+NNB+VCP+EP+EF`, `NR+VCP+EC`, `NR+VCP+EF`, `NR+VCP+EP+EC`, `NR+VCP+EP+EF`, `NR+VCP+ETM`, `NR+XSN`, `NR+XSN+JX`, `SE`, `SF`, `SH`, `SH+SL+SH`, `SL`, `SL+JC`, `SL+JKB`, `SL+JKB+JKG`, `SL+JKB+JX`, `SL+JKG`, `SL+JKO`, `SL+JKS`, `SL+JKS+JX`, `SL+JX`, `SL+MM+NNB`, `SL+NNB`, `SL+NNB+JKB`, `SL+NNB+JKG`, `SL+NNB+JKS`, `SL+NNB+JX`, `SL+NNG`, `SL+NNG+JC`, `SL+NNG+JKB`, `SL+NNG+JKG`, `SL+NNG+JKO`, `SL+NNG+JKS`, `SL+NNG+JX`, `SL+NNG+NNB+JKB`, `SL+NNG+NNG`, `SL+NNG+NNG+JKB`, `SL+NNG+VCP+EF`, `SL+NNG+XSN+JKS`, `SL+NNP`, `SL+NNP+NNG`, `SL+NNP+NNG+JKG`, `SL+NNP+NNP+JKB`, `SL+SF+SL+JKB`, `SL+SL`, `SL+SL+JC`, `SL+SL+JKG`, `SL+SL+SL`, `SL+SL+SL+JKG`, `SL+SL+VCP+ETM`, `SL+SN`, `SL+SN+JC`, `SL+SN+JKB`, `SL+SN+JKO`, `SL+SN+JX`, `SL+SN+NNG`, `SL+SN+SL`, `SL+SN+SN+SL`, `SL+SN+VCP+EC`, `SL+VCP+ETM`, `SL+VV+ETM`, `SL+XSA+EC`, `SL+XSN`, `SL+XSN+JKG`, `SN`, `SN+JC`, `SN+JKB`, `SN+JKG`, `SN+JKO`, `SN+JKS`, `SN+JX`, `SN+NNB`, `SN+NNB+JC`, `SN+NNB+JKB`, `SN+NNB+JKB+JX`, `SN+NNB+JKG`, `SN+NNB+JKO`, `SN+NNB+JKS`, `SN+NNB+JX`, `SN+NNB+JX+JKB`, `SN+NNB+JX+JKO`, `SN+NNB+JX+JX`, `SN+NNB+NNB`, `SN+NNB+NNB+JKB`, `SN+NNB+NNB+JKG`, `SN+NNB+NNG`, `SN+NNB+NNG+JC`, `SN+NNB+NNG+JKB`, `SN+NNB+NNG+JKO`, `SN+NNB+NNG+JKS`, `SN+NNB+NNG+JX`, `SN+NNB+NNG+VCP+EF`, `SN+NNB+SN+JKB`, `SN+NNB+SN+NNB`, `SN+NNB+SN+NNB+JKB`, `SN+NNB+SN+NNB+JKO`, `SN+NNB+SN+NNB+JX`, `SN+NNB+SN+NNB+SN+NNB+SN+NNB`, `SN+NNB+SN+NNB+VCP+EF`, `SN+NNB+SN+NNG+SN+NNG+JKG`, `SN+NNB+SN+NR`, `SN+NNB+VCP+EC`, `SN+NNB+VCP+EF`, `SN+NNB+VCP+EP+EC`, `SN+NNB+VCP+EP+EF`, `SN+NNB+VCP+EP+ETM`, `SN+NNB+VCP+ETM`, `SN+NNB+VCP+ETN+JKB`, `SN+NNB+XSN`, `SN+NNB+XSN+JKB`, `SN+NNB+XSN+JKO`, `SN+NNB+XSN+JKS`, `SN+NNB+XSN+JX+JX`, `SN+NNB+XSN+VCP+EF`, `SN+NNG`, `SN+NNG+JC`, `SN+NNG+JKB`, `SN+NNG+JKB+JX`, `SN+NNG+JKG`, `SN+NNG+JKO`, `SN+NNG+JKS`, `SN+NNG+JX`, `SN+NNG+NNG`, `SN+NNG+NNG+JKB`, `SN+NNG+NNG+JKO`, `SN+NNG+NNG+VCP+ETM`, `SN+NNG+SN+NNG`, `SN+NNG+SN+NNG+JKB`, `SN+NNG+SN+NNG+JKG`, `SN+NNG+VCP+EC`, `SN+NNG+VCP+EF`, `SN+NNG+VCP+ETM`, `SN+NNG+XSN`, `SN+NNG+XSN+JKB`, `SN+NNP+NNB+SN+NNB`, `SN+NR`, `SN+NR+JKB`, `SN+NR+JKS`, `SN+NR+JX`, `SN+NR+NNB`, `SN+NR+NNB+JKB`, `SN+NR+NNB+JKG`, `SN+NR+NNB+JKO`, `SN+NR+NNB+JKS`, `SN+NR+NNB+JX`, `SN+NR+NNB+SN+NR+NNB`, `SN+NR+NNB+VCP+EC`, `SN+NR+NNB+VCP+EF`, `SN+NR+NNB+XSN`, `SN+NR+NNG+JKG`, `SN+NR+NNG+SN+NNB`, `SN+NR+NNG+SN+NNG+VCP+ETM`, `SN+NR+NNG+SN+NR+SN+NNB`, `SN+NR+SN`, `SN+NR+SN+NNB`, `SN+NR+SN+NNB+JKB`, `SN+NR+SN+NNB+JKS`, `SN+NR+SN+NNB+XSA+EC+VV`, `SN+NR+SN+NNG`, `SN+NR+SN+NNG+JKG`, `SN+NR+SN+NNP`, `SN+NR+SN+NR`, `SN+NR+SN+NR+NNB`, `SN+NR+SN+NR+NNB+JKO`, `SN+NR+SN+NR+NNB+JX`, `SN+NR+SN+NR+NNB+VCP+EF`, `SN+NR+SN+NR+SN+NNB+JKB`, `SN+NR+SN+NR+SN+NNB+JKG`, `SN+NR+SN+NR+SN+NNB+JKO`, `SN+NR+SN+NR+SN+NR`, `SN+NR+SN+NR+SN+NR+NNB`, `SN+NR+SN+NR+SN+NR+SN+NNB+JKB`, `SN+NR+SN+NR+SN+NR+SN+NR`, `SN+NR+SN+NR+VCP+EF`, `SN+NR+SN+NR+XSN`, `SN+NR+SN+SL+NNG`, `SN+NR+XSN`, `SN+NR+XSN+NNB+JKO`, `SN+NR+XSN+NNB+JKS`, `SN+SL`, `SN+SL+JKB`, `SN+SL+JKG`, `SN+SL+JKO`, `SN+SL+JKS`, `SN+SL+NNG`, `SN+SL+NNG+JKO`, `SN+SL+NNG+JX`, `SN+SL+SN+JKS`, `SN+SL+VCP+EC`, `SN+SN`, `SN+SN+JKB`, `SN+SN+NNB`, `SN+SN+NNB+JKG`, `SN+SN+NNG`, `SN+SN+NNG+JX`, `SN+SN+SL`, `SN+SN+SL+JKB`, `SN+SN+SN`, `SN+SN+SN+SN`, `SN+VCP+ETM`, `SN+XSN`, `SN+XSN+JKB+JX`, `SN+XSN+JKG`, `SN+XSN+JKO`, `SN+XSN+NNB`, `SN+XSN+NNB+JKB`, `SN+XSN+NNB+JKG`, `SN+XSN+NNB+JKS`, `SN+XSN+NNB+NNB`, `SN+XSN+SL+JKG`, `SN+XSN+XSN+JX`, `SO`, `SP`, `SS`, `SW`, `VA`, `VA+EC`, `VA+EC+EC`, `VA+EC+JKO`, `VA+EC+JKS`, `VA+EC+JX`, `VA+EC+JX+JX`, `VA+EC+VCP+EC`, `VA+EC+VCP+EF`, `VA+EC+VV+ETM`, `VA+EC+VX+EC`, `VA+EC+VX+EF`, `VA+EC+VX+EP+EC`, `VA+EC+VX+EP+EF`, `VA+EC+VX+EP+ETM`, `VA+EC+VX+ETM`, `VA+EC+VX+ETN`, `VA+EF`, `VA+EF+ETM+NNG`, `VA+EP+EC`, `VA+EP+EF`, `VA+EP+ETM`, `VA+EP+ETN`, `VA+ETM`, `VA+ETM+EC`, `VA+ETM+NNB`, `VA+ETM+NNB+JKG`, `VA+ETM+NNB+XSA+ETM`, `VA+ETM+NNG`, `VA+ETM+NNG+JKG`, `VA+ETN`, `VA+ETN+JKB`, `VA+ETN+JKB+JX`, `VA+ETN+JKG`, `VA+ETN+JX`, `VCN+EC`, `VCN+EC+JX`, `VCN+EF`, `VCN+EP+EC`, `VCN+EP+ETM`, `VCN+ETM`, `VCN+ETN`, `VCP+EC`, `VCP+EC+SN`, `VCP+EC+VX+EC`, `VCP+EF`, `VCP+EP+EC`, `VCP+EP+EF`, `VCP+EP+ETM`, `VCP+ETM`, `VV`, `VV+EC`, `VV+EC+EC`, `VV+EC+EP+EC`, `VV+EC+EP+EF`, `VV+EC+ETN`, `VV+EC+JKB`, `VV+EC+JKG`, `VV+EC+JKG+NNG+JKO`, `VV+EC+JKO`, `VV+EC+JKS`, `VV+EC+JX`, `VV+EC+JX+JKG`, `VV+EC+JX+MM`, `VV+EC+JX+NNB+JKB`, `VV+EC+SH+JKB`, `VV+EC+VCP+EC`, `VV+EC+VCP+EF`, `VV+EC+VCP+EP`, `VV+EC+VCP+EP+EF`, `VV+EC+VCP+ETM`, `VV+EC+VV+EC`, `VV+EC+VV+EF`, `VV+EC+VV+EP+EC`, `VV+EC+VV+EP+EF`, `VV+EC+VV+ETM`, `VV+EC+VV+ETN`, `VV+EC+VV+ETN+NNB+JKB`, `VV+EC+VX+EC`, `VV+EC+VX+EC+JKG`, `VV+EC+VX+EC+VX+EF`, `VV+EC+VX+EF`, `VV+EC+VX+EP+EC`, `VV+EC+VX+EP+EF`, `VV+EC+VX+EP+EP+EC`, `VV+EC+VX+EP+EP+EF`, `VV+EC+VX+EP+ETM`, `VV+EC+VX+EP+ETN`, `VV+EC+VX+ETM`, `VV+EC+VX+ETM+NNB`, `VV+EC+VX+ETM+NNB+XSA+EC`, `VV+EC+VX+ETN`, `VV+EC+VX+ETN+EC`, `VV+EC+VX+ETN+JKB`, `VV+EC+VX+ETN+JKG`, `VV+EC+VX+ETN+JX`, `VV+EC+XSN`, `VV+EC+XSN+JKS`, `VV+EC+XSN+XSN+JKB`, `VV+EF`, `VV+EP+EC`, `VV+EP+EC+JX`, `VV+EP+EC+VCP+EC`, `VV+EP+EC+VX+EC`, `VV+EP+EC+VX+EP+EF`, `VV+EP+EF`, `VV+EP+EF+EC`, `VV+EP+EP+EC`, `VV+EP+EP+EF`, `VV+EP+EP+EP+EC`, `VV+EP+EP+ETN`, `VV+EP+ETM`, `VV+EP+ETN`, `VV+EP+ETN+JKO`, `VV+ETM`, `VV+ETM+NNB`, `VV+ETM+NNB+JKB`, `VV+ETM+NNB+JKG`, `VV+ETM+NNB+JKS`, `VV+ETM+NNB+JX`, `VV+ETM+NNB+NNG+JKB`, `VV+ETM+NNB+VCP+EC`, `VV+ETM+NNB+XSA+EC`, `VV+ETM+NNB+XSA+EP+EC`, `VV+ETM+NNB+XSA+ETM`, `VV+ETM+NNB+XSN+JKG`, `VV+ETM+NNG`, `VV+ETM+NNG+NNG+NNG`, `VV+ETM+NNG+NNG+NNG+NNG+NNG+NNG`, `VV+ETM+NNP`, `VV+ETM+NNP+JX+JKG`, `VV+ETM+VV+EC`, `VV+ETN`, `VV+ETN+JKB`, `VV+ETN+JKB+JX`, `VV+ETN+JKO`, `VV+ETN+JKS`, `VV+ETN+JX`, `VV+ETN+JX+JX`, `VV+ETN+MAG`, `VV+ETN+VA+ETM`, `VV+ETN+VCP+EF`, `VV+ETN+VCP+EP+EF`, `VV+NNG+JKG`, `VV+NNG+JKO`, `VV+NNP`, `VV+VV+EP+EC`, `VX+EC`, `VX+EC+JKB`, `VX+EC+JKO`, `VX+EC+JX`, `VX+EC+VX+EC`, `VX+EC+VX+EF`, `VX+EC+VX+EP+EC`, `VX+EC+VX+ETM`, `VX+EF`, `VX+EP+EC`, `VX+EP+EF`, `VX+EP+EF+ETM+NNG`, `VX+EP+EP+EC`, `VX+EP+ETM`, `VX+EP+ETN`, `VX+EP+ETN+JKO`, `VX+EP+ETN+NNB+JKB`, `VX+ETM`, `VX+ETN`, `VX+ETN+JKO`, `VX+ETN+JKS`, `VX+ETN+JX`, `XPN`, `XPN+NNC`, `XPN+NNG`, `XPN+NNG+JC`, `XPN+NNG+JKB`, `XPN+NNG+JKB+JX`, `XPN+NNG+JKG`, `XPN+NNG+JKO`, `XPN+NNG+JKS`, `XPN+NNG+JX`, `XPN+NNG+NNB+JX`, `XPN+NNG+NNG`, `XPN+NNG+NNG+JKB`, `XPN+NNG+NNG+JKG`, `XPN+NNG+NNG+JKO`, `XPN+NNG+NNG+JKS`, `XPN+NNG+NNG+JX`, `XPN+NNG+NNG+NNG`, `XPN+NNG+NNG+NNG+JX`, `XPN+NNG+VCP+EC`, `XPN+NNG+VCP+EF`, `XPN+NNG+VCP+EP+EF`, `XPN+NNG+VCP+ETM`, `XPN+NNG+XSA+EC`, `XPN+NNG+XSA+ETM`, `XPN+NNG+XSN`, `XPN+NNG+XSN+JKB`, `XPN+NNG+XSN+JKB+JX`, `XPN+NNG+XSN+JKG`, `XPN+NNG+XSN+JKO`, `XPN+NNG+XSN+VCP+ETM`, `XPN+NNG+XSN+XSN+VCP+ETM`, `XPN+NNG+XSV+EC`, `XPN+NNG+XSV+EC+JX`, `XPN+NNG+XSV+EF`, `XPN+NNG+XSV+EP+EC`, `XPN+NNG+XSV+EP+EF`, `XPN+NNG+XSV+ETM`, `XPN+NNG+XSV+ETN+JX+JKG`, `XPN+NNP`, `XPN+NNP+JC`, `XPN+NNP+JKG`, `XPN+NNP+JX`, `XPN+NNP+NNG+JKS`, `XPN+NNP+VCP+EC`, `XPN+NNP+XSN`, `XPN+NNP+XSN+JKB`, `XPN+SN`, `XPN+SN+JKG`, `XPN+SN+NNB`, `XPN+SN+NNB+JKO`, `XPN+SN+NNB+VCP+ETM`, `XPN+SN+NNG`, `XPN+SN+NNG+JC`, `XPN+SN+NNG+JKB`, `XPN+SN+NNG+JKB+JKG`, `XPN+SN+NNG+JKG`, `XPN+SN+NNG+JKO`, `XPN+SN+NNG+JKS`, `XPN+SN+NNG+JX`, `XPN+SN+NNG+NNG`, `XPN+SN+NNG+NNG+JKB+JKG`, `XPN+SN+NNG+NNG+JKO`, `XPN+SN+NNP+JX`, `XPN+VV+EP+EP+EC`, `XPN+XR+JX`, `XPN+XR+XSA+EC`, `XPN+XR+XSA+EF`, `XPN+XR+XSA+ETM`, `XPN+XR+XSN+JKO`, `XR`, `XR+JKB`, `XR+JKB+JKB`, `XR+JKB+JKO`, `XR+NNG`, `XR+NNG+JKB`, `XR+NNG+JKS`, `XR+NNG+JX`, `XR+NNG+NNG`, `XR+NNG+NNG+JX`, `XR+NNG+NNG+NNG`, `XR+NNG+NNG+NNG+JX`, `XR+NNG+VCP+ETM`, `XR+XSA+EC`, `XR+XSA+EC+JX`, `XR+XSA+EC+NNB+JX`, `XR+XSA+EC+VX+EC`, `XR+XSA+EC+VX+EF`, `XR+XSA+EC+VX+EP+EF`, `XR+XSA+EC+VX+ETM`, `XR+XSA+EF`, `XR+XSA+EP+EC`, `XR+XSA+EP+EC+JX`, `XR+XSA+EP+EF`, `XR+XSA+EP+ETM`, `XR+XSA+ETM`, `XR+XSA+ETN`, `XR+XSA+ETN+JC`, `XR+XSA+ETN+JKB`, `XR+XSA+ETN+JKO`, `XR+XSA+ETN+JKS`, `XR+XSA+ETN+JX`, `XR+XSN`, `XR+XSN+JC`, `XR+XSN+JKB`, `XR+XSN+JKO`, `XR+XSN+JKS`, `XR+XSN+JX`, `XR+XSN+VCP+EC`, `XR+XSN+VCP+ETM`, `XR+XSV+EC`, `XR+XSV+ETM`, `XSA+ETM`, `XSN`, `XSN+JKB`, `XSN+JKS`, `XSN+JX`, `XSN+NNB+JKS`, `XSV+EC`, `XSV+ETM` |
| **`morphologizer`** | `POS=NOUN`, `POS=ADV`, `POS=VERB`, `POS=PUNCT`, `POS=ADP`, `NumType=Card\|POS=NUM`, `POS=PRON`, `POS=DET`, `POS=ADJ`, `POS=NUM`, `POS=PROPN`, `POS=CCONJ`, `POS=X`, `POS=SYM`, `POS=AUX`, `POS=PART`, `POS=INTJ`, `NumType=Card\|POS=PUNCT`, `NumType=Card\|POS=DET` |
| **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:poss`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `punct` |
| **`experimental_edit_tree_lemmatizer`** | `2`, `3`, `5`, `6`, `8`, `11`, `15`, `17`, `19`, `20`, `23`, `24`, `27`, `29`, `31`, `33`, `34`, `36`, `39`, `42`, `45`, `47`, `49`, `50`, `52`, `53`, `55`, `57`, `59`, `61`, `64`, `66`, `69`, `71`, `74`, `77`, `79`, `80`, `81`, `83`, `85`, `88`, `90`, `91`, `94`, `96`, `98`, `100`, `104`, `105`, `108`, `111`, `113`, `116`, `118`, `119`, `120`, `122`, `124`, `127`, `131`, `132`, `134`, `136`, `138`, `139`, `142`, `145`, `147`, `149`, `151`, `155`, `157`, `163`, `166`, `169`, `171`, `174`, `177`, `180`, `183`, `68`, `184`, `185`, `187`, `188`, `190`, `193`, `195`, `199`, `201`, `202`, `204`, `206`, `209`, `212`, `215`, `217`, `67`, `220`, `223`, `225`, `228`, `230`, `232`, `234`, `214`, `235`, `237`, `238`, `241`, `243`, `244`, `247`, `250`, `253`, `256`, `258`, `259`, `261`, `263`, `266`, `269`, `271`, `78`, `274`, `277`, `280`, `224`, `282`, `283`, `285`, `286`, `289`, `291`, `295`, `296`, `299`, `302`, `303`, `306`, `308`, `311`, `314`, `317`, `288`, `318`, `320`, `323`, `326`, `328`, `329`, `222`, `331`, `332`, `333`, `335`, `337`, `338`, `339`, `341`, `343`, `344`, `346`, `347`, `350`, `353`, `349`, `357`, `359`, `361`, `363`, `364`, `365`, `367`, `369`, `370`, `373`, `375`, `377`, `380`, `381`, `384`, `386`, `389`, `390`, `393`, `394`, `117`, `397`, `398`, `399`, `402`, `404`, `405`, `406`, `410`, `412`, `415`, `417`, `419`, `420`, `422`, `425`, `428`, `431`, `432`, `433`, `436`, `438`, `440`, `442`, `445`, `447`, `448`, `449`, `452`, `454`, `457`, `461`, `463`, `466`, `469`, `471`, `474`, `476`, `479`, `482`, `485`, `486`, `487`, `490`, `491`, `493`, `156`, `496`, `497`, `499`, `501`, `503`, `504`, `507`, `509`, `512`, `515`, `516`, `519`, `522`, `524`, `525`, `529`, `531`, `533`, `535`, `536`, `538`, `541`, `543`, `545`, `546`, `548`, `549`, `551`, `553`, `556`, `557`, `559`, `560`, `563`, `566`, `570`, `572`, `574`, `575`, `578`, `581`, `582`, `585`, `587`, `588`, `590`, `593`, `594`, `596`, `597`, `600`, `602`, `605`, `606`, `608`, `611`, `613`, `418`, `615`, `617`, `620`, `622`, `625`, `628`, `631`, `632`, `634`, `635`, `638`, `640`, `643`, `87`, `645`, `648`, `649`, `651`, `652`, `654`, `656`, `233`, `658`, `660`, `663`, `666`, `668`, `669`, `670`, `671`, `673`, `675`, `677`, `679`, `681`, `684`, `685`, `688`, `689`, `692`, `695`, `698`, `701`, `705`, `707`, `708`, `709`, `712`, `715`, `716`, `720`, `722`, `724`, `727`, `729`, `732`, `733`, `734`, `735`, `737`, `738`, `437`, `742`, `744`, `745`, `746`, `748`, `750`, `752`, `754`, `755`, `756`, `758`, `514`, `759`, `760`, `762`, `764`, `270`, `766`, `767`, `768`, `770`, `772`, `773`, `776`, `778`, `780`, `782`, `785`, `787`, `791`, `793`, `796`, `797`, `798`, `800`, `801`, `804`, `806`, `807`, `810`, `812`, `814`, `595`, `815`, `817`, `819`, `820`, `823`, `825`, `828`, `830`, `832`, `835`, `837`, `839`, `841`, `843`, `845`, `847`, `849`, `852`, `855`, `857`, `858`, `862`, `865`, `866`, `868`, `870`, `872`, `874`, `877`, `879`, `882`, `884`, `886`, `888`, `890`, `891`, `893`, `896`, `899`, `901`, `902`, `903`, `905`, `908`, `911`, `599`, `913`, `915`, `917`, `918`, `921`, `923`, `924`, `925`, `926`, `929`, `931`, `933`, `934`, `935`, `936`, `939`, `942`, `943`, `945`, `946`, `949`, `951`, `953`, `955`, `958`, `960`, `963`, `964`, `967`, `968`, `969`, `971`, `973`, `974`, `977`, `979`, `981`, `983`, `985`, `987`, `989`, `991`, `994`, `995`, `996`, `998`, `1001`, `1003`, `1005`, `1007`, `1009`, `1012`, `1014`, `1015`, `1016`, `1018`, `1021`, `1022`, `1024`, `1025`, `1026`, `1030`, `1033`, `1034`, `1035`, `1038`, `1040`, `1043`, `1044`, `1047`, `219`, `990`, `1048`, `1050`, `1052`, `1053`, `1056`, `1058`, `1060`, `1061`, `1063`, `1066`, `1067`, `1070`, `1071`, `1074`, `1075`, `1076`, `1078`, `1081`, `1083`, `1085`, `1088`, `1089`, `1090`, `1093`, `1095`, `1097`, `1098`, `1101`, `1103`, `1105`, `1108`, `1109`, `260`, `1112`, `1115`, `1116`, `1118`, `1119`, `1120`, `1121`, `1123`, `1125`, `1126`, `1128`, `1131`, `1133`, `1135`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.92 |
| `TOKEN_P` | 99.90 |
| `TOKEN_R` | 99.94 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 95.08 |
| `SENTS_P` | 95.54 |
| `SENTS_R` | 94.63 |
| `TAG_ACC` | 90.73 |
| `POS_ACC` | 96.49 |
| `MORPH_ACC` | 99.83 |
| `DEP_UAS` | 85.05 |
| `DEP_LAS` | 80.96 |
| `LEMMA_ACC` | 92.78 |
|
{"language": ["ko"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/ko_udv25_koreangsd_trf
|
[
"spacy",
"token-classification",
"ko",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Korean-GSD
### Label Scheme
View label scheme (2415 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (2415 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2415 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (2415 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Korean-Kaist
| Feature | Description |
| --- | --- |
| **Name** | `ko_udv25_koreankaist_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (5329 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ecs`, `etm`, `f`, `f+f+jcj`, `f+f+jcs`, `f+f+jct`, `f+f+jxt`, `f+jca`, `f+jca+jp+ecc`, `f+jca+jp+ep+ef`, `f+jca+jxc`, `f+jca+jxc+jcm`, `f+jca+jxt`, `f+jcj`, `f+jcm`, `f+jco`, `f+jcs`, `f+jct`, `f+jct+jcm`, `f+jp+ef`, `f+jp+ep+ef`, `f+jp+etm`, `f+jxc`, `f+jxt`, `f+ncn`, `f+ncn+jcm`, `f+ncn+jcs`, `f+ncn+jp+ecc`, `f+ncn+jxt`, `f+ncpa+jcm`, `f+npp+jcs`, `f+nq`, `f+xsn`, `f+xsn+jco`, `f+xsn+jxt`, `ii`, `jca`, `jca+jcm`, `jca+jxc`, `jca+jxt`, `jcc`, `jcj`, `jcm`, `jco`, `jcr`, `jcr+jxc`, `jcs`, `jct`, `jct+jcm`, `jct+jxt`, `jp+ecc`, `jp+ecs`, `jp+ef`, `jp+ef+jcr`, `jp+ef+jcr+jxc`, `jp+ep+ecs`, `jp+ep+ef`, `jp+ep+etm`, `jp+ep+etn`, `jp+etm`, `jp+etn`, `jp+etn+jco`, `jp+etn+jxc`, `jxc`, `jxc+jca`, `jxc+jco`, `jxc+jcs`, `jxt`, `mad`, `mad+jxc`, `mad+jxt`, `mag`, `mag+jca`, `mag+jcm`, `mag+jcs`, `mag+jp+ef+jcr`, `mag+jxc`, `mag+jxc+jxc`, `mag+jxt`, `mag+xsn`, `maj`, `maj+jxc`, `maj+jxt`, `mma`, `mmd`, `nbn`, `nbn+jca`, `nbn+jca+jcj`, `nbn+jca+jcm`, `nbn+jca+jp+ef`, `nbn+jca+jxc`, `nbn+jca+jxt`, `nbn+jcc`, `nbn+jcj`, `nbn+jcm`, `nbn+jco`, `nbn+jcr`, `nbn+jcs`, `nbn+jct`, `nbn+jct+jcm`, `nbn+jct+jxt`, `nbn+jp+ecc`, `nbn+jp+ecs`, `nbn+jp+ecs+jca`, `nbn+jp+ecs+jcm`, `nbn+jp+ecs+jco`, `nbn+jp+ecs+jxc`, `nbn+jp+ecs+jxt`, `nbn+jp+ecx`, `nbn+jp+ef`, `nbn+jp+ef+jca`, `nbn+jp+ef+jco`, `nbn+jp+ef+jcr`, `nbn+jp+ef+jcr+jxc`, `nbn+jp+ef+jcr+jxt`, `nbn+jp+ef+jcs`, `nbn+jp+ef+jxc`, `nbn+jp+ef+jxc+jco`, `nbn+jp+ef+jxf`, `nbn+jp+ef+jxt`, `nbn+jp+ep+ecc`, `nbn+jp+ep+ecs`, `nbn+jp+ep+ecs+jxc`, `nbn+jp+ep+ef`, `nbn+jp+ep+ef+jcr`, `nbn+jp+ep+etm`, `nbn+jp+ep+etn`, `nbn+jp+ep+etn+jco`, `nbn+jp+ep+etn+jcs`, `nbn+jp+etm`, `nbn+jp+etn`, `nbn+jp+etn+jca`, `nbn+jp+etn+jca+jxt`, `nbn+jp+etn+jco`, `nbn+jp+etn+jcs`, `nbn+jp+etn+jxc`, `nbn+jp+etn+jxt`, `nbn+jxc`, `nbn+jxc+jca`, `nbn+jxc+jca+jxc`, `nbn+jxc+jca+jxt`, `nbn+jxc+jcc`, `nbn+jxc+jcm`, `nbn+jxc+jco`, `nbn+jxc+jcs`, `nbn+jxc+jp+ef`, `nbn+jxc+jxc`, `nbn+jxc+jxt`, `nbn+jxt`, `nbn+nbn`, `nbn+nbn+jp+ef`, `nbn+xsm+ecs`, `nbn+xsm+ef`, `nbn+xsm+ep+ef`, `nbn+xsm+ep+ef+jcr`, `nbn+xsm+etm`, `nbn+xsn`, `nbn+xsn+jca`, `nbn+xsn+jca+jp+ef+jcr`, `nbn+xsn+jca+jxc`, `nbn+xsn+jca+jxt`, `nbn+xsn+jcm`, `nbn+xsn+jco`, `nbn+xsn+jcs`, `nbn+xsn+jct`, `nbn+xsn+jp+ecc`, `nbn+xsn+jp+ecs`, `nbn+xsn+jp+ef`, `nbn+xsn+jp+ef+jcr`, `nbn+xsn+jp+ep+ef`, `nbn+xsn+jxc`, `nbn+xsn+jxt`, `nbn+xsv+etm`, `nbu`, `nbu+jca`, `nbu+jca+jxc`, `nbu+jca+jxt`, `nbu+jcc`, `nbu+jcc+jxc`, `nbu+jcj`, `nbu+jcm`, `nbu+jco`, `nbu+jcs`, `nbu+jct`, `nbu+jct+jxc`, `nbu+jp+ecc`, `nbu+jp+ecs`, `nbu+jp+ef`, `nbu+jp+ef+jcr`, `nbu+jp+ef+jxc`, `nbu+jp+ep+ecc`, `nbu+jp+ep+ecs`, `nbu+jp+ep+ef`, `nbu+jp+ep+ef+jcr`, `nbu+jp+ep+etm`, `nbu+jp+ep+etn+jco`, `nbu+jp+etm`, `nbu+jxc`, `nbu+jxc+jca`, `nbu+jxc+jcs`, `nbu+jxc+jp+ef`, `nbu+jxc+jp+ep+ef`, `nbu+jxc+jxt`, `nbu+jxt`, `nbu+ncn`, `nbu+ncn+jca`, `nbu+ncn+jcm`, `nbu+xsn`, `nbu+xsn+jca`, `nbu+xsn+jca+jxc`, `nbu+xsn+jca+jxt`, `nbu+xsn+jcm`, `nbu+xsn+jco`, `nbu+xsn+jcs`, `nbu+xsn+jp+ecs`, `nbu+xsn+jp+ep+ef`, `nbu+xsn+jxc`, `nbu+xsn+jxc+jxt`, `nbu+xsn+jxt`, `nbu+xsv+ecc`, `nbu+xsv+etm`, `ncn`, `ncn+f+ncpa+jco`, `ncn+jca`, `ncn+jca+jca`, `ncn+jca+jcc`, `ncn+jca+jcj`, `ncn+jca+jcm`, `ncn+jca+jcs`, `ncn+jca+jct`, `ncn+jca+jp+ecc`, `ncn+jca+jp+ecs`, `ncn+jca+jp+ef`, `ncn+jca+jp+ep+ef`, `ncn+jca+jp+etm`, `ncn+jca+jp+etn+jxt`, `ncn+jca+jxc`, `ncn+jca+jxc+jcc`, `ncn+jca+jxc+jcm`, `ncn+jca+jxc+jxc`, `ncn+jca+jxc+jxt`, `ncn+jca+jxt`, `ncn+jcc`, `ncn+jcc+jxc`, `ncn+jcj`, `ncn+jcj+jxt`, `ncn+jcm`, `ncn+jco`, `ncn+jcr`, `ncn+jcr+jxc`, `ncn+jcs`, `ncn+jcs+jxt`, `ncn+jct`, `ncn+jct+jcm`, `ncn+jct+jxc`, `ncn+jct+jxt`, `ncn+jcv`, `ncn+jp+ecc`, `ncn+jp+ecc+jct`, `ncn+jp+ecc+jxc`, `ncn+jp+ecs`, `ncn+jp+ecs+jcm`, `ncn+jp+ecs+jco`, `ncn+jp+ecs+jxc`, `ncn+jp+ecs+jxt`, `ncn+jp+ecx`, `ncn+jp+ef`, `ncn+jp+ef+jca`, `ncn+jp+ef+jcm`, `ncn+jp+ef+jco`, `ncn+jp+ef+jcr`, `ncn+jp+ef+jcr+jxc`, `ncn+jp+ef+jcr+jxt`, `ncn+jp+ef+jp+etm`, `ncn+jp+ef+jxc`, `ncn+jp+ef+jxf`, `ncn+jp+ef+jxt`, `ncn+jp+ep+ecc`, `ncn+jp+ep+ecs`, `ncn+jp+ep+ecs+jxc`, `ncn+jp+ep+ecx`, `ncn+jp+ep+ef`, `ncn+jp+ep+ef+jcr`, `ncn+jp+ep+ef+jcr+jxc`, `ncn+jp+ep+ef+jxc`, `ncn+jp+ep+ef+jxf`, `ncn+jp+ep+ef+jxt`, `ncn+jp+ep+ep+etm`, `ncn+jp+ep+etm`, `ncn+jp+ep+etn`, `ncn+jp+ep+etn+jca`, `ncn+jp+ep+etn+jca+jxc`, `ncn+jp+ep+etn+jco`, `ncn+jp+ep+etn+jcs`, `ncn+jp+ep+etn+jxt`, `ncn+jp+etm`, `ncn+jp+etn`, `ncn+jp+etn+jca`, `ncn+jp+etn+jca+jxc`, `ncn+jp+etn+jca+jxt`, `ncn+jp+etn+jco`, `ncn+jp+etn+jcs`, `ncn+jp+etn+jct`, `ncn+jp+etn+jxc`, `ncn+jp+etn+jxt`, `ncn+jxc`, `ncn+jxc+jca`, `ncn+jxc+jca+jxc`, `ncn+jxc+jca+jxt`, `ncn+jxc+jcc`, `ncn+jxc+jcm`, `ncn+jxc+jco`, `ncn+jxc+jcs`, `ncn+jxc+jct+jxt`, `ncn+jxc+jp+ef`, `ncn+jxc+jp+ef+jcr`, `ncn+jxc+jp+ep+ecs`, `ncn+jxc+jp+ep+ef`, `ncn+jxc+jp+etm`, `ncn+jxc+jxc`, `ncn+jxc+jxt`, `ncn+jxt`, `ncn+jxt+jcm`, `ncn+jxt+jxc`, `ncn+nbn`, `ncn+nbn+jca`, `ncn+nbn+jcm`, `ncn+nbn+jcs`, `ncn+nbn+jp+ecc`, `ncn+nbn+jp+ep+ef`, `ncn+nbn+jxc`, `ncn+nbn+jxt`, `ncn+nbu`, `ncn+nbu+jca`, `ncn+nbu+jcm`, `ncn+nbu+jco`, `ncn+nbu+jp+ef`, `ncn+nbu+jxc`, `ncn+nbu+ncn`, `ncn+ncn`, `ncn+ncn+jca`, `ncn+ncn+jca+jcc`, `ncn+ncn+jca+jcm`, `ncn+ncn+jca+jxc`, `ncn+ncn+jca+jxc+jcm`, `ncn+ncn+jca+jxc+jxc`, `ncn+ncn+jca+jxt`, `ncn+ncn+jcc`, `ncn+ncn+jcj`, `ncn+ncn+jcm`, `ncn+ncn+jco`, `ncn+ncn+jcr`, `ncn+ncn+jcs`, `ncn+ncn+jct`, `ncn+ncn+jct+jcm`, `ncn+ncn+jct+jxc`, `ncn+ncn+jct+jxt`, `ncn+ncn+jp+ecc`, `ncn+ncn+jp+ecs`, `ncn+ncn+jp+ef`, `ncn+ncn+jp+ef+jcm`, `ncn+ncn+jp+ef+jcr`, `ncn+ncn+jp+ef+jcs`, `ncn+ncn+jp+ep+ecc`, `ncn+ncn+jp+ep+ecs`, `ncn+ncn+jp+ep+ef`, `ncn+ncn+jp+ep+ef+jcr`, `ncn+ncn+jp+ep+ep+etm`, `ncn+ncn+jp+ep+etm`, `ncn+ncn+jp+ep+etn`, `ncn+ncn+jp+etm`, `ncn+ncn+jp+etn`, `ncn+ncn+jp+etn+jca`, `ncn+ncn+jp+etn+jco`, `ncn+ncn+jp+etn+jxc`, `ncn+ncn+jxc`, `ncn+ncn+jxc+jca`, `ncn+ncn+jxc+jcc`, `ncn+ncn+jxc+jcm`, `ncn+ncn+jxc+jco`, `ncn+ncn+jxc+jcs`, `ncn+ncn+jxc+jxc`, `ncn+ncn+jxt`, `ncn+ncn+nbn`, `ncn+ncn+ncn`, `ncn+ncn+ncn+jca`, `ncn+ncn+ncn+jca+jcm`, `ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+jcj`, `ncn+ncn+ncn+jcm`, `ncn+ncn+ncn+jco`, `ncn+ncn+ncn+jcs`, `ncn+ncn+ncn+jct+jxt`, `ncn+ncn+ncn+jp+etn+jxc`, `ncn+ncn+ncn+jxt`, `ncn+ncn+ncn+ncn+jca`, `ncn+ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+ncn+jco`, `ncn+ncn+ncn+xsn+jp+etm`, `ncn+ncn+ncpa`, `ncn+ncn+ncpa+jca`, `ncn+ncn+ncpa+jcm`, `ncn+ncn+ncpa+jco`, `ncn+ncn+ncpa+jcs`, `ncn+ncn+ncpa+jxc`, `ncn+ncn+ncpa+jxt`, `ncn+ncn+ncpa+ncn`, `ncn+ncn+ncpa+ncn+jca`, `ncn+ncn+ncpa+ncn+jcj`, `ncn+ncn+ncpa+ncn+jcm`, `ncn+ncn+ncpa+ncn+jxt`, `ncn+ncn+xsn`, `ncn+ncn+xsn+jca`, `ncn+ncn+xsn+jca+jxt`, `ncn+ncn+xsn+jcj`, `ncn+ncn+xsn+jcm`, `ncn+ncn+xsn+jco`, `ncn+ncn+xsn+jcs`, `ncn+ncn+xsn+jct`, `ncn+ncn+xsn+jp+ecs`, `ncn+ncn+xsn+jp+ep+ef`, `ncn+ncn+xsn+jp+etm`, `ncn+ncn+xsn+jxc`, `ncn+ncn+xsn+jxc+jcs`, `ncn+ncn+xsn+jxt`, `ncn+ncn+xsv+ecc`, `ncn+ncn+xsv+etm`, `ncn+ncpa`, `ncn+ncpa+jca`, `ncn+ncpa+jca+jcm`, `ncn+ncpa+jca+jxc`, `ncn+ncpa+jca+jxt`, `ncn+ncpa+jcc`, `ncn+ncpa+jcj`, `ncn+ncpa+jcm`, `ncn+ncpa+jco`, `ncn+ncpa+jcr`, `ncn+ncpa+jcs`, `ncn+ncpa+jct`, `ncn+ncpa+jct+jcm`, `ncn+ncpa+jct+jxt`, `ncn+ncpa+jp+ecc`, `ncn+ncpa+jp+ecc+jxc`, `ncn+ncpa+jp+ecs`, `ncn+ncpa+jp+ecs+jxc`, `ncn+ncpa+jp+ef`, `ncn+ncpa+jp+ef+jcr`, `ncn+ncpa+jp+ef+jcr+jxc`, `ncn+ncpa+jp+ep+ef`, `ncn+ncpa+jp+ep+etm`, `ncn+ncpa+jp+ep+etn`, `ncn+ncpa+jp+etm`, `ncn+ncpa+jxc`, `ncn+ncpa+jxc+jca+jxc`, `ncn+ncpa+jxc+jco`, `ncn+ncpa+jxc+jcs`, `ncn+ncpa+jxt`, `ncn+ncpa+nbn+jcs`, `ncn+ncpa+ncn`, `ncn+ncpa+ncn+jca`, `ncn+ncpa+ncn+jca+jcm`, `ncn+ncpa+ncn+jca+jxc`, `ncn+ncpa+ncn+jca+jxt`, `ncn+ncpa+ncn+jcj`, `ncn+ncpa+ncn+jcm`, `ncn+ncpa+ncn+jco`, `ncn+ncpa+ncn+jcs`, `ncn+ncpa+ncn+jct`, `ncn+ncpa+ncn+jct+jcm`, `ncn+ncpa+ncn+jp+ef+jcr`, `ncn+ncpa+ncn+jp+ep+etm`, `ncn+ncpa+ncn+jxc`, `ncn+ncpa+ncn+jxt`, `ncn+ncpa+ncn+xsn+jcm`, `ncn+ncpa+ncn+xsn+jxt`, `ncn+ncpa+ncpa`, `ncn+ncpa+ncpa+jca`, `ncn+ncpa+ncpa+jcj`, `ncn+ncpa+ncpa+jcm`, `ncn+ncpa+ncpa+jco`, `ncn+ncpa+ncpa+jcs`, `ncn+ncpa+ncpa+jp+ep+ef`, `ncn+ncpa+ncpa+jxt`, `ncn+ncpa+ncpa+ncn`, `ncn+ncpa+xsn`, `ncn+ncpa+xsn+jcm`, `ncn+ncpa+xsn+jco`, `ncn+ncpa+xsn+jcs`, `ncn+ncpa+xsn+jp+ecc`, `ncn+ncpa+xsn+jp+etm`, `ncn+ncpa+xsn+jxt`, `ncn+ncpa+xsv+ecc`, `ncn+ncpa+xsv+ecs`, `ncn+ncpa+xsv+ecx`, `ncn+ncpa+xsv+ecx+px+etm`, `ncn+ncpa+xsv+ef`, `ncn+ncpa+xsv+ef+jcm`, `ncn+ncpa+xsv+ef+jcr`, `ncn+ncpa+xsv+etm`, `ncn+ncpa+xsv+etn`, `ncn+ncpa+xsv+etn+jco`, `ncn+ncps`, `ncn+ncps+jca`, `ncn+ncps+jcm`, `ncn+ncps+jco`, `ncn+ncps+jcs`, `ncn+ncps+jp+ecs`, `ncn+ncps+jxt`, `ncn+ncps+ncn+jcs`, `ncn+ncps+ncpa+ncn`, `ncn+ncps+xsm+ef`, `ncn+ncps+xsm+etm`, `ncn+nnc`, `ncn+nnc+jcs`, `ncn+nnc+nnc`, `ncn+nno`, `ncn+nq`, `ncn+nq+jca`, `ncn+nq+jca+jxc`, `ncn+nq+jca+jxt`, `ncn+nq+jcm`, `ncn+nq+jcs`, `ncn+nq+jxt`, `ncn+nq+ncn+jcm`, `ncn+nq+ncn+xsn+jcs`, `ncn+nq+xsn+jxt`, `ncn+xsa`, `ncn+xsm+ecc`, `ncn+xsm+ecs`, `ncn+xsm+ecs+jxc`, `ncn+xsm+ecx`, `ncn+xsm+ecx+jcs`, `ncn+xsm+ecx+px+ep+etm`, `ncn+xsm+ef`, `ncn+xsm+ef+jcr`, `ncn+xsm+etm`, `ncn+xsm+etn+jcm`, `ncn+xsm+etn+jp+ef+jcr`, `ncn+xsn`, `ncn+xsn+jca`, `ncn+xsn+jca+jcj`, `ncn+xsn+jca+jxc`, `ncn+xsn+jca+jxc+jxc`, `ncn+xsn+jca+jxt`, `ncn+xsn+jcc`, `ncn+xsn+jcj`, `ncn+xsn+jcm`, `ncn+xsn+jco`, `ncn+xsn+jcs`, `ncn+xsn+jcs+jxt`, `ncn+xsn+jct`, `ncn+xsn+jct+jcm`, `ncn+xsn+jct+jxc`, `ncn+xsn+jct+jxt`, `ncn+xsn+jcv`, `ncn+xsn+jp+ecc`, `ncn+xsn+jp+ecc+jxc`, `ncn+xsn+jp+ecs`, `ncn+xsn+jp+ecs+jxc`, `ncn+xsn+jp+ecx`, `ncn+xsn+jp+ecx+jxt`, `ncn+xsn+jp+ef`, `ncn+xsn+jp+ef+jca`, `ncn+xsn+jp+ef+jcr`, `ncn+xsn+jp+ep+ecc`, `ncn+xsn+jp+ep+ecs`, `ncn+xsn+jp+ep+ef`, `ncn+xsn+jp+ep+ef+jcr`, `ncn+xsn+jp+ep+etm`, `ncn+xsn+jp+ep+etn`, `ncn+xsn+jp+etm`, `ncn+xsn+jp+etn`, `ncn+xsn+jp+etn+jca`, `ncn+xsn+jp+etn+jca+jxt`, `ncn+xsn+jp+etn+jxc`, `ncn+xsn+jp+etn+jxt`, `ncn+xsn+jxc`, `ncn+xsn+jxc+jcm`, `ncn+xsn+jxc+jco`, `ncn+xsn+jxc+jcs`, `ncn+xsn+jxc+jxc`, `ncn+xsn+jxt`, `ncn+xsn+ncn+jca`, `ncn+xsn+ncn+jca+jxt`, `ncn+xsn+ncn+jcs`, `ncn+xsn+ncpa+jca`, `ncn+xsn+xsn`, `ncn+xsn+xsn+jca`, `ncn+xsn+xsn+jcm`, `ncn+xsn+xsn+jp+ecs`, `ncn+xsn+xsn+jxc`, `ncn+xsn+xsn+jxc+jcc`, `ncn+xsn+xsn+jxc+jcs`, `ncn+xsn+xsv+ecc`, `ncn+xsn+xsv+etm`, `ncn+xsn+xsv+etn`, `ncn+xsv+ecc`, `ncn+xsv+ecs`, `ncn+xsv+ecx`, `ncn+xsv+ef`, `ncn+xsv+ep+ecs`, `ncn+xsv+ep+ef`, `ncn+xsv+ep+etm`, `ncn+xsv+etm`, `ncn+xsv+etn+jca`, `ncpa`, `ncpa+jca`, `ncpa+jca+jcm`, `ncpa+jca+jct`, `ncpa+jca+jp+ecs`, `ncpa+jca+jp+ef`, `ncpa+jca+jp+ep+ef`, `ncpa+jca+jxc`, `ncpa+jca+jxc+jcm`, `ncpa+jca+jxc+jxc`, `ncpa+jca+jxc+jxt`, `ncpa+jca+jxt`, `ncpa+jcc`, `ncpa+jcj`, `ncpa+jcm`, `ncpa+jco`, `ncpa+jcr`, `ncpa+jcs`, `ncpa+jct`, `ncpa+jct+jcm`, `ncpa+jct+jxc`, `ncpa+jct+jxt`, `ncpa+jp+ecc`, `ncpa+jp+ecs`, `ncpa+jp+ecs+jxc`, `ncpa+jp+ecx`, `ncpa+jp+ecx+jxc`, `ncpa+jp+ef`, `ncpa+jp+ef+jca`, `ncpa+jp+ef+jco`, `ncpa+jp+ef+jcr`, `ncpa+jp+ef+jxc`, `ncpa+jp+ef+jxf`, `ncpa+jp+ep+ecc`, `ncpa+jp+ep+ecs`, `ncpa+jp+ep+ef`, `ncpa+jp+ep+ef+jca`, `ncpa+jp+ep+ef+jcr`, `ncpa+jp+ep+ef+jxt`, `ncpa+jp+ep+etm`, `ncpa+jp+ep+etn+jca`, `ncpa+jp+ep+etn+jca+jxc`, `ncpa+jp+ep+etn+jcs`, `ncpa+jp+etm`, `ncpa+jp+etn`, `ncpa+jp+etn+jca`, `ncpa+jp+etn+jca+jxt`, `ncpa+jp+etn+jco`, `ncpa+jp+etn+jcs`, `ncpa+jp+etn+jxc`, `ncpa+jp+etn+jxt`, `ncpa+jxc`, `ncpa+jxc+jca`, `ncpa+jxc+jca+jxc`, `ncpa+jxc+jca+jxt`, `ncpa+jxc+jcc`, `ncpa+jxc+jcm`, `ncpa+jxc+jco`, `ncpa+jxc+jcs`, `ncpa+jxc+jxc`, `ncpa+jxt`, `ncpa+jxt+jxc`, `ncpa+jxt+jxt`, `ncpa+nbn+jca`, `ncpa+nbn+jct`, `ncpa+nbn+jp+ef`, `ncpa+nbn+jp+ep+ef`, `ncpa+nbn+jp+etm`, `ncpa+nbn+jxc+jcc`, `ncpa+nbu+jca`, `ncpa+ncn`, `ncpa+ncn+jca`, `ncpa+ncn+jca+jcm`, `ncpa+ncn+jca+jxc`, `ncpa+ncn+jca+jxc+jcm`, `ncpa+ncn+jca+jxt`, `ncpa+ncn+jcc`, `ncpa+ncn+jcj`, `ncpa+ncn+jcm`, `ncpa+ncn+jco`, `ncpa+ncn+jcr`, `ncpa+ncn+jcs`, `ncpa+ncn+jct`, `ncpa+ncn+jct+jcm`, `ncpa+ncn+jct+jxc`, `ncpa+ncn+jp+ecc`, `ncpa+ncn+jp+ecs`, `ncpa+ncn+jp+ef`, `ncpa+ncn+jp+ef+jcr`, `ncpa+ncn+jp+ef+jcr+jxc`, `ncpa+ncn+jp+ep+ef`, `ncpa+ncn+jp+ep+etm`, `ncpa+ncn+jp+etm`, `ncpa+ncn+jp+etn+jca+jxt`, `ncpa+ncn+jp+etn+jco`, `ncpa+ncn+jp+etn+jxc`, `ncpa+ncn+jxc`, `ncpa+ncn+jxc+jcc`, `ncpa+ncn+jxc+jco`, `ncpa+ncn+jxt`, `ncpa+ncn+nbn`, `ncpa+ncn+ncn`, `ncpa+ncn+ncn+jca`, `ncpa+ncn+ncn+jca+jxt`, `ncpa+ncn+ncn+jcm`, `ncpa+ncn+ncn+jco`, `ncpa+ncn+ncn+jcs`, `ncpa+ncn+ncn+jp+ep+ef`, `ncpa+ncn+ncn+jp+etm`, `ncpa+ncn+ncn+jxt`, `ncpa+ncn+ncn+ncn`, `ncpa+ncn+ncn+xsn+jxt`, `ncpa+ncn+ncpa`, `ncpa+ncn+ncpa+jca`, `ncpa+ncn+ncpa+jcj`, `ncpa+ncn+ncpa+jco`, `ncpa+ncn+ncpa+ncn`, `ncpa+ncn+ncpa+ncn+jco`, `ncpa+ncn+xsn`, `ncpa+ncn+xsn+jca`, `ncpa+ncn+xsn+jca+jxc`, `ncpa+ncn+xsn+jcj`, `ncpa+ncn+xsn+jcm`, `ncpa+ncn+xsn+jco`, `ncpa+ncn+xsn+jcs`, `ncpa+ncn+xsn+jct`, `ncpa+ncn+xsn+jp+ep+ef`, `ncpa+ncn+xsn+jp+etm`, `ncpa+ncn+xsn+jxt`, `ncpa+ncpa`, `ncpa+ncpa+jca`, `ncpa+ncpa+jca+jcm`, `ncpa+ncpa+jca+jxc`, `ncpa+ncpa+jca+jxt`, `ncpa+ncpa+jcj`, `ncpa+ncpa+jcm`, `ncpa+ncpa+jco`, `ncpa+ncpa+jcs`, `ncpa+ncpa+jct`, `ncpa+ncpa+jct+jxc`, `ncpa+ncpa+jct+jxt`, `ncpa+ncpa+jp+ecc`, `ncpa+ncpa+jp+ecs`, `ncpa+ncpa+jp+ecx`, `ncpa+ncpa+jp+ef`, `ncpa+ncpa+jp+ef+jca`, `ncpa+ncpa+jp+ef+jcr`, `ncpa+ncpa+jp+ef+jcr+jxc`, `ncpa+ncpa+jp+ep+ecs`, `ncpa+ncpa+jp+etm`, `ncpa+ncpa+jxc`, `ncpa+ncpa+jxt`, `ncpa+ncpa+ncn`, `ncpa+ncpa+ncn+jca`, `ncpa+ncpa+ncn+jcj`, `ncpa+ncpa+ncn+jcm`, `ncpa+ncpa+ncn+jco`, `ncpa+ncpa+ncn+jcs`, `ncpa+ncpa+ncn+jxt`, `ncpa+ncpa+ncpa+jcm`, `ncpa+ncpa+ncpa+jcs`, `ncpa+ncpa+ncpa+ncpa+jco`, `ncpa+ncpa+xsn`, `ncpa+ncpa+xsn+jca`, `ncpa+ncpa+xsn+jcj`, `ncpa+ncpa+xsn+jco`, `ncpa+ncpa+xsn+jcs`, `ncpa+ncpa+xsn+jxc`, `ncpa+ncpa+xsn+jxt`, `ncpa+ncpa+xsv+ecc`, `ncpa+ncpa+xsv+ecs`, `ncpa+ncpa+xsv+ef`, `ncpa+ncpa+xsv+ep+ef`, `ncpa+ncpa+xsv+ep+etm`, `ncpa+ncpa+xsv+etm`, `ncpa+ncpa+xsv+etn+jca`, `ncpa+ncps`, `ncpa+ncps+jca`, `ncpa+ncps+jcm`, `ncpa+ncps+jco`, `ncpa+ncps+jcs`, `ncpa+ncps+jxt`, `ncpa+ncps+xsm+etm`, `ncpa+nq+jca`, `ncpa+xsa`, `ncpa+xsn`, `ncpa+xsn+jca`, `ncpa+xsn+jca+jxc`, `ncpa+xsn+jca+jxt`, `ncpa+xsn+jcc`, `ncpa+xsn+jcj`, `ncpa+xsn+jcm`, `ncpa+xsn+jco`, `ncpa+xsn+jcs`, `ncpa+xsn+jct`, `ncpa+xsn+jp+ecc`, `ncpa+xsn+jp+ecs`, `ncpa+xsn+jp+ecs+jxc`, `ncpa+xsn+jp+ecx`, `ncpa+xsn+jp+ecx+jxt`, `ncpa+xsn+jp+ef`, `ncpa+xsn+jp+ef+jcr`, `ncpa+xsn+jp+ef+jxf`, `ncpa+xsn+jp+ep+ecc`, `ncpa+xsn+jp+ep+ef`, `ncpa+xsn+jp+ep+ef+jco`, `ncpa+xsn+jp+ep+ef+jcr`, `ncpa+xsn+jp+etm`, `ncpa+xsn+jp+etn`, `ncpa+xsn+jp+etn+jco`, `ncpa+xsn+jp+etn+jxc`, `ncpa+xsn+jxc`, `ncpa+xsn+jxt`, `ncpa+xsv+ecc`, `ncpa+xsv+ecc+jcm`, `ncpa+xsv+ecc+jxc`, `ncpa+xsv+ecc+jxt`, `ncpa+xsv+ecs`, `ncpa+xsv+ecs+jca`, `ncpa+xsv+ecs+jco`, `ncpa+xsv+ecs+jp+ef`, `ncpa+xsv+ecs+jxc`, `ncpa+xsv+ecs+jxc+jxt`, `ncpa+xsv+ecs+jxt`, `ncpa+xsv+ecx`, `ncpa+xsv+ecx+jco`, `ncpa+xsv+ecx+jxc`, `ncpa+xsv+ecx+jxt`, `ncpa+xsv+ecx+px+ecc`, `ncpa+xsv+ecx+px+ecs`, `ncpa+xsv+ecx+px+ecx`, `ncpa+xsv+ecx+px+ecx+jxc`, `ncpa+xsv+ecx+px+ecx+px+ecs`, `ncpa+xsv+ecx+px+ef`, `ncpa+xsv+ecx+px+ef+jcr`, `ncpa+xsv+ecx+px+ep+ecc`, `ncpa+xsv+ecx+px+ep+ecs`, `ncpa+xsv+ecx+px+ep+ef`, `ncpa+xsv+ecx+px+ep+ef+jcr`, `ncpa+xsv+ecx+px+ep+etm`, `ncpa+xsv+ecx+px+ep+etn+jca`, `ncpa+xsv+ecx+px+ep+etn+jco`, `ncpa+xsv+ecx+px+ep+etn+jxc`, `ncpa+xsv+ecx+px+ep+etn+jxt`, `ncpa+xsv+ecx+px+etm`, `ncpa+xsv+ecx+px+etn`, `ncpa+xsv+ecx+px+etn+jca`, `ncpa+xsv+ecx+px+etn+jco`, `ncpa+xsv+ef`, `ncpa+xsv+ef+jca`, `ncpa+xsv+ef+jcj`, `ncpa+xsv+ef+jcm`, `ncpa+xsv+ef+jco`, `ncpa+xsv+ef+jcr`, `ncpa+xsv+ef+jcr+jxt`, `ncpa+xsv+ef+jcs`, `ncpa+xsv+ef+jxc`, `ncpa+xsv+ef+jxf`, `ncpa+xsv+ef+jxt`, `ncpa+xsv+ep+ecc`, `ncpa+xsv+ep+ecs`, `ncpa+xsv+ep+ecs+jco`, `ncpa+xsv+ep+ecs+jxc`, `ncpa+xsv+ep+ecs+jxt`, `ncpa+xsv+ep+ecx`, `ncpa+xsv+ep+ecx+jxc`, `ncpa+xsv+ep+ef`, `ncpa+xsv+ep+ef+jca`, `ncpa+xsv+ep+ef+jca+jxt`, `ncpa+xsv+ep+ef+jco`, `ncpa+xsv+ep+ef+jcr`, `ncpa+xsv+ep+ef+jcr+jxc`, `ncpa+xsv+ep+ef+jcr+jxc+jxt`, `ncpa+xsv+ep+ef+jxc`, `ncpa+xsv+ep+ef+jxf`, `ncpa+xsv+ep+ef+jxt`, `ncpa+xsv+ep+ep+ecs`, `ncpa+xsv+ep+ep+ef`, `ncpa+xsv+ep+etm`, `ncpa+xsv+ep+etn`, `ncpa+xsv+ep+etn+jca`, `ncpa+xsv+ep+etn+jca+jxc`, `ncpa+xsv+ep+etn+jcj`, `ncpa+xsv+ep+etn+jco`, `ncpa+xsv+ep+etn+jcs`, `ncpa+xsv+ep+etn+jxt`, `ncpa+xsv+etm`, `ncpa+xsv+etn`, `ncpa+xsv+etn+jca`, `ncpa+xsv+etn+jca+jxc`, `ncpa+xsv+etn+jca+jxt`, `ncpa+xsv+etn+jco`, `ncpa+xsv+etn+jcs`, `ncpa+xsv+etn+jct`, `ncpa+xsv+etn+jxc`, `ncpa+xsv+etn+jxc+jcm`, `ncpa+xsv+etn+jxc+jcs`, `ncpa+xsv+etn+jxc+jxc`, `ncpa+xsv+etn+jxc+jxt`, `ncpa+xsv+etn+jxt`, `ncps`, `ncps+jca`, `ncps+jca+jcm`, `ncps+jca+jxc`, `ncps+jca+jxc+jcm`, `ncps+jcc`, `ncps+jcj`, `ncps+jcm`, `ncps+jco`, `ncps+jcs`, `ncps+jct`, `ncps+jct+jcm`, `ncps+jct+jxt`, `ncps+jp+ecc`, `ncps+jp+ecs`, `ncps+jp+ecs+jxt`, `ncps+jp+ef`, `ncps+jp+ef+jcr`, `ncps+jp+ep+ef`, `ncps+jp+ep+etn`, `ncps+jp+etm`, `ncps+jp+etn+jcs`, `ncps+jp+etn+jxt`, `ncps+jxc`, `ncps+jxc+jxc`, `ncps+jxt`, `ncps+nbn+jp+etm`, `ncps+nbn+jxc`, `ncps+ncn`, `ncps+ncn+jca`, `ncps+ncn+jca+jcm`, `ncps+ncn+jcm`, `ncps+ncn+jco`, `ncps+ncn+jcs`, `ncps+ncn+jct+jxt`, `ncps+ncn+jp+ef`, `ncps+ncn+jp+ef+jcr`, `ncps+ncn+jp+etm`, `ncps+ncn+jxc+jco`, `ncps+ncn+jxt`, `ncps+ncn+ncn`, `ncps+ncn+ncn+jca+jxc`, `ncps+ncn+ncn+jcm`, `ncps+ncn+ncn+jco`, `ncps+ncn+ncn+jxt`, `ncps+ncn+xsn`, `ncps+ncn+xsn+jca`, `ncps+ncn+xsn+jcj`, `ncps+ncn+xsn+jco`, `ncps+ncn+xsn+jp+ecc`, `ncps+ncn+xsn+jp+etm`, `ncps+ncpa`, `ncps+ncpa+jca`, `ncps+ncpa+jcc`, `ncps+ncpa+jcj`, `ncps+ncpa+jcm`, `ncps+ncpa+jco`, `ncps+ncpa+jcs`, `ncps+ncpa+jp+etm`, `ncps+ncpa+jxt`, `ncps+ncpa+xsv+etm`, `ncps+ncps+jca`, `ncps+ncps+jcm`, `ncps+ncps+xsm+ecc`, `ncps+ncps+xsm+ecs`, `ncps+ncps+xsm+etm`, `ncps+xsa`, `ncps+xsa+jxc`, `ncps+xsm+ecc`, `ncps+xsm+ecc+jxc`, `ncps+xsm+ecc+jxt`, `ncps+xsm+ecs`, `ncps+xsm+ecs+jxc`, `ncps+xsm+ecs+jxt`, `ncps+xsm+ecx`, `ncps+xsm+ecx+jcs`, `ncps+xsm+ecx+jxc`, `ncps+xsm+ecx+jxt`, `ncps+xsm+ecx+px+ecc`, `ncps+xsm+ecx+px+ecs`, `ncps+xsm+ecx+px+ecx`, `ncps+xsm+ecx+px+ecx+jxt`, `ncps+xsm+ecx+px+ef`, `ncps+xsm+ecx+px+ep+ecs`, `ncps+xsm+ecx+px+ep+ef`, `ncps+xsm+ecx+px+ep+etm`, `ncps+xsm+ecx+px+ep+etn+jco`, `ncps+xsm+ecx+px+etm`, `ncps+xsm+ecx+px+etn`, `ncps+xsm+ecx+px+etn+jca`, `ncps+xsm+ecx+px+etn+jcj`, `ncps+xsm+ecx+px+etn+jco`, `ncps+xsm+ef`, `ncps+xsm+ef+jco`, `ncps+xsm+ef+jcr`, `ncps+xsm+ef+jcr+jxc`, `ncps+xsm+ef+jcr+jxt`, `ncps+xsm+ef+jxf`, `ncps+xsm+ef+jxt`, `ncps+xsm+ep+ecc`, `ncps+xsm+ep+ecs`, `ncps+xsm+ep+ecs+etm`, `ncps+xsm+ep+ef`, `ncps+xsm+ep+ef+jco`, `ncps+xsm+ep+ef+jcr`, `ncps+xsm+ep+ef+jxf`, `ncps+xsm+ep+ep+ef`, `ncps+xsm+ep+etm`, `ncps+xsm+ep+etn`, `ncps+xsm+ep+etn+jxt`, `ncps+xsm+etm`, `ncps+xsm+etn`, `ncps+xsm+etn+jca`, `ncps+xsm+etn+jca+jxt`, `ncps+xsm+etn+jcj`, `ncps+xsm+etn+jcm`, `ncps+xsm+etn+jco`, `ncps+xsm+etn+jcs`, `ncps+xsm+etn+jct`, `ncps+xsm+etn+jct+jcm`, `ncps+xsm+etn+jp+ef+jcr`, `ncps+xsm+etn+jp+etm`, `ncps+xsm+etn+jxc`, `ncps+xsm+etn+jxc+jxt`, `ncps+xsm+etn+jxt`, `ncps+xsn`, `ncps+xsn+jca`, `ncps+xsn+jca+jxt`, `ncps+xsn+jcm`, `ncps+xsn+jco`, `ncps+xsn+jcs`, `ncps+xsn+jp+ecc`, `ncps+xsn+jp+ep+ecs`, `ncps+xsn+jp+etm`, `ncps+xsn+jxc`, `ncps+xsn+jxt`, `ncps+xsv+etm`, `nnc`, `nnc+f`, `nnc+f+jca`, `nnc+f+jp+ef`, `nnc+jca`, `nnc+jca+jxc`, `nnc+jca+jxt`, `nnc+jcc`, `nnc+jcj`, `nnc+jcm`, `nnc+jco`, `nnc+jcs`, `nnc+jp+ecc`, `nnc+jp+ecs`, `nnc+jp+ef`, `nnc+jp+ef+jcr`, `nnc+jp+ep+ef`, `nnc+jp+ep+etm`, `nnc+jp+etm`, `nnc+jp+etn+jca`, `nnc+jxc`, `nnc+jxt`, `nnc+nbn`, `nnc+nbn+jcm`, `nnc+nbn+jco`, `nnc+nbn+nbu+jcc`, `nnc+nbn+nbu+jcs`, `nnc+nbn+xsn`, `nnc+nbu`, `nnc+nbu+jca`, `nnc+nbu+jca+jxc`, `nnc+nbu+jcc`, `nnc+nbu+jcj`, `nnc+nbu+jcm`, `nnc+nbu+jco`, `nnc+nbu+jcs`, `nnc+nbu+jp+ef`, `nnc+nbu+jp+ef+jcr`, `nnc+nbu+jp+ep+ecs`, `nnc+nbu+jp+ep+ef`, `nnc+nbu+jp+etm`, `nnc+nbu+jxc`, `nnc+nbu+jxc+jcs`, `nnc+nbu+jxc+jxt`, `nnc+nbu+jxt`, `nnc+nbu+nbu`, `nnc+nbu+nbu+jcm`, `nnc+nbu+nbu+jp+ef+jcr`, `nnc+nbu+ncn`, `nnc+nbu+ncn+jca`, `nnc+nbu+ncn+jcj`, `nnc+nbu+ncn+jcm`, `nnc+nbu+ncn+jxc`, `nnc+nbu+xsn`, `nnc+nbu+xsn+jca`, `nnc+nbu+xsn+jcm`, `nnc+nbu+xsn+jco`, `nnc+nbu+xsn+jcs`, `nnc+nbu+xsn+jp+ecc`, `nnc+nbu+xsn+jp+ef`, `nnc+nbu+xsn+jxc`, `nnc+nbu+xsn+jxc+jcm`, `nnc+nbu+xsn+jxt`, `nnc+nbu+xsv+etm`, `nnc+ncn`, `nnc+ncn+jca`, `nnc+ncn+jca+jxt`, `nnc+ncn+jcj`, `nnc+ncn+jcm`, `nnc+ncn+jco`, `nnc+ncn+jcs`, `nnc+ncn+jct`, `nnc+ncn+jp+ef`, `nnc+ncn+jp+etm`, `nnc+ncn+jxc`, `nnc+ncn+jxt`, `nnc+ncn+nbu`, `nnc+ncn+nbu+xsn+jca`, `nnc+ncn+ncn+jca+jxt`, `nnc+ncn+ncn+xsn`, `nnc+ncn+nnc+nnc`, `nnc+ncn+xsn`, `nnc+ncn+xsn+jp+etm`, `nnc+ncn+xsn+jxt`, `nnc+ncpa`, `nnc+ncpa+jcs`, `nnc+nnc`, `nnc+nnc+jca`, `nnc+nnc+jca+jxt`, `nnc+nnc+jcm`, `nnc+nnc+jco`, `nnc+nnc+jp+ef`, `nnc+nnc+nbu`, `nnc+nnc+nbu+jca`, `nnc+nnc+nbu+jcc`, `nnc+nnc+nbu+jcm`, `nnc+nnc+nbu+jco`, `nnc+nnc+nbu+jcs`, `nnc+nnc+nbu+jp+ep+ef`, `nnc+nnc+nbu+jp+etm`, `nnc+nnc+nbu+jxc`, `nnc+nnc+nbu+xsn`, `nnc+nnc+nbu+xsn+jcm`, `nnc+nnc+nbu+xsn+jxc`, `nnc+nnc+ncn+jco`, `nnc+nnc+nnc`, `nnc+nnc+nnc+nnc`, `nnc+nnc+su+jp+ef`, `nnc+nnc+xsn`, `nnc+nnc+xsn+jcm`, `nnc+nnc+xsn+nbu+jca`, `nnc+nnc+xsn+nbu+jcm`, `nnc+nnc+xsn+nbu+jco`, `nnc+nnc+xsn+nbu+jcs`, `nnc+nno+nbu`, `nnc+nno+nbu+jcc`, `nnc+su`, `nnc+su+jca`, `nnc+su+jcm`, `nnc+su+jco`, `nnc+su+jcs`, `nnc+su+jxc`, `nnc+su+xsn`, `nnc+xsn`, `nnc+xsn+jca`, `nnc+xsn+jca+jxt`, `nnc+xsn+jcm`, `nnc+xsn+jco`, `nnc+xsn+jcs`, `nnc+xsn+jp+ef`, `nnc+xsn+jxc`, `nnc+xsn+nbn+jca`, `nnc+xsn+nbu`, `nnc+xsn+nbu+jca`, `nnc+xsn+nbu+jcm`, `nnc+xsn+nbu+jco`, `nnc+xsn+nbu+jcs`, `nnc+xsn+nnc+nbu`, `nnc+xsn+nnc+nbu+jcm`, `nno`, `nno+jca`, `nno+jca+jxt`, `nno+jcj`, `nno+jcm`, `nno+jco`, `nno+jcs`, `nno+jxt`, `nno+nbn`, `nno+nbn+jcm`, `nno+nbn+xsn`, `nno+nbu`, `nno+nbu+jca`, `nno+nbu+jca+jxc`, `nno+nbu+jca+jxt`, `nno+nbu+jcc`, `nno+nbu+jcj`, `nno+nbu+jcm`, `nno+nbu+jco`, `nno+nbu+jcs`, `nno+nbu+jct`, `nno+nbu+jp+ecc`, `nno+nbu+jp+ecs`, `nno+nbu+jp+ef`, `nno+nbu+jp+ep+ecc`, `nno+nbu+jp+ep+ecs`, `nno+nbu+jp+ep+ef`, `nno+nbu+jp+etm`, `nno+nbu+jxc`, `nno+nbu+jxc+jca`, `nno+nbu+jxc+jcm`, `nno+nbu+jxc+jp+ef`, `nno+nbu+jxc+jp+etm`, `nno+nbu+jxc+jxc`, `nno+nbu+jxc+jxt`, `nno+nbu+jxt`, `nno+nbu+nbu`, `nno+nbu+ncn`, `nno+nbu+ncn+jp+ep+ef`, `nno+nbu+ncn+ncn`, `nno+nbu+xsn`, `nno+nbu+xsn+jca`, `nno+nbu+xsn+jcc`, `nno+nbu+xsn+jcm`, `nno+nbu+xsn+jxc`, `nno+nbu+xsn+jxt`, `nno+ncn`, `nno+ncn+jca`, `nno+ncn+jca+jxc`, `nno+ncn+jca+jxt`, `nno+ncn+jcm`, `nno+ncn+jco`, `nno+ncn+jcs`, `nno+ncn+jct`, `nno+ncn+jp+ef`, `nno+ncn+jp+etm`, `nno+ncn+jxc`, `nno+ncn+jxc+jxt`, `nno+ncn+ncn+jp+etm`, `nno+ncn+xsn`, `nno+ncn+xsn+jca`, `nno+ncn+xsn+jp+ep+ef`, `nno+ncn+xsn+jp+etm`, `nno+ncpa+jp+ep+etn+jca+jxc`, `nno+nnc`, `nno+xsn`, `nno+xsn+jca`, `nno+xsn+jca+jxc`, `nno+xsn+jxc`, `nno+xsn+jxc+jcs`, `nno+xsn+nbu`, `nno+xsn+nbu+jcm`, `npd`, `npd+jca`, `npd+jca+jcm`, `npd+jca+jp+ef`, `npd+jca+jp+ef+jca`, `npd+jca+jxc`, `npd+jca+jxc+jcm`, `npd+jca+jxt`, `npd+jcc`, `npd+jcj`, `npd+jcm`, `npd+jco`, `npd+jcs`, `npd+jct`, `npd+jct+jcm`, `npd+jct+jxt`, `npd+jp+ecc`, `npd+jp+ecs`, `npd+jp+ecs+jco`, `npd+jp+ecs+jxt`, `npd+jp+ef`, `npd+jp+ef+jca`, `npd+jp+ef+jcm`, `npd+jp+ef+jco`, `npd+jp+ef+jcr`, `npd+jp+ef+jcs`, `npd+jp+ef+jp+ef`, `npd+jp+ef+jp+etm`, `npd+jp+ef+jxc`, `npd+jp+ef+jxt`, `npd+jp+ep+ef`, `npd+jp+etm`, `npd+jxc`, `npd+jxc+jca`, `npd+jxc+jca+jxc`, `npd+jxc+jcc`, `npd+jxc+jcr`, `npd+jxc+jp+ef`, `npd+jxc+jxc`, `npd+jxc+jxt`, `npd+jxt`, `npd+nbn`, `npd+nbn+jca`, `npd+nbn+jcs`, `npd+nbn+jxc`, `npd+nbn+jxc+jxt`, `npd+ncn`, `npd+ncn+jca`, `npd+ncn+jca+jxc`, `npd+ncn+jcm`, `npd+ncn+jco`, `npd+ncn+jcs`, `npd+ncn+jxt`, `npd+npd`, `npd+xsn`, `npd+xsn+jca`, `npd+xsn+jca+jxc`, `npd+xsn+jca+jxt`, `npd+xsn+jcm`, `npd+xsn+jco`, `npd+xsn+jcs`, `npd+xsn+jct`, `npd+xsn+jp+ef`, `npd+xsn+jxc`, `npd+xsn+jxt`, `npp`, `npp+jca`, `npp+jca+jcm`, `npp+jca+jxc`, `npp+jca+jxc+jcm`, `npp+jca+jxt`, `npp+jcc`, `npp+jcj`, `npp+jcm`, `npp+jco`, `npp+jcs`, `npp+jcs+jxt`, `npp+jct`, `npp+jct+jcm`, `npp+jct+jxc`, `npp+jct+jxt`, `npp+jp+ecs`, `npp+jp+ecs+jco`, `npp+jp+ef`, `npp+jp+ef+jcs`, `npp+jp+ef+jxc+jcs`, `npp+jp+ef+jxt`, `npp+jp+ep+ecc`, `npp+jp+ep+ef`, `npp+jp+ep+etm`, `npp+jp+etm`, `npp+jxc`, `npp+jxc+jcc`, `npp+jxc+jcm`, `npp+jxc+jco`, `npp+jxt`, `npp+nbn+jca`, `npp+nbn+jcs`, `npp+ncn`, `npp+ncn+jca`, `npp+ncn+jca+jxc`, `npp+ncn+jca+jxt`, `npp+ncn+jcm`, `npp+ncn+jco`, `npp+ncn+jcs`, `npp+ncn+jct`, `npp+ncn+jct+jxt`, `npp+ncn+jp+ecs`, `npp+ncn+jxc`, `npp+ncn+jxt`, `npp+ncn+xsn`, `npp+ncpa`, `npp+ncpa+jca`, `npp+ncpa+jca+jxc`, `npp+ncpa+jcj`, `npp+ncpa+jcm`, `npp+ncpa+jco`, `npp+ncpa+jcs`, `npp+ncpa+jxt`, `npp+ncpa+ncpa+jca`, `npp+ncpa+xsn+jp+ecc`, `npp+ncpa+xsn+jp+etm`, `npp+npp+jco`, `npp+xsn`, `npp+xsn+jca`, `npp+xsn+jca+jxc`, `npp+xsn+jca+jxc+jxc`, `npp+xsn+jca+jxt`, `npp+xsn+jcj`, `npp+xsn+jcm`, `npp+xsn+jco`, `npp+xsn+jcs`, `npp+xsn+jcs+jxt`, `npp+xsn+jct`, `npp+xsn+jct+jcm`, `npp+xsn+jct+jxt`, `npp+xsn+jp+ecs`, `npp+xsn+jp+ef`, `npp+xsn+jp+etm`, `npp+xsn+jxc`, `npp+xsn+jxc+jcs`, `npp+xsn+jxc+jxt`, `npp+xsn+jxt`, `npp+xsn+ncn`, `npp+xsn+xsn`, `npp+xsn+xsn+jca`, `npp+xsn+xsn+jca+jxt`, `nq`, `nq+jca`, `nq+jca+jca`, `nq+jca+jca+jxc`, `nq+jca+jcm`, `nq+jca+jxc`, `nq+jca+jxc+jcm`, `nq+jca+jxc+jxc`, `nq+jca+jxt`, `nq+jcc`, `nq+jcj`, `nq+jcm`, `nq+jco`, `nq+jcr`, `nq+jcs`, `nq+jcs+jca+jxc`, `nq+jcs+jxt`, `nq+jct`, `nq+jct+jcm`, `nq+jct+jxt`, `nq+jp+ecc`, `nq+jp+ecs`, `nq+jp+ef`, `nq+jp+ef+jcr`, `nq+jp+ef+jcr+jxc`, `nq+jp+ep+ecc`, `nq+jp+ep+ecs`, `nq+jp+ep+ef`, `nq+jp+ep+etm`, `nq+jp+ep+etn`, `nq+jp+etm`, `nq+jp+etn+jco`, `nq+jxc`, `nq+jxc+jca+jxt`, `nq+jxc+jcm`, `nq+jxc+jcs`, `nq+jxc+jp+ef`, `nq+jxc+jp+ef+jcr`, `nq+jxc+jxc`, `nq+jxc+jxc+jxt`, `nq+jxc+jxt`, `nq+jxt`, `nq+nbn`, `nq+nbn+jca`, `nq+nbn+jcm`, `nq+nbn+jp+ep+ef`, `nq+ncn`, `nq+ncn+jca`, `nq+ncn+jca+jcm`, `nq+ncn+jca+jxc`, `nq+ncn+jca+jxt`, `nq+ncn+jcc`, `nq+ncn+jcj`, `nq+ncn+jcm`, `nq+ncn+jco`, `nq+ncn+jcs`, `nq+ncn+jct`, `nq+ncn+jct+jcm`, `nq+ncn+jct+jxc`, `nq+ncn+jct+jxt`, `nq+ncn+jp+ef`, `nq+ncn+jp+ep+ef`, `nq+ncn+jp+ep+etm`, `nq+ncn+jp+etm`, `nq+ncn+jxc`, `nq+ncn+jxc+jxt`, `nq+ncn+jxt`, `nq+ncn+ncn`, `nq+ncn+ncn+jca`, `nq+ncn+ncn+jca+jxt`, `nq+ncn+ncn+jcm`, `nq+ncn+ncn+jco`, `nq+ncn+ncn+jp+etm`, `nq+ncn+ncn+jxc`, `nq+ncn+ncn+ncn`, `nq+ncn+ncn+ncn+jca`, `nq+ncn+ncn+ncn+jcs`, `nq+ncn+ncn+xsn+jxt`, `nq+ncn+ncpa+jca`, `nq+ncn+ncpa+jcs`, `nq+ncn+ncpa+jxt`, `nq+ncn+ncpa+ncn`, `nq+ncn+ncpa+ncn+jcm`, `nq+ncn+xsn`, `nq+ncn+xsn+jca`, `nq+ncn+xsn+jca+jxt`, `nq+ncn+xsn+jcm`, `nq+ncn+xsn+jco`, `nq+ncn+xsn+jcs`, `nq+ncn+xsn+jct`, `nq+ncn+xsn+jp+etm`, `nq+ncn+xsn+jxt`, `nq+ncpa`, `nq+ncpa+jca`, `nq+ncpa+jcm`, `nq+ncpa+jco`, `nq+ncpa+jxt`, `nq+ncpa+ncn+jcm`, `nq+ncpa+ncn+jp+ef`, `nq+ncpa+ncn+jp+etm`, `nq+nq`, `nq+nq+jca`, `nq+nq+jcj`, `nq+nq+jcm`, `nq+nq+jcs`, `nq+nq+jct`, `nq+nq+jxc+jcs`, `nq+nq+jxt`, `nq+nq+ncn`, `nq+nq+ncn+jca`, `nq+nq+nq+jxt`, `nq+nq+nq+nq+jcm`, `nq+xsm+ecs`, `nq+xsm+etm`, `nq+xsn`, `nq+xsn+jca`, `nq+xsn+jca+jxc`, `nq+xsn+jca+jxt`, `nq+xsn+jcj`, `nq+xsn+jcm`, `nq+xsn+jco`, `nq+xsn+jcs`, `nq+xsn+jcs+jxt`, `nq+xsn+jct`, `nq+xsn+jct+jcm`, `nq+xsn+jp+ef`, `nq+xsn+jp+ef+jcr`, `nq+xsn+jp+ep+ef`, `nq+xsn+jp+etm`, `nq+xsn+jp+etn+jco`, `nq+xsn+jxc`, `nq+xsn+jxt`, `nq+xsn+xsn`, `nq+xsn+xsn+jcj`, `nq+xsn+xsn+jcs`, `nq+xsn+xsv+ep+etm`, `nq+xsv+ecs`, `paa+ecc`, `paa+ecc+jxc`, `paa+ecc+jxt`, `paa+ecs`, `paa+ecs+etm`, `paa+ecs+jca`, `paa+ecs+jcm`, `paa+ecs+jco`, `paa+ecs+jct`, `paa+ecs+jp+ecc`, `paa+ecs+jp+ep+ef`, `paa+ecs+jxc`, `paa+ecs+jxc+jxt`, `paa+ecs+jxt`, `paa+ecx`, `paa+ecx+jco`, `paa+ecx+jcs`, `paa+ecx+jxc`, `paa+ecx+jxt`, `paa+ecx+px+ecc`, `paa+ecx+px+ecs`, `paa+ecx+px+ecx`, `paa+ecx+px+ecx+jxc`, `paa+ecx+px+ecx+px+ecc`, `paa+ecx+px+ecx+px+ecx`, `paa+ecx+px+ecx+px+ef`, `paa+ecx+px+ecx+px+ep+ef`, `paa+ecx+px+ecx+px+etm`, `paa+ecx+px+ef`, `paa+ecx+px+ef+jcr`, `paa+ecx+px+ep+ecc`, `paa+ecx+px+ep+ecs`, `paa+ecx+px+ep+ef`, `paa+ecx+px+ep+ef+jcr`, `paa+ecx+px+ep+etm`, `paa+ecx+px+ep+etn`, `paa+ecx+px+ep+etn+jco`, `paa+ecx+px+etm`, `paa+ecx+px+etn`, `paa+ecx+px+etn+jca`, `paa+ecx+px+etn+jco`, `paa+ecx+px+etn+jcs`, `paa+ecx+px+etn+jxc`, `paa+ecx+px+etn+jxt`, `paa+ef`, `paa+ef+ecc`, `paa+ef+ecs`, `paa+ef+ecs+jxc`, `paa+ef+jca`, `paa+ef+jcm`, `paa+ef+jco`, `paa+ef+jcr`, `paa+ef+jcr+jxc`, `paa+ef+jcr+jxt`, `paa+ef+jxf`, `paa+ep+ecc`, `paa+ep+ecs`, `paa+ep+ecs+jxc`, `paa+ep+ef`, `paa+ep+ef+jcr`, `paa+ep+ef+jxc`, `paa+ep+ef+jxf`, `paa+ep+ef+jxt`, `paa+ep+ep+ecs`, `paa+ep+ep+ef`, `paa+ep+ep+etm`, `paa+ep+etm`, `paa+ep+etn`, `paa+ep+etn+jca`, `paa+ep+etn+jca+jxc`, `paa+ep+etn+jco`, `paa+ep+etn+jcs`, `paa+ep+etn+jxt`, `paa+etm`, `paa+etn`, `paa+etn+jca`, `paa+etn+jca+jxc`, `paa+etn+jca+jxt`, `paa+etn+jcc`, `paa+etn+jcj`, `paa+etn+jcm`, `paa+etn+jco`, `paa+etn+jcs`, `paa+etn+jct`, `paa+etn+jp+ecc`, `paa+etn+jp+ef`, `paa+etn+jp+ep+ecs`, `paa+etn+jp+ep+ef`, `paa+etn+jxc`, `paa+etn+jxt`, `paa+jxt`, `pad+ecc`, `pad+ecc+jxt`, `pad+ecs`, `pad+ecs+jxc`, `pad+ecs+jxt`, `pad+ecx`, `pad+ecx+jcs`, `pad+ecx+jxc`, `pad+ecx+jxt`, `pad+ecx+px+ecs`, `pad+ecx+px+ecx+px+ecc+jxt`, `pad+ef`, `pad+ef+jcr`, `pad+ef+jcr+jxt`, `pad+ef+jxf`, `pad+ef+jxt`, `pad+ep+ecc`, `pad+ep+ecs`, `pad+ep+ef`, `pad+ep+ef+jco`, `pad+ep+etm`, `pad+etm`, `pad+etn`, `pad+etn+jxt`, `pvd+ecc+jxc`, `pvd+ecs`, `pvd+ecs+jp+ecs`, `pvd+ecs+jxc`, `pvd+ecs+jxt`, `pvd+ecx`, `pvd+ep+ef`, `pvd+ep+etm`, `pvd+etm`, `pvd+etn`, `pvd+etn+jca`, `pvd+etn+jca+jxc`, `pvg+ecc`, `pvg+ecc+jxc`, `pvg+ecc+jxt`, `pvg+ecs`, `pvg+ecs+ecs`, `pvg+ecs+jca`, `pvg+ecs+jca+jxt`, `pvg+ecs+jcc`, `pvg+ecs+jcm`, `pvg+ecs+jco`, `pvg+ecs+jcs`, `pvg+ecs+jct`, `pvg+ecs+jp+ecs`, `pvg+ecs+jp+ef`, `pvg+ecs+jp+ep+ecs`, `pvg+ecs+jp+ep+ef`, `pvg+ecs+jp+ep+ef+jcr`, `pvg+ecs+jxc`, `pvg+ecs+jxc+jcc`, `pvg+ecs+jxc+jp+ef`, `pvg+ecs+jxc+jp+ep+ef`, `pvg+ecs+jxt`, `pvg+ecx`, `pvg+ecx+jco`, `pvg+ecx+jxc`, `pvg+ecx+jxt`, `pvg+ecx+jxt+px+ep+ef`, `pvg+ecx+px+ecc`, `pvg+ecx+px+ecc+jxc`, `pvg+ecx+px+ecc+jxt`, `pvg+ecx+px+ecs`, `pvg+ecx+px+ecs+jxc`, `pvg+ecx+px+ecs+jxt`, `pvg+ecx+px+ecx`, `pvg+ecx+px+ecx+jco`, `pvg+ecx+px+ecx+jxc`, `pvg+ecx+px+ecx+jxt`, `pvg+ecx+px+ecx+px+ecc`, `pvg+ecx+px+ecx+px+ecs`, `pvg+ecx+px+ecx+px+ecs+jxt`, `pvg+ecx+px+ecx+px+ecx`, `pvg+ecx+px+ecx+px+ecx+px+ecc`, `pvg+ecx+px+ecx+px+ef`, `pvg+ecx+px+ecx+px+ep+ecc`, `pvg+ecx+px+ecx+px+ep+ef`, `pvg+ecx+px+ecx+px+ep+etm`, `pvg+ecx+px+ecx+px+ep+etn+jco`, `pvg+ecx+px+ecx+px+etm`, `pvg+ecx+px+ecx+px+etn`, `pvg+ecx+px+ecx+px+etn+jca`, `pvg+ecx+px+ef`, `pvg+ecx+px+ef+jca`, `pvg+ecx+px+ef+jcm`, `pvg+ecx+px+ef+jcr`, `pvg+ecx+px+ep+ecc`, `pvg+ecx+px+ep+ecs`, `pvg+ecx+px+ep+ecs+jxc`, `pvg+ecx+px+ep+ef`, `pvg+ecx+px+ep+ef+jcm`, `pvg+ecx+px+ep+ef+jcr`, `pvg+ecx+px+ep+ef+jxf`, `pvg+ecx+px+ep+ep+ecs`, `pvg+ecx+px+ep+etm`, `pvg+ecx+px+ep+etn`, `pvg+ecx+px+ep+etn+jca`, `pvg+ecx+px+ep+etn+jca+jxc`, `pvg+ecx+px+ep+etn+jco`, `pvg+ecx+px+etm`, `pvg+ecx+px+etn`, `pvg+ecx+px+etn+jca`, `pvg+ecx+px+etn+jca+jxc`, `pvg+ecx+px+etn+jca+jxt`, `pvg+ecx+px+etn+jco`, `pvg+ecx+px+etn+jcs`, `pvg+ecx+px+etn+jct`, `pvg+ecx+px+etn+jxc`, `pvg+ecx+px+etn+jxc+jxt`, `pvg+ecx+px+etn+jxt`, `pvg+ef`, `pvg+ef+jca`, `pvg+ef+jcm`, `pvg+ef+jco`, `pvg+ef+jcr`, `pvg+ef+jcr+jxc`, `pvg+ef+jcr+jxt`, `pvg+ef+jcs`, `pvg+ef+jp+ef+jcr`, `pvg+ef+jp+etm`, `pvg+ef+jxc`, `pvg+ef+jxf`, `pvg+ef+jxt`, `pvg+ep+ecc`, `pvg+ep+ecc+jxt`, `pvg+ep+ecs`, `pvg+ep+ecs+jca+jxt`, `pvg+ep+ecs+jco`, `pvg+ep+ecs+jxc`, `pvg+ep+ecs+jxt`, `pvg+ep+ecx`, `pvg+ep+ecx+px+ef`, `pvg+ep+ef`, `pvg+ep+ef+jca`, `pvg+ep+ef+jcm`, `pvg+ep+ef+jco`, `pvg+ep+ef+jcr`, `pvg+ep+ef+jcr+jxc`, `pvg+ep+ef+jcr+jxt`, `pvg+ep+ef+jct`, `pvg+ep+ef+jxc`, `pvg+ep+ef+jxf`, `pvg+ep+ef+jxt`, `pvg+ep+ep+ef`, `pvg+ep+ep+ef+jco`, `pvg+ep+ep+ef+jxf`, `pvg+ep+etm`, `pvg+ep+etn`, `pvg+ep+etn+jca`, `pvg+ep+etn+jca+jxc`, `pvg+ep+etn+jca+jxt`, `pvg+ep+etn+jco`, `pvg+ep+etn+jcs`, `pvg+ep+etn+jxt`, `pvg+etm`, `pvg+etn`, `pvg+etn+jca`, `pvg+etn+jca+jxc`, `pvg+etn+jca+jxt`, `pvg+etn+jcc`, `pvg+etn+jcj`, `pvg+etn+jcm`, `pvg+etn+jco`, `pvg+etn+jcr`, `pvg+etn+jcs`, `pvg+etn+jct`, `pvg+etn+jct+jxt`, `pvg+etn+jp+ecc`, `pvg+etn+jp+ecs`, `pvg+etn+jp+ef`, `pvg+etn+jp+ef+jcr`, `pvg+etn+jp+ef+jcs`, `pvg+etn+jp+ep+ef`, `pvg+etn+jp+ep+ef+jcr`, `pvg+etn+jp+etm`, `pvg+etn+jxc`, `pvg+etn+jxc+jca+jxt`, `pvg+etn+jxc+jcm`, `pvg+etn+jxc+jco`, `pvg+etn+jxc+jcs`, `pvg+etn+jxc+jxt`, `pvg+etn+jxt`, `pvg+etn+xsm+ecs`, `pvg+etn+xsn+jcm`, `px+ecc`, `px+ecc+jxc`, `px+ecc+jxc+jp+ef`, `px+ecc+jxt`, `px+ecs`, `px+ecs+jca`, `px+ecs+jcc`, `px+ecs+jcj`, `px+ecs+jcm`, `px+ecs+jco`, `px+ecs+jp+ep+ef`, `px+ecs+jxc`, `px+ecs+jxt`, `px+ecx`, `px+ecx+jxc`, `px+ecx+jxt`, `px+ecx+px+ecs`, `px+ecx+px+ecx`, `px+ecx+px+ef`, `px+ecx+px+ef+jcr`, `px+ecx+px+ep+ef`, `px+ecx+px+etm`, `px+ecx+px+etn+jca`, `px+ef`, `px+ef+etm`, `px+ef+jca`, `px+ef+jca+jxc`, `px+ef+jcj`, `px+ef+jcm`, `px+ef+jco`, `px+ef+jcr`, `px+ef+jcr+jxc`, `px+ef+jcs`, `px+ef+jp+etm`, `px+ef+jxc`, `px+ef+jxf`, `px+ef+jxt`, `px+ep+ecc`, `px+ep+ecs`, `px+ep+ecs+jxc`, `px+ep+ecs+jxt`, `px+ep+ecx`, `px+ep+ef`, `px+ep+ef+jca`, `px+ep+ef+jco`, `px+ep+ef+jcr`, `px+ep+ef+jcr+jxc`, `px+ep+ef+jxf`, `px+ep+ep+ef`, `px+ep+ep+ef+jxf`, `px+ep+etm`, `px+ep+etn`, `px+ep+etn+jca`, `px+ep+etn+jca+jxc`, `px+ep+etn+jco`, `px+ep+etn+jcs`, `px+ep+etn+jxc`, `px+ep+etn+jxt`, `px+etm`, `px+etn`, `px+etn+jca`, `px+etn+jca+jxc`, `px+etn+jca+jxt`, `px+etn+jco`, `px+etn+jcs`, `px+etn+jct`, `px+etn+jxc`, `px+etn+jxc+jxt`, `px+etn+jxt`, `sf`, `sl`, `sp`, `sr`, `su`, `su+jca`, `su+jcm`, `xp+nbn`, `xp+nbu`, `xp+ncn`, `xp+ncn+jca`, `xp+ncn+jcm`, `xp+ncn+jco`, `xp+ncn+jcs`, `xp+ncn+jp+ef`, `xp+ncn+jp+ep+ef`, `xp+ncn+jxt`, `xp+ncn+ncn+jca`, `xp+ncn+ncn+jcm`, `xp+ncn+ncn+jco`, `xp+ncn+ncpa+jco`, `xp+ncn+xsn`, `xp+ncn+xsn+jca`, `xp+ncn+xsn+jcm`, `xp+ncn+xsn+jp+ef`, `xp+ncn+xsn+jp+etm`, `xp+ncpa`, `xp+ncpa+jca`, `xp+ncpa+jcm`, `xp+ncpa+jco`, `xp+ncpa+ncn+jcm`, `xp+ncpa+ncn+jco`, `xp+ncpa+ncpa+jco`, `xp+ncpa+xsn`, `xp+ncpa+xsn+jp+etm`, `xp+ncpa+xsv+ecc`, `xp+ncpa+xsv+ecs`, `xp+ncpa+xsv+ecx`, `xp+ncpa+xsv+ef`, `xp+ncpa+xsv+ef+jcr`, `xp+ncpa+xsv+ep+ef`, `xp+ncpa+xsv+etm`, `xp+ncpa+xsv+etn+jca`, `xp+ncps`, `xp+ncps+xsm+ecs`, `xp+ncps+xsm+ecx`, `xp+ncps+xsm+ef`, `xp+ncps+xsm+ep+ef`, `xp+ncps+xsm+etm`, `xp+ncps+xsn`, `xp+nnc`, `xp+nnc+jcm`, `xp+nnc+nbn`, `xp+nnc+nbu`, `xp+nnc+nbu+jcs`, `xp+nnc+ncn`, `xp+nnc+ncn+jca`, `xp+nnc+ncn+jcm`, `xp+nnc+ncn+jcs`, `xp+nnc+ncn+jp+ef+jcr`, `xp+nno`, `xp+nno+jcm`, `xp+nno+nbn+jca`, `xp+nno+nbu`, `xp+nno+nbu+jcs`, `xp+nno+ncn`, `xp+nno+ncn+jca`, `xp+nno+ncn+jcs`, `xp+nno+ncn+jxt`, `xp+nq`, `xp+nq+ncn+jca`, `xp+nq+ncpa`, `xp+nq+ncpa+jco`, `xp+nq+ncpa+jp+etm`, `xsm+etm`, `xsn`, `xsn+jca`, `xsn+jca+jxt`, `xsn+jco`, `xsn+jcs`, `xsn+jp+ef`, `xsn+jp+ep+ef`, `xsn+jxc+jca+jxt`, `xsn+jxc+jcs`, `xsn+jxt`, `xsv+ecc`, `xsv+ecs`, `xsv+ecx+px+ep+ef`, `xsv+ep+ecx`, `xsv+etm` |
| **`morphologizer`** | `POS=CCONJ`, `POS=ADV`, `POS=SCONJ`, `POS=DET`, `POS=NOUN`, `POS=VERB`, `POS=ADJ`, `POS=PUNCT`, `POS=AUX`, `POS=PRON`, `POS=PROPN`, `POS=NUM`, `POS=INTJ`, `POS=PART`, `POS=X`, `POS=ADP`, `POS=SYM` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `3`, `5`, `7`, `9`, `11`, `12`, `16`, `18`, `20`, `22`, `25`, `28`, `31`, `34`, `35`, `36`, `39`, `40`, `43`, `45`, `47`, `48`, `51`, `54`, `56`, `58`, `60`, `61`, `63`, `65`, `67`, `69`, `71`, `73`, `75`, `76`, `78`, `79`, `82`, `85`, `87`, `89`, `92`, `95`, `97`, `99`, `101`, `104`, `106`, `109`, `112`, `114`, `116`, `119`, `121`, `122`, `124`, `126`, `127`, `128`, `130`, `133`, `135`, `137`, `140`, `142`, `145`, `147`, `148`, `150`, `151`, `152`, `155`, `156`, `158`, `161`, `162`, `164`, `167`, `169`, `172`, `174`, `176`, `177`, `179`, `182`, `184`, `186`, `188`, `191`, `192`, `194`, `196`, `199`, `202`, `203`, `173`, `115`, `205`, `207`, `210`, `213`, `216`, `218`, `221`, `146`, `223`, `225`, `227`, `229`, `230`, `231`, `232`, `234`, `236`, `238`, `239`, `242`, `244`, `246`, `248`, `251`, `253`, `255`, `256`, `259`, `261`, `264`, `265`, `268`, `270`, `272`, `274`, `276`, `278`, `279`, `282`, `285`, `287`, `289`, `293`, `295`, `297`, `300`, `302`, `304`, `307`, `309`, `310`, `313`, `315`, `226`, `318`, `319`, `321`, `323`, `325`, `327`, `329`, `332`, `334`, `335`, `337`, `149`, `339`, `340`, `342`, `344`, `346`, `348`, `349`, `350`, `352`, `354`, `356`, `358`, `360`, `361`, `363`, `365`, `369`, `370`, `372`, `374`, `376`, `21`, `15`, `377`, `379`, `382`, `385`, `387`, `388`, `254`, `390`, `393`, `395`, `397`, `399`, `401`, `403`, `404`, `405`, `407`, `408`, `411`, `414`, `417`, `418`, `421`, `422`, `424`, `427`, `429`, `431`, `435`, `437`, `439`, `440`, `442`, `443`, `444`, `447`, `449`, `451`, `389`, `454`, `455`, `457`, `460`, `461`, `463`, `466`, `468`, `471`, `473`, `476`, `477`, `479`, `482`, `296`, `485`, `487`, `490`, `492`, `493`, `495`, `497`, `500`, `502`, `504`, `505`, `507`, `510`, `511`, `514`, `267`, `516`, `520`, `472`, `523`, `525`, `526`, `527`, `530`, `532`, `462`, `533`, `534`, `535`, `537`, `540`, `541`, `465`, `543`, `545`, `546`, `547`, `550`, `551`, `552`, `553`, `555`, `556`, `72`, `558`, `560`, `562`, `563`, `564`, `567`, `568`, `571`, `574`, `577`, `579`, `581`, `582`, `584`, `587`, `589`, `591`, `594`, `595`, `597`, `600`, `603`, `606`, `608`, `610`, `611`, `613`, `614`, `616`, `617`, `620`, `10`, `623`, `626`, `629`, `632`, `633`, `635`, `637`, `638`, `640`, `642`, `644`, `645`, `647`, `648`, `651`, `652`, `653`, `655`, `657`, `659`, `660`, `664`, `666`, `667`, `669`, `672`, `674`, `675`, `676`, `678`, `679`, `680`, `683`, `684`, `687`, `689`, `690`, `692`, `694`, `697`, `699`, `702`, `703`, `706`, `707`, `710`, `713`, `715`, `717`, `719`, `721`, `723`, `725`, `728`, `730`, `733`, `735`, `738`, `740`, `743`, `744`, `649`, `747`, `749`, `753`, `756`, `757`, `759`, `761`, `764`, `767`, `769`, `772`, `774`, `777`, `780`, `783`, `785`, `787`, `789`, `792`, `794`, `797`, `799`, `800`, `802`, `805`, `806`, `808`, `809`, `811`, `812`, `813`, `815`, `817`, `819`, `820`, `59`, `822`, `824`, `827`, `829`, `831`, `618`, `832`, `834`, `836`, `838`, `724`, `841`, `55`, `842`, `844`, `846`, `847`, `850`, `852`, `855`, `857`, `859`, `861`, `863`, `865`, `868`, `869`, `871`, `873`, `874`, `877`, `880`, `884`, `887`, `890`, `891`, `892`, `893`, `896`, `898`, `901`, `351`, `904`, `906`, `908`, `911`, `913`, `915`, `650`, `918`, `920`, `830`, `921`, `923`, `924`, `926`, `927`, `930`, `931`, `934`, `937`, `938`, `940`, `941`, `942`, `945`, `947`, `949`, `952`, `954`, `957`, `960`, `963`, `965`, `967`, `970`, `972`, `974`, `977`, `980`, `981`, `983`, `985`, `986`, `988`, `991`, `994`, `997`, `999`, `1000`, `1002`, `1005`, `1006`, `1007`, `1010`, `125`, `1013`, `1016`, `1017`, `1019`, `1020`, `1024`, `1026`, `1028`, `1030`, `1032`, `1034`, `1036`, `1038`, `1040`, `1041`, `1044`, `1045`, `1048`, `415`, `1051`, `1053`, `1055`, `1056`, `1058`, `1061`, `1063`, `1065`, `1067`, `1068`, `1069`, `1070`, `1074`, `946`, `1077`, `1079`, `1081`, `1083`, `1086`, `1088`, `1089`, `1092`, `936`, `1096`, `1098`, `1101`, `1104`, `1106`, `1108`, `1110`, `1112`, `1114`, `1116`, `1118`, `1119`, `1120`, `1085`, `1123`, `1125`, `1127`, `1031`, `1128`, `1131`, `1124`, `1134`, `1135`, `1137`, `1139`, `1142`, `1144`, `1145`, `1147`, `1150`, `1152`, `1156`, `1158`, `1159`, `1162`, `1164`, `1166`, `1167`, `1170`, `1172`, `1174`, `1176`, `1178`, `1180`, `1181`, `1183`, `1186`, `1187`, `1189`, `1192`, `1195`, `1198`, `1200`, `1201`, `1204`, `1206`, `1208`, `1209`, `763`, `1211`, `1212`, `1214`, `1215`, `1218`, `1220`, `1222`, `1225`, `1226`, `1227`, `1228`, `1230`, `1232`, `1234`, `1236`, `1237`, `1239`, `1241`, `181`, `1244`, `1245`, `1247`, `1249`, `1251`, `1253`, `1256`, `1257`, `1260`, `1261`, `1262`, `1264`, `1267`, `1268`, `1269`, `1272`, `1274`, `1277`, `1280`, `1283`, `1285`, `1287`, `1289`, `1290`, `1294`, `1296`, `1279`, `1298`, `1300`, `1303`, `1304`, `1306`, `1309`, `1311`, `1313`, `1314`, `1317`, `1319`, `1320`, `1324`, `1327`, `1329`, `1332`, `1334`, `1336`, `1338`, `1340`, `1342`, `1344`, `1345`, `303`, `1346`, `1349`, `1350`, `1352`, `1354`, `1356`, `1359`, `362`, `1360`, `1363`, `1365`, `1366`, `1367`, `1369`, `1370`, `1372`, `1374`, `1375`, `1378`, `1380`, `1384`, `1385`, `1389`, `1390`, `1393`, `1395`, `1398`, `1403`, `1404`, `1405`, `1407`, `1410`, `1413`, `1415`, `1418`, `1420`, `1422`, `1423`, `1425`, `1426`, `1428`, `1429`, `1431`, `1433`, `1435`, `1436`, `1438`, `1440`, `1442`, `1444`, `1447`, `1448`, `1449`, `1451`, `1452`, `105`, `1454`, `1456`, `1457`, `1459`, `1462`, `1463`, `1464`, `1466`, `1468`, `1470`, `1471`, `1475`, `810`, `1476`, `1478`, `1480`, `1483`, `1485`, `1487`, `1490`, `1493`, `450`, `1496`, `1498`, `1501`, `1504`, `1506`, `1508`, `1510`, `1513`, `1515`, `1517`, `1520`, `1523`, `1526`, `1529`, `1531`, `1535`, `1536`, `1538`, `1540`, `1542`, `1545`, `1548`, `1550`, `1554`, `1555`, `1558`, `1559`, `1561`, `1563`, `1565`, `1566`, `1568`, `1569`, `1572`, `1574`, `1576`, `1578`, `1580`, `1581`, `1582`, `1585`, `1586`, `1589`, `1591`, `1593`, `1596`, `1597`, `416`, `615`, `1599`, `1601`, `1603`, `1608`, `1611`, `840`, `1613`, `1614`, `1616`, `1618`, `1622`, `1624`, `1627`, `1630`, `1633`, `1636`, `1638`, `1642`, `1645`, `1647`, `1650`, `1653`, `1656`, `1659`, `1661`, `1664`, `1665`, `1668`, `1670`, `1671`, `1674`, `1676`, `1679`, `1680`, `1683`, `1685`, `1687`, `1689`, `1694`, `1697`, `1698`, `1699`, `1700`, `1702`, `1705`, `1706`, `1709`, `1711`, `1712`, `1714`, `1718`, `1720`, `1721`, `1723`, `1725`, `1726`, `1728`, `987`, `506`, `1730`, `1733`, `1735`, `1736`, `1738`, `1740`, `1741`, `1743`, `1745`, `1747`, `1748`, `166`, `1750`, `1752`, `1753`, `1755`, `1758`, `1761`, `1763`, `224`, `1764`, `1767`, `1768`, `1771`, `1773`, `1777`, `1779`, `1783`, `1786`, `1787`, `1791`, `1794`, `1797`, `1798`, `1799`, `1801`, `1804`, `1806`, `1807`, `1809`, `228`, `1810`, `1813`, `1814`, `1817`, `1819`, `1821`, `1824`, `1826`, `1829`, `1831`, `1833`, `1834`, `1835`, `1837`, `1839`, `1637`, `1840`, `1844`, `1846`, `905`, `1850`, `1851`, `1853`, `1855`, `1858`, `1859`, `1861`, `1862`, `1863`, `1866`, `1867`, `1869`, `1873`, `1875`, `1878`, `1879`, `1883`, `1884`, `1887`, `1890`, `1892`, `1895`, `1896`, `1899`, `1901`, `1903`, `1905`, `1907`, `1908`, `1909`, `1910`, `1912`, `1914`, `1917`, `1920`, `1922`, `1924`, `1926`, `1928`, `1929`, `1932`, `1933`, `1935`, `1936`, `1937`, `1940`, `1942`, `1944`, `1946`, `1947`, `1949`, `1952`, `1953`, `1956`, `1959`, `1960`, `1962`, `1964`, `1965`, `1966`, `1967`, `1970`, `1971`, `1972`, `1974`, `1975`, `1976`, `1977`, `1978`, `1979`, `1981`, `1982`, `1983`, `1985`, `1987`, `1991`, `673`, `1992`, `1994`, `1995`, `1997`, `1999`, `2002`, `2003`, `2005`, `2008`, `2010`, `2012`, `2013`, `2015`, `2017`, `2019`, `2020`, `2023`, `2026`, `2027`, `2030`, `2032`, `2034`, `2036`, `2038`, `2040`, `2041`, `2042`, `2045`, `2046`, `2048`, `2049`, `2051`, `2052`, `2053`, `1295`, `2054`, `536`, `2057`, `2059`, `2062`, `2064`, `2066`, `2067`, `2068`, `2072`, `2075`, `2076`, `2078`, `2081`, `2083`, `2085`, `2086`, `2088`, `2090`, `2091`, `2093`, `2096`, `2098`, `2099`, `2102`, `2104`, `2105`, `2107`, `2110`, `2111`, `17`, `2113`, `2116`, `2118`, `2121`, `2123`, `2124`, `2125`, `2127`, `2128`, `2129`, `2131`, `2133`, `2135`, `2137`, `2140`, `2141`, `2143`, `2145`, `2146`, `2147`, `2149`, `2151`, `2154`, `2155`, `2156`, `2159`, `2160`, `2161`, `2162`, `2163`, `2165`, `2168`, `1477`, `2170`, `2171`, `2173`, `2174`, `2175`, `2177`, `2180`, `2181`, `2183`, `2185`, `2187`, `2188`, `2190`, `2193`, `2195`, `2199`, `2202`, `2204`, `2205`, `2207`, `2210`, `2212`, `2213`, `2216`, `338`, `2218`, `2220`, `2222`, `2224`, `2226`, `2229`, `2231`, `2233`, `2236`, `2238`, `2240`, `2243`, `2245`, `2247`, `2248`, `593`, `2250`, `2251`, `2256`, `2258`, `2261`, `2263`, `2264`, `2266`, `2268`, `2271`, `2274`, `2277`, `2278`, `2281`, `2282`, `2284`, `2287`, `2289`, `2292`, `345`, `2294`, `2297`, `2299`, `2301`, `2304`, `2306`, `2308`, `2310`, `2312`, `2315`, `2317`, `2318`, `2321`, `2322`, `2323`, `1663`, `2324`, `2328`, `2331`, `2332`, `2335`, `2337`, `2339`, `2341`, `2344`, `2346`, `2348`, `2350`, `2354`, `2355`, `2359`, `2361`, `2363`, `2366`, `2368`, `2369`, `2372`, `2375`, `2376`, `2380`, `2384`, `2167`, `2385`, `2386`, `2388`, `2391`, `2393`, `2395`, `2397`, `2398`, `2400`, `2403`, `2404`, `2406`, `2410`, `2412`, `2414`, `2416`, `2418`, `1111`, `2420`, `2421`, `2422`, `2425`, `2428`, `2431`, `2433`, `2435`, `2437`, `2438`, `2439`, `2442`, `2445`, `2447`, `2448`, `2450`, `2453`, `2456`, `2459`, `2461`, `2462`, `2463`, `2466`, `2467`, `2470`, `2471`, `2473`, `2476`, `2478`, `2479`, `2482`, `2485`, `2486`, `2488`, `2489`, `2491`, `2494`, `2496`, `2498`, `2501`, `2503`, `2506`, `2507`, `2508`, `2510`, `2512`, `2513`, `2515`, `2517`, `2518`, `2520`, `2522`, `2526`, `2529`, `2531`, `1219`, `2534`, `2536`, `2538`, `2540`, `2542`, `2544`, `2546`, `2547`, `2549`, `2550`, `2552`, `2553`, `2556`, `2559`, `2561`, `2563`, `2565`, `2567`, `2569`, `2571`, `2573`, `2575`, `2577`, `2578`, `2579`, `2580`, `2583`, `2585`, `2587`, `2590`, `2594`, `2596`, `2598`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 100.00 |
| `TOKEN_P` | 100.00 |
| `TOKEN_R` | 100.00 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 100.00 |
| `SENTS_P` | 100.00 |
| `SENTS_R` | 100.00 |
| `TAG_ACC` | 88.93 |
| `POS_ACC` | 96.52 |
| `MORPH_ACC` | 100.00 |
| `MORPH_PER_FEAT` | 0.00 |
| `DEP_UAS` | 89.48 |
| `DEP_LAS` | 87.18 |
| `LEMMA_ACC` | 94.51 |
|
{"language": ["ko"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/ko_udv25_koreankaist_trf
|
[
"spacy",
"token-classification",
"ko",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Korean-Kaist
### Label Scheme
View label scheme (5329 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (5329 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (5329 labels for 6 components)",
"### Accuracy"
] |
[
32,
18,
5
] |
[
"passage: TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (5329 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Lithuanian-ALKSNIS
| Feature | Description |
| --- | --- |
| **Name** | `lt_udv25_lithuanianalksnis_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (3674 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `.`, `akr.`, `bdv.aukšt.mot.dgs.K.`, `bdv.aukšt.mot.dgs.V.`, `bdv.aukšt.mot.dgs.Vt.`, `bdv.aukšt.mot.dgs.Įn.`, `bdv.aukšt.mot.vns.G.`, `bdv.aukšt.mot.vns.K.`, `bdv.aukšt.mot.vns.V.`, `bdv.aukšt.vyr.dgs.G.`, `bdv.aukšt.vyr.dgs.K.`, `bdv.aukšt.vyr.dgs.N.`, `bdv.aukšt.vyr.dgs.V.`, `bdv.aukšt.vyr.dgs.Vt.`, `bdv.aukšt.vyr.dgs.Įn.`, `bdv.aukšt.vyr.vns.G.`, `bdv.aukšt.vyr.vns.K.`, `bdv.aukšt.vyr.vns.N.`, `bdv.aukšt.vyr.vns.V.`, `bdv.aukšt.vyr.vns.Vt.`, `bdv.aukšt.vyr.vns.Įn.`, `bdv.aukšč.bev.`, `bdv.aukšč.mot.dgs.G.`, `bdv.aukšč.mot.dgs.K.`, `bdv.aukšč.mot.dgs.V.`, `bdv.aukšč.mot.dgs.Įn.`, `bdv.aukšč.mot.vns.K.`, `bdv.aukšč.mot.vns.V.`, `bdv.aukšč.mot.vns.Vt.`, `bdv.aukšč.mot.vns.Įn.`, `bdv.aukšč.vyr.dgs.G.`, `bdv.aukšč.vyr.dgs.K.`, `bdv.aukšč.vyr.dgs.V.`, `bdv.aukšč.vyr.dgs.Vt.`, `bdv.aukšč.vyr.dgs.Įn.`, `bdv.aukšč.vyr.vns.G.`, `bdv.aukšč.vyr.vns.K.`, `bdv.aukšč.vyr.vns.V.`, `bdv.aukšč.vyr.vns.Įn.`, `bdv.aukšč.įvardž.mot.vns.K.`, `bdv.nelygin.`, `bdv.nelygin..vyr.vns.K.`, 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`sampl.jng.`, `sampl.jst.`, `sampl.prv.`, `sampl.prv.nelyg.`, `sampl.prv.nelygin.`, `sampl.sktv.`, `sampl.sktv.raid.kiek.`, `sampl.sutr.`, `sampl.užs.`, `sampl.vksm.pad.es.`, `sampl.įv.`, `sampl.įv.G.`, `sampl.įv.K.`, `sampl.įv.V.`, `sampl.įv.bev.`, `sampl.įv.mot.dgs.G.`, `sampl.įv.mot.dgs.K.`, `sampl.įv.mot.dgs.V.`, `sampl.įv.mot.dgs.Vt.`, `sampl.įv.mot.dgs.Įn.`, `sampl.įv.mot.vns.G.`, `sampl.įv.mot.vns.K.`, `sampl.įv.mot.vns.N.`, `sampl.įv.mot.vns.V.`, `sampl.įv.mot.vns.Vt.`, `sampl.įv.mot.vns.Įn.`, `sampl.įv.vyr.dgs.G.`, `sampl.įv.vyr.dgs.K.`, `sampl.įv.vyr.dgs.N.`, `sampl.įv.vyr.dgs.V.`, `sampl.įv.vyr.dgs.Vt.`, `sampl.įv.vyr.dgs.Įn.`, `sampl.įv.vyr.vns.G.`, `sampl.įv.vyr.vns.K.`, `sampl.įv.vyr.vns.V.`, `sampl.įv.vyr.vns.Vt.`, `sampl.įv.vyr.vns.Įn.`, `sampl.įv.Įn.`, `sktv.`, `sktv.arab`, `sktv.arab.`, `sktv.kelint.mot.vns.Vt.`, `sktv.kelint.įvardž.mot.vns.V.`, `sktv.kelint.įvardž.vyr.vns.G.`, `sktv.kiek.mot.V.`, `sktv.kiek.vyr.dgs.G.`, `sktv.mišr.`, `sktv.mišr.kelint.įvardž.mot.vns.G.`, `sktv.mišr.kelint.įvardž.mot.vns.K.`, `sktv.mišr.kelint.įvardž.mot.vns.V.`, `sktv.mišr.kelint.įvardž.vyr.vns.G.`, `sktv.mišr.kelint.įvardž.vyr.vns.K.`, `sktv.mišr.kelint.įvardž.vyr.vns.Vt.`, `sktv.raid.daugin.vyr.G.`, `sktv.raid.daugin.vyr.K.`, `sktv.raid.kelint.bev.`, `sktv.raid.kelint.mot.vns.K.`, `sktv.raid.kelint.mot.vns.V.`, `sktv.raid.kelint.mot.vns.Vt.`, `sktv.raid.kelint.vyr.dgs.K.`, `sktv.raid.kelint.vyr.dgs.V.`, `sktv.raid.kelint.vyr.dgs.Vt.`, `sktv.raid.kelint.vyr.dgs.Įn.`, `sktv.raid.kelint.vyr.vns.G.`, `sktv.raid.kelint.vyr.vns.K.`, `sktv.raid.kelint.vyr.vns.V.`, `sktv.raid.kelint.vyr.vns.Vt.`, `sktv.raid.kelint.įvardž.mot.vns.G.`, `sktv.raid.kelint.įvardž.mot.vns.K.`, `sktv.raid.kelint.įvardž.mot.vns.N.`, `sktv.raid.kelint.įvardž.mot.vns.V.`, `sktv.raid.kelint.įvardž.mot.vns.Vt.`, `sktv.raid.kelint.įvardž.vyr.dgs.K.`, `sktv.raid.kelint.įvardž.vyr.dgs.N.`, `sktv.raid.kelint.įvardž.vyr.dgs.V.`, `sktv.raid.kelint.įvardž.vyr.dgs.Įn.`, `sktv.raid.kelint.įvardž.vyr.vns.G.`, `sktv.raid.kelint.įvardž.vyr.vns.K.`, `sktv.raid.kelint.įvardž.vyr.vns.V.`, `sktv.raid.kiek.`, `sktv.raid.kiek.K.`, `sktv.raid.kiek.mot.G.`, `sktv.raid.kiek.mot.K.`, `sktv.raid.kiek.mot.N.`, `sktv.raid.kiek.mot.V.`, `sktv.raid.kiek.mot.Vt.`, `sktv.raid.kiek.mot.dgs.V.`, `sktv.raid.kiek.mot.vns.G.`, `sktv.raid.kiek.mot.vns.K.`, `sktv.raid.kiek.mot.vns.Įn.`, `sktv.raid.kiek.mot.Įn.`, `sktv.raid.kiek.vyr.G.`, `sktv.raid.kiek.vyr.K.`, `sktv.raid.kiek.vyr.N.`, `sktv.raid.kiek.vyr.V.`, `sktv.raid.kiek.vyr.Vt.`, `sktv.raid.kiek.vyr.dgs.K.`, `sktv.raid.kiek.vyr.dgs.V.`, `sktv.raid.kiek.vyr.vns.G.`, `sktv.raid.kiek.vyr.vns.K.`, `sktv.raid.kiek.vyr.vns.V.`, `sktv.raid.kiek.vyr.Įn.`, `sktv.raid.kiekin.mot.vns.G.`, `sktv.raid.kiekin.mot.vns.V.`, `sktv.raid.kuopin.G.`, `sktv.rom.`, `skyr.`, `sutr.`, `tęs`, `tęs.`, `tęs.sktv.raid.kelint.vyr.vns.G.`, `tęs.įv.vyr.dgs.G.`, `tęs.įv.vyr.dgs.N.`, `tęs.įv.vyr.vns.G.`, `tęs.įv.vyr.vns.N.`, `tęs.įv.vyr.vns.V.`, `tęs.įv.vyr.vns.Įn.`, `užs.`, `vksm.asm.liep.dgs.1.`, `vksm.asm.liep.dgs.2.`, `vksm.asm.liep.vns.2.`, `vksm.asm.liep.vns.3.`, `vksm.asm.neig.liep.dgs.2.`, `vksm.asm.neig.liep.vns.2.`, `vksm.asm.neig.sngr.liep.dgs.2.`, `vksm.asm.neig.sngr.tar.3.`, `vksm.asm.neig.sngr.tar.dgs.1.`, `vksm.asm.neig.sngr.tar.vns.1.`, `vksm.asm.neig.sngr.tar.vns.3.`, `vksm.asm.neig.sngr.tiesiog.būs.vns.2.`, `vksm.asm.neig.sngr.tiesiog.būs.vns.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.dgs.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.vns.1.`, `vksm.asm.neig.sngr.tiesiog.būt-k.vns.3.`, `vksm.asm.neig.sngr.tiesiog.es.3.`, `vksm.asm.neig.sngr.tiesiog.es.dgs.3.`, `vksm.asm.neig.sngr.tiesiog.es.vns.1.`, `vksm.asm.neig.sngr.tiesiog.es.vns.3.`, `vksm.asm.neig.tar.3.`, `vksm.asm.neig.tar.dgs.1.`, `vksm.asm.neig.tar.dgs.3.`, `vksm.asm.neig.tar.vns.1.`, `vksm.asm.neig.tar.vns.2.`, `vksm.asm.neig.tar.vns.3.`, `vksm.asm.neig.tiesiog.būs.3.`, `vksm.asm.neig.tiesiog.būs.dgs.1.`, `vksm.asm.neig.tiesiog.būs.dgs.3.`, `vksm.asm.neig.tiesiog.būs.vns.1.`, `vksm.asm.neig.tiesiog.būs.vns.2.`, `vksm.asm.neig.tiesiog.būs.vns.3.`, `vksm.asm.neig.tiesiog.būt-d.vns.1.`, `vksm.asm.neig.tiesiog.būt-d.vns.3.`, `vksm.asm.neig.tiesiog.būt-k.3.`, `vksm.asm.neig.tiesiog.būt-k.dgs.1.`, `vksm.asm.neig.tiesiog.būt-k.dgs.3.`, `vksm.asm.neig.tiesiog.būt-k.vns.1.`, `vksm.asm.neig.tiesiog.būt-k.vns.2.`, `vksm.asm.neig.tiesiog.būt-k.vns.3.`, `vksm.asm.neig.tiesiog.es.3.`, `vksm.asm.neig.tiesiog.es.dgs.1.`, `vksm.asm.neig.tiesiog.es.dgs.2.`, `vksm.asm.neig.tiesiog.es.dgs.3.`, `vksm.asm.neig.tiesiog.es.vns.1.`, `vksm.asm.neig.tiesiog.es.vns.2.`, `vksm.asm.neig.tiesiog.es.vns.3.`, `vksm.asm.sngr.liep.dgs.1.`, `vksm.asm.sngr.liep.dgs.2.`, `vksm.asm.sngr.liep.vns.2.`, `vksm.asm.sngr.tar.3.`, `vksm.asm.sngr.tar.dgs.3.`, `vksm.asm.sngr.tar.vns.1.`, `vksm.asm.sngr.tar.vns.3.`, `vksm.asm.sngr.tiesiog.būs.dgs.1.`, `vksm.asm.sngr.tiesiog.būs.dgs.2.`, `vksm.asm.sngr.tiesiog.būs.dgs.3.`, `vksm.asm.sngr.tiesiog.būs.vns.2.`, `vksm.asm.sngr.tiesiog.būs.vns.3.`, `vksm.asm.sngr.tiesiog.būt-d.dgs.3.`, `vksm.asm.sngr.tiesiog.būt-d.vns.1.`, `vksm.asm.sngr.tiesiog.būt-d.vns.3.`, `vksm.asm.sngr.tiesiog.būt-k.3.`, `vksm.asm.sngr.tiesiog.būt-k.dgs.1.`, `vksm.asm.sngr.tiesiog.būt-k.dgs.3.`, `vksm.asm.sngr.tiesiog.būt-k.vns.1.`, `vksm.asm.sngr.tiesiog.būt-k.vns.3.`, `vksm.asm.sngr.tiesiog.es.3.`, `vksm.asm.sngr.tiesiog.es.dgs.1.`, `vksm.asm.sngr.tiesiog.es.dgs.3.`, `vksm.asm.sngr.tiesiog.es.vns.1.`, `vksm.asm.sngr.tiesiog.es.vns.2.`, `vksm.asm.sngr.tiesiog.es.vns.3.`, `vksm.asm.tar.3.`, `vksm.asm.tar.dgs.1.`, `vksm.asm.tar.dgs.2.`, `vksm.asm.tar.dgs.3.`, `vksm.asm.tar.vns.1.`, `vksm.asm.tar.vns.2.`, `vksm.asm.tar.vns.3.`, `vksm.asm.tiesiog.būs.3.`, `vksm.asm.tiesiog.būs.dgs.1.`, `vksm.asm.tiesiog.būs.dgs.2.`, `vksm.asm.tiesiog.būs.dgs.3.`, `vksm.asm.tiesiog.būs.vns.1.`, `vksm.asm.tiesiog.būs.vns.2.`, `vksm.asm.tiesiog.būs.vns.3.`, `vksm.asm.tiesiog.būt-d.3.`, `vksm.asm.tiesiog.būt-d.dgs.3.`, `vksm.asm.tiesiog.būt-d.vns.1.`, `vksm.asm.tiesiog.būt-d.vns.2.`, `vksm.asm.tiesiog.būt-d.vns.3.`, `vksm.asm.tiesiog.būt-k.`, `vksm.asm.tiesiog.būt-k.3.`, `vksm.asm.tiesiog.būt-k.dgs.1.`, `vksm.asm.tiesiog.būt-k.dgs.2.`, `vksm.asm.tiesiog.būt-k.dgs.3.`, `vksm.asm.tiesiog.būt-k.vns.1.`, `vksm.asm.tiesiog.būt-k.vns.2.`, `vksm.asm.tiesiog.būt-k.vns.3.`, `vksm.asm.tiesiog.es.3.`, `vksm.asm.tiesiog.es.dgs.1.`, `vksm.asm.tiesiog.es.dgs.2.`, `vksm.asm.tiesiog.es.dgs.3.`, `vksm.asm.tiesiog.es.vns.1.`, `vksm.asm.tiesiog.es.vns.2.`, `vksm.asm.tiesiog.es.vns.3.`, `vksm.bndr.`, `vksm.bndr.neig.`, `vksm.bndr.neig.sngr.`, `vksm.bndr.sngr.`, `vksm.dlv.neig.neveik.būt.bev.`, `vksm.dlv.neig.neveik.būt.mot.dgs.G.`, `vksm.dlv.neig.neveik.būt.mot.dgs.K.`, `vksm.dlv.neig.neveik.būt.mot.dgs.V.`, `vksm.dlv.neig.neveik.būt.mot.vns.K.`, `vksm.dlv.neig.neveik.būt.mot.vns.V.`, `vksm.dlv.neig.neveik.būt.vyr.dgs.N.`, `vksm.dlv.neig.neveik.būt.vyr.dgs.V.`, `vksm.dlv.neig.neveik.būt.vyr.vns.G.`, `vksm.dlv.neig.neveik.būt.vyr.vns.N.`, `vksm.dlv.neig.neveik.būt.vyr.vns.V.`, `vksm.dlv.neig.neveik.es.bev.`, `vksm.dlv.neig.neveik.es.mot.dgs.K.`, `vksm.dlv.neig.neveik.es.mot.dgs.V.`, `vksm.dlv.neig.neveik.es.mot.vns.G.`, `vksm.dlv.neig.neveik.es.mot.vns.K.`, `vksm.dlv.neig.neveik.es.mot.vns.V.`, `vksm.dlv.neig.neveik.es.mot.vns.Įn.`, `vksm.dlv.neig.neveik.es.vyr.dgs.G.`, `vksm.dlv.neig.neveik.es.vyr.dgs.K.`, `vksm.dlv.neig.neveik.es.vyr.dgs.V.`, `vksm.dlv.neig.neveik.es.vyr.vns.V.`, `vksm.dlv.neig.neveik.es.įvardž.mot.dgs.V.`, `vksm.dlv.neig.reik.bev.`, `vksm.dlv.neig.reik.mot.dgs.K.`, `vksm.dlv.neig.reik.mot.vns.V.`, `vksm.dlv.neig.reik.vyr.vns.V.`, `vksm.dlv.neig.sngr.neveik.būt.bev.`, `vksm.dlv.neig.sngr.neveik.es.bev.`, `vksm.dlv.neig.sngr.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.neig.sngr.veik.es.vyr.vns.V.`, `vksm.dlv.neig.veik.būt-k.bev.`, `vksm.dlv.neig.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.neig.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.neig.veik.būt-k.vyr.vns.G.`, `vksm.dlv.neig.veik.būt-k.vyr.vns.V.`, `vksm.dlv.neig.veik.es.mot.dgs.K.`, `vksm.dlv.neig.veik.es.mot.vns.N.`, `vksm.dlv.neig.veik.es.mot.vns.V.`, `vksm.dlv.neig.veik.es.mot.vns.Įn.`, `vksm.dlv.neig.veik.es.vyr.dgs.G.`, `vksm.dlv.neig.veik.es.vyr.dgs.N.`, `vksm.dlv.neig.veik.es.vyr.dgs.V.`, `vksm.dlv.neig.veik.es.vyr.dgs.Įn.`, `vksm.dlv.neig.veik.es.vyr.vns.K.`, `vksm.dlv.neig.veik.es.vyr.vns.N.`, `vksm.dlv.neig.veik.es.vyr.vns.V.`, `vksm.dlv.neig.veik.es.įvardž.vyr.dgs.V.`, `vksm.dlv.neig.veik.es.įvardž.vyr.dgs.Įn.`, `vksm.dlv.neveik.būs.vyr.vns.G.`, `vksm.dlv.neveik.būs.vyr.vns.N.`, `vksm.dlv.neveik.būt-k.vyr.dgs.V.`, `vksm.dlv.neveik.būt-k.vyr.vns.V.`, `vksm.dlv.neveik.būt.bev.`, `vksm.dlv.neveik.būt.mot.V.`, `vksm.dlv.neveik.būt.mot.dgs.G.`, `vksm.dlv.neveik.būt.mot.dgs.K`, `vksm.dlv.neveik.būt.mot.dgs.K.`, `vksm.dlv.neveik.būt.mot.dgs.N.`, `vksm.dlv.neveik.būt.mot.dgs.V.`, `vksm.dlv.neveik.būt.mot.dgs.Įn.`, `vksm.dlv.neveik.būt.mot.vns.G.`, `vksm.dlv.neveik.būt.mot.vns.K.`, `vksm.dlv.neveik.būt.mot.vns.N.`, `vksm.dlv.neveik.būt.mot.vns.V`, `vksm.dlv.neveik.būt.mot.vns.V.`, `vksm.dlv.neveik.būt.mot.vns.Vt.`, `vksm.dlv.neveik.būt.mot.vns.Įn.`, `vksm.dlv.neveik.būt.vyr.dgs.G.`, `vksm.dlv.neveik.būt.vyr.dgs.K.`, `vksm.dlv.neveik.būt.vyr.dgs.N.`, `vksm.dlv.neveik.būt.vyr.dgs.V`, `vksm.dlv.neveik.būt.vyr.dgs.V.`, `vksm.dlv.neveik.būt.vyr.dgs.Vt.`, `vksm.dlv.neveik.būt.vyr.dgs.Įn.`, `vksm.dlv.neveik.būt.vyr.vns.G.`, `vksm.dlv.neveik.būt.vyr.vns.K.`, `vksm.dlv.neveik.būt.vyr.vns.N.`, `vksm.dlv.neveik.būt.vyr.vns.V`, `vksm.dlv.neveik.būt.vyr.vns.V.`, `vksm.dlv.neveik.būt.vyr.vns.Vt.`, `vksm.dlv.neveik.būt.vyr.vns.Įn.`, `vksm.dlv.neveik.būt.įvardž.mot.dgs.G.`, `vksm.dlv.neveik.būt.įvardž.mot.dgs.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.G.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.V.`, `vksm.dlv.neveik.būt.įvardž.vyr.vns.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.vns.V.`, `vksm.dlv.neveik.būts.vyr.dgs.V.`, `vksm.dlv.neveik.es.bev.`, `vksm.dlv.neveik.es.mot.V.`, `vksm.dlv.neveik.es.mot.dgs.G.`, `vksm.dlv.neveik.es.mot.dgs.K.`, `vksm.dlv.neveik.es.mot.dgs.N.`, `vksm.dlv.neveik.es.mot.dgs.V.`, `vksm.dlv.neveik.es.mot.dgs.Vt.`, `vksm.dlv.neveik.es.mot.dgs.Įn.`, `vksm.dlv.neveik.es.mot.vns.G.`, `vksm.dlv.neveik.es.mot.vns.K.`, `vksm.dlv.neveik.es.mot.vns.N.`, `vksm.dlv.neveik.es.mot.vns.V`, `vksm.dlv.neveik.es.mot.vns.V.`, `vksm.dlv.neveik.es.mot.vns.Vt.`, `vksm.dlv.neveik.es.mot.vns.Įn.`, `vksm.dlv.neveik.es.vyr.dgs.G.`, `vksm.dlv.neveik.es.vyr.dgs.K.`, `vksm.dlv.neveik.es.vyr.dgs.N.`, `vksm.dlv.neveik.es.vyr.dgs.V.`, `vksm.dlv.neveik.es.vyr.dgs.Įn.`, `vksm.dlv.neveik.es.vyr.vns.G.`, `vksm.dlv.neveik.es.vyr.vns.K.`, `vksm.dlv.neveik.es.vyr.vns.N.`, `vksm.dlv.neveik.es.vyr.vns.V.`, `vksm.dlv.neveik.es.vyr.vns.Įn.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.K.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.V.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.Įn.`, `vksm.dlv.neveik.es.įvardž.mot.vns.G.`, `vksm.dlv.neveik.es.įvardž.mot.vns.K.`, `vksm.dlv.neveik.es.įvardž.mot.vns.N.`, `vksm.dlv.neveik.es.įvardž.mot.vns.V.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.G.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.K.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.N.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.V.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.G.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.K.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.N.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.V.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.Įn.`, `vksm.dlv.neveik.mot.vns.V.`, `vksm.dlv.neveik.vyr.dgs.K.`, `vksm.dlv.neveik.įvardž.es.mot.vns.Vt.`, `vksm.dlv.neveik.įvardž.es.vyr.dgs.K.`, `vksm.dlv.neveik.įvardž.es.vyr.vns.K.`, `vksm.dlv.reik.bev.`, `vksm.dlv.reik.mot.vns.V.`, `vksm.dlv.reik.vyr.dgs.K.`, `vksm.dlv.reik.vyr.dgs.V.`, `vksm.dlv.reik.vyr.vns.V.`, `vksm.dlv.sngr.neveik.būt.bev.`, `vksm.dlv.sngr.neveik.būt.mot.dgs.G.`, `vksm.dlv.sngr.neveik.būt.mot.dgs.V.`, `vksm.dlv.sngr.neveik.būt.mot.vns.V.`, `vksm.dlv.sngr.neveik.būt.mot.vns.Vt.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.G.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.V.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.Vt.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.Įn.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.G.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.K.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.V.`, `vksm.dlv.sngr.neveik.es.bev.`, `vksm.dlv.sngr.neveik.es.mot.dgs.V.`, `vksm.dlv.sngr.neveik.es.mot.vns.V.`, `vksm.dlv.sngr.neveik.es.vyr.dgs.Įn.`, `vksm.dlv.sngr.neveik.es.vyr.vns.V.`, `vksm.dlv.sngr.veik.būt-k.bev.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.G.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.K.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.V.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.Įn.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.G.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.K.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.V.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.Įn.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.G.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.K.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.G.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.K.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.V.`, `vksm.dlv.sngr.veik.es.mot.dgs.K.`, `vksm.dlv.sngr.veik.es.mot.dgs.V.`, `vksm.dlv.sngr.veik.es.mot.dgs.Įn.`, `vksm.dlv.sngr.veik.es.mot.vns.K.`, `vksm.dlv.sngr.veik.es.vyr.dgs.G.`, `vksm.dlv.sngr.veik.es.vyr.dgs.K.`, `vksm.dlv.sngr.veik.es.vyr.dgs.N.`, `vksm.dlv.sngr.veik.es.vyr.dgs.V.`, `vksm.dlv.sngr.veik.es.vyr.vns.G.`, `vksm.dlv.sngr.veik.es.vyr.vns.K.`, `vksm.dlv.sngr.veik.es.vyr.vns.N.`, `vksm.dlv.sngr.veik.es.vyr.vns.V.`, `vksm.dlv.sngr.veik.es.įvardž.mot.vns.K.`, `vksm.dlv.veik.būs.vyr.vns.V.`, `vksm.dlv.veik.būt-k.bev.`, `vksm.dlv.veik.būt-k.mot.dgs.G.`, `vksm.dlv.veik.būt-k.mot.dgs.K.`, `vksm.dlv.veik.būt-k.mot.dgs.N.`, `vksm.dlv.veik.būt-k.mot.dgs.V.`, `vksm.dlv.veik.būt-k.mot.dgs.Vt.`, `vksm.dlv.veik.būt-k.mot.vns.G.`, `vksm.dlv.veik.būt-k.mot.vns.K.`, `vksm.dlv.veik.būt-k.mot.vns.N.`, `vksm.dlv.veik.būt-k.mot.vns.V.`, `vksm.dlv.veik.būt-k.mot.vns.Įn.`, `vksm.dlv.veik.būt-k.vyr.dgs.G.`, `vksm.dlv.veik.būt-k.vyr.dgs.K.`, `vksm.dlv.veik.būt-k.vyr.dgs.N.`, `vksm.dlv.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.veik.būt-k.vyr.vns.G.`, `vksm.dlv.veik.būt-k.vyr.vns.K.`, `vksm.dlv.veik.būt-k.vyr.vns.N.`, `vksm.dlv.veik.būt-k.vyr.vns.V.`, `vksm.dlv.veik.būt-k.vyr.vns.Vt.`, `vksm.dlv.veik.būt-k.vyr.vns.Įn.`, `vksm.dlv.veik.būt-k.įvardž.vyr.dgs.K.`, `vksm.dlv.veik.būt-k.įvardž.vyr.dgs.V.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.K.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.V.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.Įn.`, `vksm.dlv.veik.būt.k.vyr.dgs.V.`, `vksm.dlv.veik.es.mot.dgs.G.`, `vksm.dlv.veik.es.mot.dgs.K.`, `vksm.dlv.veik.es.mot.dgs.N.`, `vksm.dlv.veik.es.mot.dgs.V.`, `vksm.dlv.veik.es.mot.dgs.Vt.`, `vksm.dlv.veik.es.mot.dgs.Įn.`, `vksm.dlv.veik.es.mot.vns.G.`, `vksm.dlv.veik.es.mot.vns.K.`, `vksm.dlv.veik.es.mot.vns.N.`, `vksm.dlv.veik.es.mot.vns.V`, `vksm.dlv.veik.es.mot.vns.V.`, `vksm.dlv.veik.es.mot.vns.Vt.`, `vksm.dlv.veik.es.mot.vns.Įn.`, `vksm.dlv.veik.es.vyr.dgs.G.`, `vksm.dlv.veik.es.vyr.dgs.K.`, `vksm.dlv.veik.es.vyr.dgs.N.`, `vksm.dlv.veik.es.vyr.dgs.V.`, `vksm.dlv.veik.es.vyr.dgs.Vt.`, `vksm.dlv.veik.es.vyr.dgs.Įn.`, `vksm.dlv.veik.es.vyr.vns.G.`, `vksm.dlv.veik.es.vyr.vns.K.`, `vksm.dlv.veik.es.vyr.vns.N.`, `vksm.dlv.veik.es.vyr.vns.V.`, `vksm.dlv.veik.es.vyr.vns.Vt.`, `vksm.dlv.veik.es.vyr.vns.Įn.`, `vksm.dlv.veik.es.įvardž.mot.vns.K.`, `vksm.dlv.veik.es.įvardž.mot.vns.V.`, `vksm.dlv.veik.es.įvardž.vyr.dgs.K.`, `vksm.dlv.veik.es.įvardž.vyr.vns.K.`, `vksm.dlv.veik.es.įvardž.vyr.vns.N.`, `vksm.neig.dlv.neveik.es.mot.vns.V.`, `vksm.neveik.būt.vyr.dgs.V.`, `vksm.pad.būt-k.`, `vksm.pad.es.`, `vksm.pad.es.sngr.`, `vksm.pad.neig.būt-k.`, `vksm.pad.neig.es.`, `vksm.pad.neig.sngr.būt-k.`, `vksm.pad.neig.sngr.es.`, `vksm.pad.sngr.būt-k.`, `vksm.pad.sngr.es.`, `vksm.padlv.sngr.es.`, `vksm.pusd.mot.dgs.`, `vksm.pusd.mot.vns.`, `vksm.pusd.neig.mot.vns.`, `vksm.pusd.neig.vyr.dgs.`, `vksm.pusd.neig.vyr.vns.`, `vksm.pusd.sngr.mot.dgs.`, `vksm.pusd.sngr.mot.vns.`, `vksm.pusd.sngr.vyr.dgs.`, `vksm.pusd.sngr.vyr.vns.`, `vksm.pusd.vyr.dgs.`, `vksm.pusd.vyr.vns.`, `vksm.sngr.pad.es.`, `įv.G.`, `įv.K.`, `įv.N.`, `įv.V.`, `įv.bev.`, `įv.dgs.G.`, `įv.dgs.K.`, `įv.dgs.N.`, `įv.dgs.V.`, `įv.dgs.Vt.`, `įv.dgs.Įn.`, `įv.dvisk.V.`, `įv.mot.G.`, `įv.mot.K.`, `įv.mot.V.`, `įv.mot.dgs.G.`, `įv.mot.dgs.K.`, `įv.mot.dgs.N.`, `įv.mot.dgs.V.`, `įv.mot.dgs.Vt.`, `įv.mot.dgs.Įn.`, `įv.mot.dvisk.N.`, `įv.mot.dvisk.V.`, `įv.mot.vns.G.`, `įv.mot.vns.K.`, `įv.mot.vns.N.`, `įv.mot.vns.V.`, `įv.mot.vns.Vt.`, `įv.mot.vns.Įn.`, `įv.vns.G.`, `įv.vns.K.`, `įv.vns.N.`, `įv.vns.V.`, `įv.vns.Vt.`, `įv.vns.Įn.`, `įv.vyr.G.`, `įv.vyr.K.`, `įv.vyr.N.`, `įv.vyr.V.`, `įv.vyr.dgs.G.`, `įv.vyr.dgs.K.`, `įv.vyr.dgs.N.`, `įv.vyr.dgs.V.`, `įv.vyr.dgs.Vt.`, `įv.vyr.dgs.Įn.`, `įv.vyr.dvisk.G.`, `įv.vyr.dvisk.K.`, `įv.vyr.dvisk.V.`, `įv.vyr.vns.G.`, `įv.vyr.vns.K.`, `įv.vyr.vns.N.`, `įv.vyr.vns.V.`, `įv.vyr.vns.Vt.`, `įv.vyr.vns.Įn.`, `įv.vyr.Įn,`, `įv.Įn.`, `įv.įvardž.bev.`, `įv.įvardž.mot.vns.K.`, `įv.įvardž.mot.vns.V.` |
| **`morphologizer`** | `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `POS=PUNCT`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=X`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Hyph=Yes\|POS=ADV`, `Hyph=Yes\|POS=X`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SCONJ`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Ind`, `POS=PART`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Ger`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|NumForm=Digit\|POS=NUM`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Hyph=Yes\|POS=PART`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|Case=Acc\|POS=ADP`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|NumForm=Roman\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Degree=Sup\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `AdpType=Prep\|Case=Ins\|POS=ADP`, `Case=Gen\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=INTJ`, `Definite=Ind\|Gender=Neut\|NumForm=Word\|NumType=Ord\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Hyph=Yes\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Hyph=Yes\|POS=SCONJ`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Ger`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Ger`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Mood=Cnd\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Hyph=Yes\|POS=ADV`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|NumForm=Word\|NumType=Card\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Foreign=Yes\|POS=X`, `Case=Acc\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PROPN`, `Aspect=Perf\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Ger`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Ger`, `Case=Nom\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Definite=Ind\|Hyph=Yes\|POS=NUM`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Ger`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Hyph=Yes\|POS=CCONJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Ger`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Dual\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Pos\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Definite=Def\|Gender=Neut\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Dual\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Foreign=Yes\|Hyph=Yes\|POS=X`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Sup\|Gender=Neut\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=SYM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Definite=Ind\|NumForm=Combi\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Acc\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Definite=Ind\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Gen\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Hyph=Yes\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `POS=VERB\|Polarity=Neg\|VerbForm=Inf`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Ger`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Aspect=Perf\|Case=Ins\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres`, `Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Loc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Inf`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ill\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Abbr=Yes\|Hyph=Yes\|POS=X`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Peri`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Gender=Fem\|POS=PROPN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Dat\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|Mood=Nec\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Aspect=Hab\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Hyph=Yes\|POS=INTJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Com\|Number=Sing\|POS=NOUN`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|NumForm=Word\|NumType=Sets\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Mult\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Loc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|NumForm=Word\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `flat`, `flat:foreign`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `nummod:gov`, `obj`, `obl`, `obl:arg`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `2`, `3`, `5`, `7`, `9`, `12`, `16`, `18`, `19`, `21`, `24`, `26`, `30`, `32`, `34`, `37`, `39`, `41`, `43`, `44`, `46`, `48`, `50`, `52`, `55`, `59`, `62`, `64`, `66`, `68`, `70`, `72`, `74`, `75`, `77`, `79`, `81`, `84`, `86`, `88`, `90`, `92`, `94`, `96`, `98`, `101`, `103`, `105`, `107`, `109`, `110`, `111`, `113`, `115`, `117`, `119`, `121`, `123`, `125`, `127`, `129`, `131`, `133`, `135`, `137`, `139`, `142`, `146`, `148`, `151`, `153`, `155`, `158`, `162`, `165`, `167`, `168`, `170`, `173`, `175`, `177`, `180`, `182`, `184`, `185`, `187`, `189`, `190`, `194`, `195`, `196`, `197`, `200`, `202`, `204`, `205`, `206`, `207`, `208`, `209`, `211`, `213`, `216`, `217`, `219`, `220`, `222`, `224`, `225`, `227`, `231`, `234`, `238`, `242`, `246`, `249`, `251`, `252`, `255`, `258`, `261`, `263`, `265`, `267`, `269`, `272`, `274`, `276`, `278`, `281`, `284`, `285`, `287`, `289`, `292`, `294`, `295`, `297`, `299`, `301`, `303`, `306`, `308`, `310`, `313`, `314`, `317`, `319`, `323`, `325`, `328`, `331`, `333`, `336`, `339`, `341`, `344`, `346`, `350`, `353`, `356`, `359`, `360`, `363`, `366`, `368`, `371`, `374`, `376`, `378`, `380`, `382`, `384`, `385`, `387`, `389`, `390`, `391`, `393`, `395`, `397`, `402`, `403`, `404`, `406`, `408`, `409`, `413`, `415`, `417`, `419`, `420`, `423`, `424`, `426`, `429`, `432`, `434`, `436`, `439`, `442`, `445`, `447`, `448`, `450`, `452`, `455`, `456`, `458`, `460`, `463`, `465`, `468`, `472`, `475`, `477`, `480`, `482`, `483`, `485`, `487`, `488`, `489`, `491`, `492`, `494`, `496`, `497`, `500`, `501`, `502`, `504`, `505`, `506`, `508`, `509`, `513`, `515`, `518`, `519`, `521`, `522`, `523`, `525`, `527`, `529`, `533`, `535`, `538`, `541`, `542`, `545`, `547`, `550`, `552`, `554`, `555`, `557`, `560`, `561`, `563`, `566`, `569`, `572`, `574`, `577`, `580`, `582`, `584`, `589`, `594`, `596`, `599`, `600`, `602`, `604`, `607`, `609`, `611`, `613`, `615`, `616`, `619`, `623`, `625`, `628`, `629`, `631`, `633`, `635`, `638`, `640`, `642`, `645`, `647`, `649`, `653`, `655`, `658`, `660`, `661`, `663`, `665`, `666`, `668`, `670`, `671`, `672`, `673`, `675`, `678`, `679`, `681`, `683`, `685`, `688`, `691`, `693`, `697`, `699`, `700`, `702`, `703`, `704`, `705`, `706`, `707`, `709`, `714`, `715`, `717`, `719`, `721`, `722`, `725`, `726`, `728`, `730`, `732`, `735`, `738`, `739`, `741`, `742`, `743`, `746`, `748`, `750`, `754`, `755`, `757`, `759`, `761`, `762`, `765`, `768`, `770`, `773`, `774`, `777`, `781`, `784`, `785`, `788`, `791`, `793`, `795`, `796`, `799`, `801`, `803`, `805`, `807`, `808`, `811`, `813`, `814`, `816`, `817`, `818`, `822`, `825`, `827`, `829`, `831`, `835`, `836`, `838`, `839`, `841`, `843`, `844`, `846`, `849`, `850`, `851`, `854`, `855`, `856`, `857`, `858`, `859`, `860`, `861`, `367`, `862`, `865`, `867`, `868`, `869`, `870`, `873`, `874`, `875`, `878`, `879`, `882`, `886`, `888`, `890`, `893`, `895`, `898`, `900`, `901`, `902`, `903`, `905`, `907`, `908`, `910`, `912`, `914`, `915`, `917`, `919`, `921`, `922`, `924`, `928`, `929`, `930`, `931`, `932`, `935`, `936`, `938`, `940`, `942`, `944`, `945`, `947`, `951`, `953`, `956`, `958`, `959`, `961`, `963`, `965`, `967`, `969`, `970`, `972`, `975`, `976`, `977`, `979`, `980`, `981`, `983`, `987`, `990`, `992`, `993`, `995`, `996`, `998`, `1000`, `1002`, `1004`, `1006`, `1008`, `1009`, `1012`, `1014`, `1015`, `1016`, `1018`, `1019`, `1022`, `1024`, `1026`, `1028`, `1029`, `1033`, `1036`, `1038`, `1040`, `1042`, `1047`, `1049`, `1051`, `1053`, `1055`, `1057`, `1060`, `1063`, `1065`, `1067`, `1069`, `1070`, `1071`, `1073`, `1075`, `1078`, `1080`, `1082`, `1084`, `1086`, `1089`, `1092`, `1093`, `1094`, `1095`, `1096`, `1098`, `1100`, `1102`, `1104`, `1105`, `1107`, `1108`, `1110`, `1113`, `1115`, `1118`, `1121`, `1123`, `1124`, `1126`, `1127`, `1129`, `1131`, `1134`, `1137`, `1141`, `1142`, `1144`, `1146`, `1148`, `1150`, `1151`, `1153`, `1154`, `1156`, `1158`, `1159`, `1162`, `1164`, `1166`, `1169`, `1173`, `1175`, `1178`, `1180`, `1183`, `1184`, `1186`, `1187`, `1189`, `1191`, `1194`, `1196`, `1197`, `1198`, `1200`, `1201`, `1203`, `1205`, `1207`, `1210`, `1212`, `1215`, `1216`, `1218`, `1220`, `1223`, `1224`, `1227`, `1229`, `1232`, `1234`, `1235`, `1238`, `1241`, `1242`, `1243`, `1246`, `1247`, `1249`, `1251`, `1252`, `1253`, `1256`, `1259`, `1262`, `1264`, `1267`, `1269`, `1271`, `1272`, `1275`, `1277`, `1278`, `1280`, `1282`, `1284`, `1285`, `1288`, `1291`, `1293`, `1296`, `1298`, `1300`, `1301`, `1302`, `1303`, `1305`, `1307`, `1309`, `1312`, `1315`, `1316`, `1319`, `1320`, `1321`, `1322`, `1323`, `1324`, `1327`, `1330`, `1333`, `1334`, `1335`, `1336`, `1339`, `1341`, `1344`, `1345`, `1347`, `1349`, `1350`, `1351`, `1352`, `1354`, `1357`, `1358`, `1359`, `1360`, `1362`, `1365`, `1368`, `1369`, `1370`, `1372`, `1374`, `1376`, `1377`, `1379`, `1382`, `1385`, `1386`, `1390`, `1393`, `1394`, `1396`, `1398`, `1400`, `1403`, `1405`, `1408`, `1410`, `1413`, `1415`, `1418`, `1420`, `1421`, `1423`, `1424`, `1426`, `1428`, `1429`, `1432`, `1434`, `1436`, `1438`, `1441`, `1443`, `1444`, `1445`, `1447`, `1449`, `1450`, `1451`, `1453`, `1455`, `1457`, `1458`, `1460`, `1461`, `1463`, `1465`, `1467`, `1470`, `1472`, `1474`, `1476`, `1477`, `1479`, `1481`, `1482`, `1483`, `1484`, `1486`, `1489`, `1492`, `1494`, `1495`, `1497`, `1498`, `1501`, `1503`, `1505`, `1506`, `1507`, `1508`, `1510`, `1511`, `1514`, `1515`, `1518`, `1521`, `1524`, `1526`, `1529`, `1532`, `1533`, `1534`, `1537`, `1539`, `1540`, `1542`, `1544`, `1545`, `1547`, `1549`, `1550`, `1551`, `1552`, `1553`, `1555`, `1557`, `1559`, `1562`, `1565`, `1568`, `1570`, `1571`, `1574`, `1576`, `1579`, `1580`, `1582`, `1583`, `1585`, `1586`, `1588`, `1590`, `1591`, `1592`, `1594`, `1595`, `1597`, `1598`, `1600`, `1602`, `1605`, `1607`, `1608`, `1609`, `1611`, `1613`, `1615`, `1616`, `1617`, `1620`, `1621`, `1623`, `1624`, `1625`, `1628`, `1630`, `1632`, `1634`, `1635`, `1636`, `1638`, `1639`, `1641`, `1643`, `1644`, `1647`, `1649`, `1650`, `1651`, `1652`, `1654`, `1656`, `1657`, `1658`, `1659`, `1660`, `1661`, `1663`, `1664`, `1665`, `1666`, `1669`, `1672`, `1673`, `1674`, `1675`, `1678`, `1679`, `1682`, `1685`, `1686`, `1689`, `1690`, `1691`, `1693`, `1694`, `1695`, `1697`, `1699`, `1701`, `1702`, `1704`, `1706`, `1707`, `1709`, `1711`, `1713`, `1715`, `1716`, `1720`, `1722`, `1724`, `1726`, `1727`, `1728`, `1729`, `1732`, `1733`, `1736`, `1737`, `1740`, `1741`, `1742`, `1744`, `1747`, `1749`, `1751`, `1755`, `1756`, `1757`, `1759`, `1761`, `1763`, `1764`, `1766`, `1769`, `1771`, `1772`, `1774`, `1776`, `1777`, `1780`, `1781`, `1782`, `1784`, `1785`, `1787`, `1789`, `1790`, `1791`, `1794`, `1796`, `1798`, `1801`, `1802`, `1805`, `1806`, `1807`, `1808`, `1811`, `1812`, `1815`, `1818`, `1821`, `1823`, `1825`, `1828`, `1830`, `1832`, `1835`, `1836`, `1839`, `1841`, `1844`, `1847`, `1850`, `1852`, `1853`, `1854`, `1855`, `1856`, `1857`, `1860`, `1862`, `1863`, `1864`, `1866`, `1867`, `1869`, `1870`, `1871`, `1874`, `1876`, `1878`, `1879`, `1882`, `1885`, `1887`, `1890`, `1893`, `1896`, `1898`, `1900`, `1902`, `1903`, `1904`, `1905`, `1906`, `1909`, `1912`, `1913`, `1917`, `1919`, `1921`, `1924`, `1925`, `1926`, `1928`, `1929`, `1931`, `1933`, `1935`, `1936`, `1937`, `1939`, `1941`, `1944`, `1946`, `1947`, `1950`, `1951`, `1954`, `1955`, `1957`, `1958`, `1960`, `1961`, `1964`, `1966`, `1968`, `1970`, `1971`, `1972`, `1975`, `1977`, `1980`, `1982`, `1983`, `1984`, `1985`, `1986`, `1987`, `1988`, `1991`, `1993`, `1995`, `1996`, `1997`, `1999`, `2000`, `2001`, `2003`, `2005`, `2008`, `2011`, `2012`, `2014`, `2017`, `2018`, `2019`, `2020`, `2022`, `2024`, `2025`, `2027`, `2029`, `2031`, `2032`, `2035`, `2036`, `2039`, `2040`, `2041`, `2044`, `2045`, `40`, `2046`, `2048`, `2049`, `2052`, `2055`, `2056`, `2058`, `2059`, `2061`, `2063`, `2066`, `2068`, `2069`, `2071`, `2072`, `2074`, `2076`, `2077`, `2078`, `2079`, `2080`, `2082`, `2084`, `2086`, `2087`, `2088`, `2090`, `2091`, `2094`, `2097`, `2098`, `2100`, `2102`, `2103`, `2104`, `2106`, `2107`, `2108`, `2111`, `2113`, `2114`, `2116`, `2118`, `2121`, `2124`, `2126`, `2128`, `2130`, `2134`, `2137`, `2139`, `2141`, `2143`, `2145`, `2146`, `2148`, `2150`, `2152`, `2155`, `2157`, `2160`, `2161`, `2163`, `2164`, `2165`, `2166`, `2167`, `2169`, `2170`, `2171`, `2174`, `2177`, `2178`, `2179`, `2180`, `2182`, `2185`, `2186`, `2187`, `2189`, `2190`, `2191`, `2192`, `2194`, `2195`, `2196`, `2199`, `2200`, `2202`, `2204`, `2206`, `2207`, `2208`, `2211`, `2213`, `2214`, `2215`, `2216`, `2217`, `2219`, `2220`, `2221`, `2222`, `2223`, `2225`, `2226`, `2227`, `2228`, `2230`, `2232`, `2234`, `2237`, `2239`, `2240`, `2241`, `2242`, `2243`, `2244`, `2245`, `2246`, `2247`, `2249`, `2251`, `2254`, `2256`, `2257`, `2258`, `2260`, `2261`, `2263`, `2266`, `2268`, `2269`, `2270`, `2271`, `2272`, `2273`, `2274`, `2275`, `2276`, `2279`, `2281`, `2283`, `2284`, `2285`, `2286`, `2287`, `2289`, `2291`, `2294`, `2295`, `2297`, `2298`, `2301`, `2302`, `2303`, `2304`, `2305`, `2306`, `2308`, `2310`, `2311`, `2312`, `2313`, `2314`, `2315`, `2316`, `2317`, `2318`, `2319`, `2322`, `2324`, `2326`, `2327`, `2330`, `2331`, `2332`, `2334`, `2335`, `2336`, `2337`, `2338`, `2339`, `2340`, `2341`, `2342`, `2343`, `2344`, `2345`, `2346`, `2347`, `2348`, `2349`, `2351`, `2353`, `2354`, `2356`, `2357`, `2358`, `2359`, `2360`, `2361`, `2362`, `2363`, `2364`, `2365`, `2366`, `2367`, `2368`, `2369`, `2370`, `2371`, `2372`, `2375`, `2376`, `2377`, `2378`, `2379`, `2380`, `2381`, `2382`, `2383`, `2384`, `2385`, `2386`, `2387`, `2388`, `2389`, `2390`, `2391`, `2392`, `2393`, `2394`, `2395`, `2396`, `2398`, `2399`, `2401`, `2403`, `2404`, `2405`, `2406`, `2407`, `2410`, `2411`, `2413`, `2414`, `2415`, `2416`, `2417`, `2418`, `2419`, `2420`, `2421`, `2422`, `2423`, `2424`, `2425`, `2426`, `2429`, `2430`, `2432`, `2433`, `2435`, `2437`, `2440`, `2443`, `2444`, `2445`, `2446`, `2447`, `2448`, `2450`, `2451`, `2452`, `2453`, `2454`, 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`3461`, `3462`, `3463`, `3464`, `3465`, `3466`, `3468`, `3469`, `3473`, `3474`, `3475`, `3477`, `3478`, `3479`, `3481`, `3482`, `3483`, `3485`, `3488`, `3489`, `3490`, `3491`, `3492`, `3493`, `3495`, `3496`, `3497`, `3498`, `3499`, `3500`, `3501`, `3502`, `3503`, `3504`, `3505`, `3506`, `3509`, `3510`, `3511`, `3512`, `3514`, `3516`, `3517`, `3518`, `3519`, `3520`, `3521`, `3522`, `3523`, `3524`, `3525`, `3526`, `3527`, `3528`, `3529`, `3530`, `3531`, `3532`, `3535`, `3536`, `3537`, `3538`, `3539`, `3540`, `3542`, `3543`, `3546`, `3547`, `3548`, `3549`, `3550`, `3551`, `3552`, `3553` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.96 |
| `TOKEN_P` | 99.94 |
| `TOKEN_R` | 99.98 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 95.65 |
| `SENTS_P` | 96.84 |
| `SENTS_R` | 94.49 |
| `TAG_ACC` | 95.43 |
| `POS_ACC` | 98.07 |
| `MORPH_ACC` | 95.50 |
| `DEP_UAS` | 88.11 |
| `DEP_LAS` | 83.62 |
| `LEMMA_ACC` | 90.46 |
|
{"language": ["lt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/lt_udv25_lithuanianalksnis_trf
|
[
"spacy",
"token-classification",
"lt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"lt"
] |
TAGS
#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Lithuanian-ALKSNIS
### Label Scheme
View label scheme (3674 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (3674 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3674 labels for 6 components)",
"### Accuracy"
] |
[
32,
18,
5
] |
[
"passage: TAGS\n#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (3674 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Latvian-LVTB
| Feature | Description |
| --- | --- |
| **Name** | `lv_udv25_latvianlvtb_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (6012 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `X`, `affpanc`, `affpanp`, `affpayc`, `affpayp`, `affpays`, `affpdnc`, `affpdnp`, `affpdyc`, `affpdyp`, `affpdys`, `affpgnp`, `affpgyc`, `affpgyp`, `affplnc`, `affplnp`, `affplyc`, `affplyp`, `affpnnc`, `affpnnp`, `affpnyc`, `affpnyp`, `affpnys`, `affsanc`, `affsanp`, `affsayc`, `affsayp`, `affsays`, `affsdnc`, `affsdnp`, `affsdyc`, `affsdyp`, `affsgnc`, `affsgnp`, `affsgyc`, `affsgyp`, `affsgys`, `affslnc`, `affslnp`, `affslyc`, `affslyp`, `affslys`, `affsnnc`, `affsnnp`, `affsnyc`, `affsnyp`, `affsnys`, `affsvyp`, `afmpanc`, `afmpanp`, `afmpayc`, `afmpayp`, `afmpays`, `afmpdnc`, `afmpdnp`, `afmpdyc`, `afmpdyp`, `afmpdys`, `afmpgnc`, `afmpgnp`, `afmpgyc`, `afmpgyp`, `afmpgys`, `afmplnc`, `afmplnp`, `afmplyc`, `afmplyp`, `afmplys`, `afmpnnc`, `afmpnnp`, `afmpnyc`, `afmpnyp`, `afmpnys`, `afmpvyp`, `afmsanc`, `afmsanp`, `afmsayc`, `afmsayp`, `afmsays`, `afmsdnc`, `afmsdnp`, `afmsdyc`, `afmsdyp`, `afmsdys`, `afmsgnc`, `afmsgnp`, `afmsgyc`, `afmsgyp`, `afmsgys`, `afmslnc`, `afmslnp`, `afmslyc`, `afmslyp`, `afmslys`, `afmsnnc`, `afmsnnp`, `afmsnyc`, `afmsnyp`, `afmsnys`, `arfpanp`, `arfpayp`, `arfpdnp`, `arfpdyc`, `arfpdyp`, `arfpgnp`, `arfpgyp`, `arfplnc`, `arfplnp`, `arfplyc`, `arfplyp`, `arfpnnc`, `arfpnnp`, `arfpnyp`, `arfpnys`, `arfsanp`, `arfsayp`, `arfsdnp`, `arfsdyp`, `arfsgnc`, `arfsgnp`, `arfsgyp`, `arfslnp`, `arfslyp`, `arfsnnc`, `arfsnnp`, `arfsnyc`, `arfsnyp`, `arfsvyp`, `armpanp`, `armpayc`, `armpayp`, `armpdnp`, `armpdyc`, `armpdyp`, `armpdys`, `armpgnp`, `armpgyp`, `armplnp`, `armplyc`, `armplyp`, `armpnnc`, `armpnnp`, `armpnyc`, `armpnyp`, `armsanp`, `armsayc`, `armsayp`, `armsdnp`, `armsdyp`, `armsgnp`, `armsgyp`, `armslnp`, `armslyp`, `armsnnp`, `armsnyp`, `armsnys`, `cc`, `cs`, `i`, `mcc0p0`, `mccfpa`, `mccmpn`, `mccmsa`, `mcs0p0`, `mcsfp0`, `mcsfpa`, `mcsfpd`, `mcsfpg`, `mcsfpl`, `mcsfpn`, `mcsfsa`, `mcsfsd`, `mcsfsg`, `mcsfsl`, `mcsfsn`, `mcsmpa`, `mcsmpd`, `mcsmpg`, `mcsmpl`, `mcsmpn`, `mcsmsa`, `mcsmsd`, `mcsmsg`, `mcsmsl`, 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`ncfvg5`, `ncfvl4`, `ncfvl5`, `ncfvn5`, `ncm000`, `ncmda1`, `ncmda2`, `ncmda6`, `ncmdd1`, `ncmdd2`, `ncmdd3`, `ncmdd6`, `ncmdg1`, `ncmdg2`, `ncmdg3`, `ncmdg6`, `ncmdgg`, `ncmdl1`, `ncmdl2`, `ncmdn1`, `ncmdn2`, `ncmdn6`, `ncmpa1`, `ncmpa2`, `ncmpa3`, `ncmpa4`, `ncmpd1`, `ncmpd2`, `ncmpd3`, `ncmpd5`, `ncmpg1`, `ncmpg2`, `ncmpg3`, `ncmpg4`, `ncmpg5`, `ncmpg6`, `ncmpgg`, `ncmpl1`, `ncmpl2`, `ncmpl3`, `ncmpl4`, `ncmpn0`, `ncmpn1`, `ncmpn2`, `ncmpn3`, `ncmpn4`, `ncmpn5`, `ncmpv1`, `ncmpv2`, `ncmsa1`, `ncmsa2`, `ncmsa3`, `ncmsa4`, `ncmsa5`, `ncmsd1`, `ncmsd2`, `ncmsd3`, `ncmsd4`, `ncmsg0`, `ncmsg1`, `ncmsg2`, `ncmsg3`, `ncmsg4`, `ncmsgg`, `ncmsgr`, `ncmsl1`, `ncmsl2`, `ncmsl3`, `ncmsl4`, `ncmsl5`, `ncmsn1`, `ncmsn2`, `ncmsn3`, `ncmsn4`, `ncmsnr`, `ncmsv1`, `ncmsv2`, `ncmva1`, `ncmva3`, `ncmvd1`, `ncmvd3`, `ncmvg1`, `ncmvg3`, `ncmvl1`, `ncmvl3`, `ncmvn1`, `ncmvn3`, `np0000`, `npfda4`, `npfdd4`, `npfdd6`, `npfdg1`, `npfdg4`, `npfdg6`, `npfdl4`, `npfdl6`, `npfdn4`, `npfdn5`, `npfdn6`, `npfpa5`, `npfpd5`, `npfpg2`, `npfpg4`, `npfpn4`, `npfpn5`, `npfsa4`, `npfsa5`, `npfsa6`, `npfsd4`, `npfsd5`, `npfsg1`, `npfsg3`, `npfsg4`, `npfsg5`, `npfsg6`, `npfsl4`, `npfsl5`, `npfsl6`, `npfsn3`, `npfsn4`, `npfsn5`, `npfsn6`, `npfsv4`, `npfsv5`, `npmda1`, `npmda2`, `npmdd1`, `npmdd2`, `npmdg1`, `npmdg2`, `npmdl1`, `npmdl2`, `npmdn1`, `npmdn2`, `npmpa1`, `npmpd1`, `npmpd2`, `npmpg1`, `npmpg2`, `npmpgg`, `npmpl1`, `npmpl2`, `npmpn1`, `npmpn2`, `npmsa1`, `npmsa2`, `npmsa3`, `npmsa4`, `npmsa5`, `npmsd1`, `npmsd2`, `npmsd3`, `npmsd4`, `npmsd5`, `npmsg0`, `npmsg1`, `npmsg2`, `npmsg3`, `npmsg4`, `npmsg5`, `npmsl1`, `npmsl2`, `npmsn1`, `npmsn2`, `npmsn3`, `npmsn4`, `npmsn5`, `npmsv1`, `npmsv2`, `pd0fpan`, `pd0fpdn`, `pd0fpgn`, `pd0fpln`, `pd0fpnn`, `pd0fsan`, `pd0fsdn`, `pd0fsgn`, `pd0fsln`, `pd0fsnn`, `pd0mpan`, `pd0mpdn`, `pd0mpgn`, `pd0mpln`, `pd0mply`, `pd0mpnn`, `pd0msan`, `pd0msdn`, `pd0msgn`, `pd0msln`, `pd0msnn`, `pd3fpan`, `pd3fpdn`, `pd3fpgn`, `pd3fpln`, `pd3fpnn`, `pd3fsan`, `pd3fsdn`, `pd3fsgn`, `pd3fsln`, `pd3fsnn`, `pd3mpan`, `pd3mpdn`, `pd3mpgn`, `pd3mpln`, `pd3mpnn`, `pd3msan`, `pd3msdn`, `pd3msgn`, `pd3msln`, `pd3msnn`, `pg0fpan`, `pg0fpdn`, `pg0fpgn`, `pg0fpln`, `pg0fpnn`, `pg0fsan`, `pg0fsdn`, `pg0fsgn`, `pg0fsln`, `pg0fsnn`, `pg0mpan`, `pg0mpdn`, `pg0mpgn`, `pg0mpln`, `pg0mpnn`, `pg0msan`, `pg0msdn`, `pg0msgn`, `pg0msln`, `pg0msnn`, `pi000an`, `pi000ay`, `pi000dn`, `pi000dy`, `pi000gn`, `pi000gy`, `pi000nn`, `pi000ny`, `pi0fpan`, `pi0fpay`, `pi0fpdn`, `pi0fpgn`, `pi0fpgy`, `pi0fpln`, `pi0fply`, `pi0fpnn`, `pi0fpny`, `pi0fsan`, `pi0fsay`, `pi0fsdn`, `pi0fsgn`, `pi0fsgy`, `pi0fsln`, `pi0fsnn`, `pi0fsny`, `pi0mpan`, `pi0mpay`, `pi0mpdn`, `pi0mpgn`, `pi0mpgy`, `pi0mpln`, `pi0mpnn`, `pi0mpny`, `pi0msan`, `pi0msay`, `pi0msdn`, `pi0msdy`, `pi0msgn`, `pi0msgy`, `pi0msln`, `pi0msly`, `pi0msnn`, `pi0msny`, `pi3msnn`, `pp10pan`, `pp10pdn`, `pp10pgn`, `pp10pln`, `pp10pnn`, `pp10san`, `pp10sdn`, `pp10sgn`, `pp10sln`, `pp10snn`, `pp1mpgn`, `pp20pan`, `pp20pdn`, `pp20pgn`, `pp20pnn`, `pp20san`, `pp20sdn`, `pp20sgn`, `pp20sln`, `pp20snn`, `pp2fsln`, `pp3fpan`, `pp3fpdn`, `pp3fpgn`, `pp3fpnn`, `pp3fsan`, `pp3fsdn`, `pp3fsgn`, `pp3fsln`, `pp3fsnn`, `pp3mpan`, `pp3mpdn`, `pp3mpgn`, `pp3mpln`, `pp3mpnn`, `pp3msan`, `pp3msdn`, `pp3msgn`, `pp3msln`, `pp3msnn`, `pq000an`, `pq000dn`, `pq000gn`, `pq000nn`, `pq0fpan`, `pq0fpnn`, `pq0fsnn`, `pq0mpnn`, `pq0msan`, `pq0msdn`, `pq0msln`, `pq0msnn`, `pr000an`, `pr000dn`, `pr000gn`, `pr000nn`, `pr00pgn`, `pr0fpan`, `pr0fpdn`, `pr0fpgn`, `pr0fpln`, `pr0fpnn`, `pr0fsan`, `pr0fsdn`, `pr0fsgn`, `pr0fsln`, `pr0fsnn`, `pr0mpan`, `pr0mpdn`, `pr0mpgn`, `pr0mpln`, `pr0mpnn`, `pr0msan`, `pr0msdn`, `pr0msgn`, `pr0msln`, `pr0msnn`, `ps0fpan`, `ps0fpdn`, `ps0fpgn`, `ps0fpln`, `ps0fpnn`, `ps0fsan`, `ps0fsdn`, `ps0fsgn`, `ps0fsln`, `ps0fsnn`, `ps0mpan`, `ps0mpdn`, `ps0mpgn`, `ps0mpln`, `ps0mpnn`, `ps0msan`, `ps0msdn`, `ps0msgn`, `ps0msln`, `ps0msnn`, `ps10sgn`, `ps1mpnn`, `ps1msgn`, `ps1msnn`, `ps2fsnn`, `px000an`, `px000dn`, `px000gn`, `px000ln`, `q`, `r0c`, `r0m`, `r0p`, `r0q`, `r0t`, `rcc`, `rcm`, `rcp`, `rcq`, `rct`, `rpc`, `rpm`, `rpp`, `rpq`, `rpt`, `rrm`, `rrp`, `rrt`, `rsm`, `rsp`, `rsq`, `rst`, `sp00`, `sppd`, `sppg`, `spsa`, `spsd`, `spsg`, `stpg`, `stsg`, `vcnc0ii00an`, `vcnc0ii00ay`, `vcnd0ii00an`, `vcnifi130an`, `vcnifii1pan`, `vcnifii1pay`, `vcnifii1san`, `vcnifii1say`, `vcnifii2pan`, `vcnifii2pay`, `vcnifii2san`, `vcnifii2say`, `vcnifii30an`, `vcnifii30ay`, `vcnipii1pan`, `vcnipii1pay`, `vcnipii1san`, `vcnipii1say`, `vcnipii2pan`, `vcnipii2pay`, `vcnipii2san`, `vcnipii2say`, `vcnipii30an`, `vcnipii30ay`, `vcnisii1pan`, `vcnisii1pay`, `vcnisii1san`, `vcnisii1say`, `vcnisii2pay`, `vcnisii30an`, `vcnisii30ay`, `vcnist330an`, `vcnm0ii2pan`, `vcnm0ii2san`, `vcnn0ii000n`, `vcnn0ii000y`, `vcnn0ii00an`, `vcnn0t3000n`, `vcnpdfpnasnpn`, `vcnpdfsaasypn`, `vcnpdfsgapypn`, `vcnpdfsnapnpn`, `vcnpdfsnasnpn`, `vcnpdmplasypn`, `vcnpdmpnasnpn`, `vcnpdmsaasnpy`, `vcnpdmsaasypn`, `vcnpdmsnasn0n`, `vcnpdmsnasnpn`, `vcnppfsn0000n`, `vcnppmpn0000n`, `vcnppmsn0000n`, `vcnpu0000000n`, `vcnrfii00an`, `vcnrpii00an`, `vcnrpii00ay`, `venipi130an`, `venipi130ay`, `venisi130an`, `veyifii30an`, `veyipi130an`, `veyipi130ay`, `veyipi330an`, `veyipii30an`, `veyipii30ay`, `veyisi130an`, `veyisi330an`, `veyisii30an`, `veyisii30ay`, `veypdmpnasnpn`, `veypdmsnasnpn`, `vgnpdmsgapypn`, `vmnc0i100an`, `vmnc0i100ay`, `vmnc0i10say`, `vmnc0i200an`, `vmnc0i300an`, `vmnc0i300ay`, `vmnc0ii000n`, `vmnc0ii00an`, `vmnc0ii00ay`, `vmnc0t100an`, `vmnc0t100ay`, `vmnc0t200an`, `vmnc0t200ay`, `vmnc0t300an`, `vmnc0t300ay`, `vmnc0ti00an`, `vmnd0i100an`, `vmnd0i200an`, `vmnd0i300an`, `vmnd0ii00an`, `vmnd0t100an`, `vmnd0t130an`, `vmnd0t200an`, `vmnd0t300an`, `vmnd0ti00an`, `vmnd0ti00pn`, `vmnifi11pan`, `vmnifi11pay`, `vmnifi11san`, `vmnifi11say`, `vmnifi12pan`, `vmnifi12san`, `vmnifi130an`, `vmnifi130ay`, `vmnifi13san`, `vmnifi21pan`, `vmnifi21san`, `vmnifi21say`, `vmnifi22san`, `vmnifi230an`, `vmnifi230ay`, `vmnifi31pan`, `vmnifi32san`, `vmnifi32say`, `vmnifi330an`, `vmnifi330ay`, `vmnifii1san`, `vmnifii2san`, `vmnifii30an`, `vmnifii30ay`, `vmnift11pan`, `vmnift11pay`, `vmnift11san`, `vmnift11say`, `vmnift12pan`, `vmnift12san`, `vmnift12say`, `vmnift130an`, `vmnift130ay`, `vmnift21pan`, `vmnift21pay`, `vmnift21san`, `vmnift21say`, `vmnift22pan`, `vmnift22pay`, `vmnift22san`, `vmnift22say`, `vmnift230an`, `vmnift230ay`, `vmnift31pan`, `vmnift31pay`, `vmnift31san`, `vmnift31say`, `vmnift32pan`, `vmnift32san`, `vmnift32say`, `vmnift330an`, `vmnift330ay`, `vmnifti1san`, `vmnifti2san`, `vmnifti30an`, `vmnim0230an`, `vmnipi11pan`, `vmnipi11pay`, `vmnipi11san`, `vmnipi12pan`, `vmnipi12san`, `vmnipi130an`, `vmnipi130ay`, `vmnipi21pan`, `vmnipi21san`, `vmnipi22pan`, `vmnipi22pay`, `vmnipi22san`, `vmnipi230an`, `vmnipi230ay`, `vmnipi23san`, `vmnipi31pan`, `vmnipi31san`, `vmnipi31say`, `vmnipi32pan`, `vmnipi32san`, `vmnipi330an`, `vmnipi330ay`, `vmnipii1pan`, `vmnipii1san`, `vmnipii2pan`, `vmnipii2pay`, `vmnipii2san`, `vmnipii30an`, `vmnipii30ay`, `vmnipt110an`, `vmnipt11pan`, `vmnipt11pay`, `vmnipt11san`, `vmnipt11say`, `vmnipt12pan`, `vmnipt12san`, `vmnipt12say`, `vmnipt130an`, `vmnipt130ay`, `vmnipt21pan`, `vmnipt21pay`, `vmnipt21san`, `vmnipt21say`, `vmnipt22pan`, `vmnipt22san`, `vmnipt22say`, `vmnipt230an`, `vmnipt230ay`, `vmnipt23san`, `vmnipt31pan`, `vmnipt31pay`, `vmnipt31san`, `vmnipt31say`, `vmnipt32pan`, `vmnipt32san`, `vmnipt32say`, `vmnipt330an`, `vmnipt330ay`, `vmnipti1pan`, `vmnipti1san`, `vmnipti2pan`, `vmnipti30an`, `vmnipti30ay`, `vmnipti3san`, `vmnisi11pan`, `vmnisi11san`, `vmnisi11say`, `vmnisi12san`, `vmnisi130an`, `vmnisi130ay`, `vmnisi21pan`, `vmnisi21san`, `vmnisi22pan`, `vmnisi230an`, `vmnisi230ay`, `vmnisi31pan`, `vmnisi31san`, `vmnisi31say`, `vmnisi330an`, `vmnisi330ay`, `vmnisii1pan`, `vmnisii1pay`, `vmnisii1san`, `vmnisii2san`, `vmnisii30an`, `vmnisii30ay`, `vmnist11pan`, `vmnist11pay`, `vmnist11san`, `vmnist11say`, `vmnist12pan`, `vmnist12san`, `vmnist130an`, `vmnist130ay`, `vmnist21pan`, `vmnist21pay`, `vmnist21san`, `vmnist21say`, `vmnist230an`, `vmnist230ay`, `vmnist31pan`, `vmnist31pay`, `vmnist31san`, `vmnist31say`, `vmnist32pan`, `vmnist32san`, `vmnist32say`, `vmnist330an`, `vmnist330ay`, `vmnisti1san`, `vmnisti30an`, `vmnisti30ay`, `vmnm0i12pan`, `vmnm0i12pay`, `vmnm0i12san`, `vmnm0i12say`, `vmnm0i21san`, `vmnm0i22pan`, `vmnm0i22san`, `vmnm0i32pan`, `vmnm0i32san`, `vmnm0i32say`, `vmnm0ii1pan`, `vmnm0ii2pan`, `vmnm0ii2san`, `vmnm0t11san`, `vmnm0t12pan`, `vmnm0t12pay`, `vmnm0t12san`, `vmnm0t12say`, `vmnm0t130an`, `vmnm0t21san`, `vmnm0t21say`, `vmnm0t22pan`, `vmnm0t22san`, `vmnm0t22say`, `vmnm0t230an`, `vmnm0t31san`, `vmnm0t32pan`, `vmnm0t32pay`, `vmnm0t32san`, `vmnm0t32say`, `vmnm0ti2pan`, `vmnm0ti2san`, `vmnmpi130ay`, `vmnmpi32san`, `vmnmpii2pan`, `vmnmpt12pan`, `vmnmpt12say`, `vmnmpt130ay`, `vmnmpt22san`, `vmnmpt32pan`, `vmnmpt32san`, `vmnn0i1000n`, `vmnn0i1000y`, `vmnn0i100an`, `vmnn0i130an`, `vmnn0i2000n`, `vmnn0i2000y`, `vmnn0i200an`, `vmnn0i3000n`, `vmnn0i3000y`, `vmnn0i300an`, `vmnn0ii000n`, `vmnn0ii000y`, `vmnn0t1000n`, `vmnn0t1000y`, `vmnn0t100an`, `vmnn0t2000n`, `vmnn0t2000y`, `vmnn0t200an`, `vmnn0t3000n`, `vmnn0t3000y`, `vmnn0t300an`, `vmnn0ti000n`, `vmnn0ti00an`, `vmnpdfpaapnpn`, `vmnpdfpaapypn`, `vmnpdfpaasnpn`, `vmnpdfpaasypn`, `vmnpdfpappnpn`, `vmnpdfpappnpy`, `vmnpdfpappypn`, `vmnpdfpapsnpn`, `vmnpdfpapsnpy`, `vmnpdfpapsypn`, `vmnpdfpdapnpn`, `vmnpdfpdapnpy`, `vmnpdfpdapypn`, `vmnpdfpdapysn`, `vmnpdfpdasnpn`, `vmnpdfpdasypn`, `vmnpdfpdppnpn`, `vmnpdfpdppnpy`, `vmnpdfpdppypn`, `vmnpdfpdpsnpn`, `vmnpdfpdpsypn`, `vmnpdfpdpsypy`, `vmnpdfpgapncn`, `vmnpdfpgapypn`, `vmnpdfpgppnpn`, `vmnpdfpgppnpy`, `vmnpdfpgppypn`, `vmnpdfpgpsnpn`, `vmnpdfpgpsypn`, `vmnpdfplapnpn`, `vmnpdfplapypn`, `vmnpdfplasnpn`, `vmnpdfplasypn`, `vmnpdfplppnpy`, `vmnpdfplpsnpn`, `vmnpdfplpsypn`, `vmnpdfpnapn0n`, `vmnpdfpnapnpn`, `vmnpdfpnapnpy`, `vmnpdfpnapypn`, `vmnpdfpnasnpn`, `vmnpdfpnasypn`, `vmnpdfpnasypy`, `vmnpdfpnppnpn`, `vmnpdfpnppnpy`, `vmnpdfpnppypn`, `vmnpdfpnpsnpn`, `vmnpdfpnpsnpy`, `vmnpdfpnpsypn`, `vmnpdfpnpsypy`, `vmnpdfsaapn0n`, `vmnpdfsaapncn`, `vmnpdfsaapnpn`, `vmnpdfsaapnpy`, `vmnpdfsaapypn`, `vmnpdfsaasnpn`, `vmnpdfsaasypn`, `vmnpdfsappnpn`, `vmnpdfsappnpy`, `vmnpdfsappypn`, `vmnpdfsappypy`, `vmnpdfsapsncn`, `vmnpdfsapsnpn`, `vmnpdfsapsnpy`, `vmnpdfsapsypn`, `vmnpdfsdapnpn`, `vmnpdfsdapypn`, `vmnpdfsdasnpn`, `vmnpdfsdasypn`, `vmnpdfsdppnpn`, `vmnpdfsdppypn`, `vmnpdfsdpsnpn`, `vmnpdfsdpsnpy`, `vmnpdfsdpsypn`, `vmnpdfsgapnpn`, `vmnpdfsgapypn`, `vmnpdfsgasnpn`, `vmnpdfsgasypn`, `vmnpdfsgppnpn`, `vmnpdfsgppnpy`, `vmnpdfsgppypn`, `vmnpdfsgpsnpn`, `vmnpdfsgpsypn`, `vmnpdfsgpsypy`, `vmnpdfslapnpn`, `vmnpdfslapypn`, `vmnpdfslasnpn`, `vmnpdfslasypn`, `vmnpdfslppnpn`, `vmnpdfslppypn`, `vmnpdfslpsnpn`, `vmnpdfslpsypn`, `vmnpdfslpsypy`, `vmnpdfsnapnpn`, `vmnpdfsnapnpy`, `vmnpdfsnapypn`, `vmnpdfsnapysn`, `vmnpdfsnasn0n`, `vmnpdfsnasnpn`, `vmnpdfsnasnpy`, `vmnpdfsnasypn`, `vmnpdfsnppncn`, `vmnpdfsnppnpn`, `vmnpdfsnppnpy`, `vmnpdfsnppypn`, `vmnpdfsnppypy`, `vmnpdfsnpsncn`, `vmnpdfsnpsnpn`, `vmnpdfsnpsnpy`, `vmnpdfsnpsypn`, `vmnpdfsnpsypy`, `vmnpdmpaapnpn`, `vmnpdmpaapycn`, `vmnpdmpaapypn`, `vmnpdmpaasnpn`, `vmnpdmpaasypn`, `vmnpdmpappnpn`, `vmnpdmpappypn`, `vmnpdmpapsnpn`, `vmnpdmpapsnpy`, `vmnpdmpapsypn`, `vmnpdmpapsypy`, `vmnpdmpdapnpn`, `vmnpdmpdapypn`, `vmnpdmpdasnpn`, `vmnpdmpdasypn`, `vmnpdmpdppnpn`, `vmnpdmpdppycn`, `vmnpdmpdppypn`, `vmnpdmpdpsnpn`, `vmnpdmpdpsnpy`, `vmnpdmpdpsycn`, `vmnpdmpdpsypn`, `vmnpdmpdpsypy`, `vmnpdmpgapnpn`, `vmnpdmpgapypn`, `vmnpdmpgasnpn`, `vmnpdmpgasypn`, `vmnpdmpgppypn`, `vmnpdmpgpsnpn`, `vmnpdmpgpsypn`, `vmnpdmpgpsypy`, `vmnpdmplapnpn`, `vmnpdmplapypn`, `vmnpdmplpsnpn`, `vmnpdmplpsypn`, `vmnpdmpnapnpn`, `vmnpdmpnapypn`, `vmnpdmpnasnpn`, `vmnpdmpnasypn`, `vmnpdmpnppn0n`, `vmnpdmpnppnpn`, `vmnpdmpnppnpy`, `vmnpdmpnppypn`, `vmnpdmpnpsnpn`, `vmnpdmpnpsnpy`, `vmnpdmpnpsypn`, `vmnpdmpnpsypy`, `vmnpdmpvppypn`, `vmnpdmsaapnpn`, `vmnpdmsaapypn`, `vmnpdmsaasnpn`, `vmnpdmsaasypn`, `vmnpdmsappnpn`, `vmnpdmsappnpy`, `vmnpdmsappypn`, `vmnpdmsappypy`, `vmnpdmsapsnpn`, `vmnpdmsapsnpy`, `vmnpdmsapsypn`, `vmnpdmsapsypy`, `vmnpdmsdapnpn`, `vmnpdmsdapypn`, `vmnpdmsdasnpn`, `vmnpdmsdppnpn`, `vmnpdmsdppypn`, `vmnpdmsdppypy`, `vmnpdmsdpsnpn`, `vmnpdmsdpsypn`, `vmnpdmsdpsypy`, `vmnpdmsgapnpn`, `vmnpdmsgapypn`, `vmnpdmsgasnpn`, `vmnpdmsgasypn`, `vmnpdmsgppnpn`, `vmnpdmsgppy0n`, `vmnpdmsgppypn`, `vmnpdmsgppypy`, `vmnpdmsgpsnpn`, `vmnpdmsgpsycn`, `vmnpdmsgpsypn`, `vmnpdmsgpsypy`, `vmnpdmslapnpn`, `vmnpdmslapypn`, `vmnpdmslasnpn`, `vmnpdmslasypn`, `vmnpdmslppnpn`, `vmnpdmslppy0n`, `vmnpdmslppypn`, `vmnpdmslpsnpn`, `vmnpdmslpsypn`, `vmnpdmsnapnpn`, `vmnpdmsnapnpy`, `vmnpdmsnapypn`, `vmnpdmsnasn0n`, `vmnpdmsnasnpn`, `vmnpdmsnasnpy`, `vmnpdmsnasypn`, `vmnpdmsnppnpn`, `vmnpdmsnppnpy`, `vmnpdmsnppypn`, `vmnpdmsnppypy`, `vmnpdmsnpsnpn`, `vmnpdmsnpsnpy`, `vmnpdmsnpsycn`, `vmnpdmsnpsypn`, `vmnpdmsnpsypy`, `vmnppfpn0000y`, `vmnppfsn0000n`, `vmnppmpn0000n`, `vmnppmpnap00n`, `vmnppmpnap0pn`, `vmnppmpnap0py`, `vmnppmsn0000n`, `vmnpu0000000n`, `vmnpu0000000y`, `vmnpu000000pn`, `vmnpu00000n0n`, `vmnpu000apnpn`, `vmnpumpgpsnpn`, `vmnr0t100an`, `vmnr0t3000n`, `vmnrfi100an`, `vmnrft100an`, `vmnrft200an`, `vmnrft200ay`, `vmnrft300an`, `vmnrpi1000y`, `vmnrpi100an`, `vmnrpi2000n`, `vmnrpi200an`, `vmnrpi300an`, `vmnrpii00an`, `vmnrpii00ay`, `vmnrpt100an`, `vmnrpt100ay`, `vmnrpt200an`, `vmnrpt200ay`, `vmnrpt300an`, `vmnrpt300ay`, `vmyc0i100an`, `vmyc0i100ay`, `vmyc0i200an`, `vmyc0i200ay`, `vmyc0i300an`, `vmyc0i300ay`, `vmyc0t100an`, `vmyc0t200an`, `vmyc0t300an`, `vmyc0ti00an`, `vmyd0i100an`, `vmyd0i200an`, `vmyd0i300an`, `vmyd0ii00an`, `vmyd0t100an`, `vmyd0t200an`, `vmyd0t300an`, `vmyd0ti00an`, `vmyifi11pan`, `vmyifi11san`, `vmyifi11say`, `vmyifi12pan`, `vmyifi12san`, `vmyifi130an`, `vmyifi130ay`, `vmyifi21san`, `vmyifi230an`, `vmyifi230ay`, `vmyifi31pan`, `vmyifi31san`, `vmyifi31say`, `vmyifi32san`, `vmyifi330an`, `vmyifi330ay`, `vmyift11pan`, `vmyift130an`, `vmyift21san`, `vmyift31pan`, `vmyift32san`, `vmyift330an`, `vmyifti1san`, `vmyifti30an`, `vmyipi110ay`, `vmyipi11pan`, `vmyipi11san`, `vmyipi12pan`, `vmyipi12san`, `vmyipi12say`, `vmyipi130an`, `vmyipi130ay`, `vmyipi21pan`, `vmyipi21san`, `vmyipi21say`, `vmyipi22pan`, `vmyipi22san`, `vmyipi230an`, `vmyipi230ay`, `vmyipi31pan`, `vmyipi31san`, `vmyipi31say`, `vmyipi32pan`, `vmyipi32san`, `vmyipi330an`, `vmyipi330ay`, `vmyipii1pan`, `vmyipt11pan`, `vmyipt11san`, `vmyipt12san`, `vmyipt130an`, `vmyipt130ay`, `vmyipt21san`, `vmyipt22san`, `vmyipt230an`, `vmyipt31pan`, `vmyipt31san`, `vmyipt31say`, `vmyipt32pan`, `vmyipt32san`, `vmyipt32say`, `vmyipt330an`, `vmyipt330ay`, `vmyipti1pan`, `vmyipti1san`, `vmyipti2pan`, `vmyipti30an`, `vmyipti30ay`, `vmyisi11pan`, `vmyisi11san`, `vmyisi12san`, `vmyisi130an`, `vmyisi130ay`, `vmyisi13pan`, `vmyisi21pan`, `vmyisi21san`, `vmyisi22san`, `vmyisi230an`, `vmyisi230ay`, `vmyisi31pan`, `vmyisi31san`, `vmyisi31say`, `vmyisi32san`, `vmyisi330an`, `vmyisi330ay`, `vmyisii1san`, `vmyisii30an`, `vmyist11pan`, `vmyist11san`, `vmyist130an`, `vmyist21pan`, `vmyist230an`, `vmyist230ay`, `vmyist31pan`, `vmyist31san`, `vmyist32pan`, `vmyist330an`, `vmyist330ay`, `vmyisti1pan`, `vmyisti1san`, `vmyisti30an`, `vmyisti30ay`, `vmym0i11san`, `vmym0i12pan`, `vmym0i12san`, `vmym0i12say`, `vmym0i22pan`, `vmym0i22san`, `vmym0i22say`, `vmym0i32pan`, `vmym0i32pay`, `vmym0i32san`, `vmym0t22pan`, `vmym0t22san`, `vmym0t32pan`, `vmym0t32san`, `vmympi32san`, `vmympt32san`, `vmyn0i1000n`, `vmyn0i1000y`, `vmyn0i2000n`, `vmyn0i3000n`, `vmyn0i3000y`, `vmyn0ii000n`, `vmyn0ii00an`, `vmyn0t1000n`, `vmyn0t1000y`, `vmyn0t100an`, `vmyn0t2000n`, `vmyn0t3000n`, `vmyn0t3000y`, `vmyn0ti000n`, `vmypdfpaasnpn`, `vmypdfpnasnpn`, `vmypdfpnasnpy`, `vmypdfpnasypn`, `vmypdfpnppypn`, `vmypdfsaasnpn`, `vmypdfsaasnpy`, `vmypdfsnasn0n`, `vmypdfsnasnpn`, `vmypdmpaapnpn`, `vmypdmpaasypn`, `vmypdmpnasn0n`, `vmypdmpnasnpn`, `vmypdmsaapnpn`, `vmypdmsaasnpn`, `vmypdmsnasn0n`, `vmypdmsnasnpn`, `vmypdmsnasnpy`, `vmypdmsnpsnpn`, `vmyppf0n0000n`, `vmyppfsn0000n`, `vmyppfsn0000y`, `vmyppm0n0000n`, `vmyppmpn0000n`, `vmyppms00000n`, `vmyppmsn0000n`, `vmypu0000000n`, `vmypu0000000y`, `vmypu000000pn`, `vmypumsnasnpn`, `vmyrfi100an`, `vmyrpi200an`, `vmyrpi300an`, `vmyrpt100an`, `vmyrpt300an`, `vmyrpt300ay`, `vonc0i100an`, `vonc0i100ay`, `vonc0i300an`, `vonc0i300ay`, `vonc0t300ay`, `vond0i100an`, `vond0t300an`, `vondpi300an`, `vonifi11pay`, `vonifi12pay`, `vonifi130an`, `vonifi130ay`, `vonifi230an`, `vonifi31pan`, `vonifi31san`, `vonifi31say`, `vonifi32san`, `vonifi32say`, `vonifi330an`, `vonifi330ay`, `vonift31say`, `vonift32san`, `vonift330an`, `vonift330ay`, `vonipi11pan`, `vonipi11pay`, `vonipi11san`, `vonipi11say`, `vonipi12pan`, `vonipi130an`, `vonipi130ay`, `vonipi21pan`, `vonipi230an`, `vonipi230ay`, `vonipi300ay`, `vonipi31pan`, `vonipi31pay`, `vonipi31san`, `vonipi31say`, `vonipi32pan`, `vonipi32pay`, `vonipi32san`, `vonipi32say`, `vonipi330an`, `vonipi330ay`, `vonipii30an`, `vonipt130an`, `vonipt230an`, `vonipt31pan`, `vonipt31pay`, `vonipt31san`, `vonipt31say`, `vonipt32pan`, `vonipt32san`, `vonipt330an`, `vonipt330ay`, `vonisi11san`, `vonisi11say`, `vonisi130an`, `vonisi130ay`, `vonisi230an`, `vonisi31pan`, `vonisi31pay`, `vonisi31san`, `vonisi31say`, `vonisi32pan`, `vonisi330an`, `vonisi330ay`, `vonist130an`, `vonist330an`, `vonist330ay`, `vonm0i32san`, `vonmpi32san`, `vonn0i3000n`, `vonn0t3000n`, `vonpdfpn00npy`, `vonpdfpnasnpn`, `vonpdfsnasnpn`, `vonpdfsnasnpy`, `vonpdmpnasnpn`, `vonpdmsnasnpn`, `vonpdmsnpsnpn`, `vonpdmsnpsypn`, `vonppfsn0000n`, `vonppmsn0000n`, `vonppmsn0000y`, `vonpu0000000n`, `vonpu0000000y`, `vonrft300an`, `vonrpi100ay`, `vonrpi300an`, `vonrpi300ay`, `vonrpt300an`, `vonrpt300ay`, `voyc0i100an`, `voyc0i100ay`, `voyc0i300an`, `voyc0i300ay`, `voyc0t300an`, `voyd0i100an`, `voyifi12san`, `voyifi130an`, `voyifi330an`, `voyifi330ay`, `voyifii30an`, `voyipi11pan`, `voyipi11san`, `voyipi11say`, `voyipi130an`, `voyipi130ay`, `voyipi230ay`, `voyipi300ay`, `voyipi31pan`, `voyipi31san`, `voyipi31say`, `voyipi32pan`, `voyipi330an`, `voyipi330ay`, `voyipii30an`, `voyipt11pan`, `voyipt130an`, `voyipt31san`, `voyipt32san`, `voyipt330an`, `voyipt330ay`, `voyisi11pan`, `voyisi11san`, `voyisi11say`, `voyisi130an`, `voyisi230an`, `voyisi31san`, `voyisi31say`, `voyisi330an`, `voyisi330ay`, `voyist11san`, `voyist330an`, `voym0i12pay`, `voyn0i1000n`, `voyn0i3000n`, `voyn0t1000n`, `voyp0msnap00n`, `voypdfsnasnpn`, `voypdmpnasnpn`, `voypdmsnasnpn`, `voypdmsnasnpy`, `voypu0000000n`, `voyrfi100an`, `voyrpi100an`, `voyrpi300ay`, `vpnc0i100an`, `vpnc0i300an`, `vpnd0i100an`, `vpnd0t100an`, `vpnifi12san`, `vpnifi130an`, `vpnifi31pan`, `vpnifi330an`, `vpnift130an`, `vpnift31pan`, `vpnipi11pan`, `vpnipi11pay`, `vpnipi11san`, `vpnipi130an`, `vpnipi130ay`, `vpnipi330an`, `vpnipt11pan`, `vpnipt11san`, `vpnipt130an`, `vpnipt31pan`, `vpnisi11pan`, `vpnisi11san`, `vpnisi11say`, `vpnisi130an`, `vpnisi130ay`, `vpnisi230an`, `vpnisi31san`, `vpnisi330an`, `vpnist11san`, `vpnist130an`, `vpnist330an`, `vpnisti30an`, `vpnm0i12san`, `vpnm0i32san`, `vpnm0t32san`, `vpnn0i1000n`, `vpnn0i3000n`, `vpnn0t1000n`, `vpnn0t3000n`, `vpnpdfpnasnpn`, `vpnpdfsgasypn`, `vpnpdfsnasnpn`, `vpnpdmpnasnpn`, `vpnpdmsnasnpn`, `vpnpdmsnpsnpn`, `vpnppmsn0000n`, `vpnpu0000000n`, `vpyifi130an`, `vpyipi130an`, `vpyisi130an`, `vtnc0i100an`, `vtnc0i100ay`, `vtnc0t200an`, `vtnd0i100an`, `vtnifi11pay`, `vtnifi11san`, `vtnifi130an`, `vtnifi130ay`, `vtnift130an`, `vtnipi11pan`, `vtnipi11san`, `vtnipi130an`, `vtnipi130ay`, `vtnipi230an`, `vtnipii30an`, `vtnipt230an`, `vtnipt330an`, `vtnisi11san`, `vtnisi12san`, `vtnisi130an`, `vtnisi130ay`, `vtnist330an`, `vtnn0i1000n`, `vtnn0i100an`, `vtnn0t1000n`, `vtnpdfpnasnpn`, `vtnpdfsnasnpn`, `vtnpdmpnasnpn`, `vtnpdmsnasnpn`, `vtnppmsn0000n`, `vtnpu0000000n`, `vtnrpi100an`, `vtyc0i300ay`, `vtyifi330an`, `vtyipi11san`, `vtyipi130an`, `vtyipi130ay`, `vtyipi330an`, `vtyipi330ay`, `vtyipt11pay`, `vtyipt11say`, `vtyipt130an`, `vtyipt330an`, `vtyisi11san`, `vtyisi130an`, `vtyisi330an`, `vtyist11pan`, `vtyist11san`, `vtyist130an`, `vtyist330an`, `vtyn0i1000n`, `vtyn0i3000n`, `vtyn0t1000n`, `vtyn0t3000n`, `vtypdfsnasnpn`, `vtypdmsnasnpn`, `xf`, `xn`, `xo`, `xu`, `xx`, `ya`, `yd`, `yn`, `yp`, `yr`, `yv`, `z_`, `zb`, `zc`, `zd`, `zo`, `zq`, `zs`, `zx` |
| **`morphologizer`** | `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PUNCT`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART`, `POS=CCONJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADV\|PronType=Dem`, `POS=ADV\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=SCONJ`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Rel`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|POS=PRON\|PronType=Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=CCONJ\|Polarity=Neg`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|POS=PROPN`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Neg`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|POS=PRON\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Cnd\|POS=VERB\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Mood=Cnd\|POS=AUX\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|POS=AUX\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PART\|Polarity=Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|POS=PRON\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Polarity=Neg\|VerbForm=Inf`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `NumType=Ord\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=PROPN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Gender=Masc\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Mood=Nec\|POS=AUX\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Abbr=Yes\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=SYM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Foreign=Yes\|POS=X\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=CCONJ\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `POS=X\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=SYM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Foreign=Yes\|POS=X`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|POS=NOUN`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|POS=DET\|PronType=Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `POS=PART\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Int,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|POS=PRON\|PronType=Ind,Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|POS=DET\|PronType=Ind,Neg`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|POS=PRON\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Int`, `Case=Gen\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=ADV\|PronType=Tot`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|POS=DET\|PronType=Ind`, `Case=Acc\|POS=PRON\|PronType=Ind,Neg`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Conv`, `POS=INTJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PART\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `POS=ADV\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Nec\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `NumType=Mult\|POS=ADV`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Ind`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADP\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|POS=DET\|PronType=Rel`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|Typo=Yes`, `Case=Nom\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Inf`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|POS=DET\|PronType=Rel`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADV`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Coll\|POS=NOUN\|PronType=Int`, `POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Inf`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Mood=Nec\|POS=AUX\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|Typo=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=PUNCT\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|POS=PRON\|PronType=Rel`, `Mood=Imp\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `POS=SCONJ\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|POS=ADV\|Typo=Yes`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|POS=SYM\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=X`, `POS=ADV\|PronType=Neg\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Degree=Cmp\|POS=ADV\|Typo=Yes`, `POS=NOUN\|Typo=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=AUX\|Polarity=Neg\|VerbForm=Inf`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=ADV\|PronType=Int\|Typo=Yes`, `Case=Dat\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|POS=VERB`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `NumType=Ord\|POS=ADJ\|Typo=Yes`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Neg`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `2`, `4`, `6`, `8`, `10`, `11`, `13`, `15`, `18`, `20`, `22`, `26`, `28`, `31`, `34`, `37`, `39`, `41`, `43`, `45`, `47`, `49`, `52`, `54`, `56`, `58`, `60`, `61`, `64`, `66`, `67`, `69`, `71`, `73`, `74`, `76`, `78`, `80`, `83`, `85`, `86`, `87`, `89`, `91`, `93`, `95`, `97`, `98`, `101`, `104`, `107`, `109`, `110`, `112`, `113`, `116`, `119`, `122`, `124`, `126`, `128`, `131`, `134`, `138`, `140`, `142`, `145`, `147`, `149`, `152`, `153`, `155`, `157`, `160`, `163`, `164`, `166`, `168`, `172`, `175`, `177`, `179`, `181`, `183`, `184`, `187`, `190`, `193`, `195`, `196`, `198`, `200`, `201`, `203`, `205`, `208`, `209`, `210`, `212`, `214`, `218`, `220`, `222`, `224`, `227`, `230`, `233`, `235`, `237`, `239`, `243`, `245`, `246`, `248`, `250`, `251`, `253`, `255`, `256`, `259`, `260`, `262`, `265`, `269`, `272`, `274`, `275`, `276`, `278`, `281`, `283`, `287`, `291`, `293`, `295`, `298`, `300`, `303`, `305`, `306`, `308`, `311`, `313`, `314`, `316`, `319`, `322`, `324`, `326`, `328`, `329`, `332`, `333`, `335`, `337`, `339`, `341`, `343`, `345`, `348`, `349`, `350`, `352`, `354`, `355`, `358`, `359`, `361`, `362`, `365`, `368`, `370`, `372`, `374`, `376`, `377`, `379`, `381`, `382`, `384`, `387`, `389`, `390`, `392`, `396`, `398`, `400`, `401`, `405`, `408`, `409`, `410`, `412`, `415`, `417`, `419`, `420`, `422`, `425`, `426`, `428`, `430`, `432`, `434`, `436`, `438`, `439`, `440`, `443`, `445`, `447`, `448`, `450`, `452`, `454`, `455`, `458`, `461`, `462`, `464`, `466`, `468`, `469`, `471`, `473`, `474`, `476`, `477`, `480`, `483`, `484`, `485`, `487`, `488`, `491`, `494`, `495`, `497`, `498`, `499`, `500`, `501`, `502`, `504`, `506`, `508`, `510`, `511`, `512`, `513`, `515`, `517`, `518`, `519`, `520`, `524`, `525`, `527`, `529`, `532`, `535`, `536`, `537`, `539`, `540`, `541`, `543`, `546`, `548`, `549`, `550`, `551`, `553`, `555`, `556`, `560`, `562`, `564`, `566`, `567`, `569`, `571`, `572`, `575`, `577`, `579`, `582`, `584`, `585`, `586`, `587`, `588`, `593`, `595`, `596`, `599`, `601`, `603`, `605`, `607`, `610`, `613`, `616`, `619`, `622`, `624`, `625`, `627`, `629`, `631`, `633`, `636`, `638`, `640`, `642`, `644`, `645`, `646`, `650`, `651`, `653`, `655`, `657`, `660`, `662`, `665`, `667`, `668`, `670`, `673`, `676`, `678`, `680`, `681`, `682`, `684`, `687`, `688`, `690`, `691`, `692`, `693`, `694`, `697`, `699`, `700`, `701`, `702`, `705`, `706`, `708`, `709`, `712`, `714`, `715`, `718`, `721`, `723`, `725`, `726`, `728`, `729`, `731`, `732`, `733`, `735`, `736`, `737`, `738`, `739`, `741`, `743`, `745`, `746`, `748`, `749`, `750`, `751`, `753`, `754`, `755`, `756`, `758`, `759`, `760`, `761`, `762`, `764`, `766`, `767`, `768`, `771`, `773`, `774`, `775`, `776`, `777`, `778`, `779`, `781`, `783`, `785`, `786`, `787`, `790`, `791`, `792`, `793`, `795`, `796`, `798`, `799`, `800`, `801`, `804`, `805`, `806`, `807`, `808`, `809`, `810`, `811`, `812`, `813`, `814`, `816`, `823`, `825`, `826`, `828`, `830`, `831`, `832`, `835`, `838`, `839`, `840`, `842`, `843`, `844`, `846`, `847`, `849`, `851`, `853`, `855`, `856`, `858`, `859`, `861`, `862`, `864`, `865`, `866`, `869`, `870`, `871`, `873`, `876`, `878`, `879`, `880`, `881`, `882`, `883`, `886`, `888`, `889`, `891`, `894`, `897`, `898`, `899`, `900`, `901`, `904`, `907`, `908`, `909`, `912`, `914`, `916`, `917`, `919`, `921`, `922`, `923`, `925`, `928`, `930`, `932`, `933`, `936`, `937`, `938`, `940`, `942`, `943`, `944`, `946`, `947`, `949`, `951`, `953`, `955`, `956`, `957`, `961`, `963`, `966`, `967`, `968`, `969`, `972`, `974`, `976`, `977`, `979`, `981`, `982`, `983`, `986`, `988`, `989`, `990`, `991`, `995`, `998`, `999`, `1002`, `1004`, `1007`, `1008`, `1009`, `1012`, `1015`, `1016`, `1018`, `1019`, `1021`, `1024`, `1027`, `1028`, `1031`, `1034`, `1035`, `1037`, `1039`, `1041`, `1043`, `1044`, `1046`, `1049`, `1051`, `1053`, `1054`, `1056`, `1058`, `1060`, `1061`, `1062`, `1064`, `1065`, `1067`, `1069`, `1070`, `1071`, `1073`, `1074`, `1075`, `1076`, `1078`, `1079`, `1082`, `1083`, `1085`, `1088`, `1089`, `1092`, `1095`, `1097`, `1099`, `1100`, `1102`, `1104`, `1105`, `1108`, `1110`, `1114`, `1116`, `1117`, `1119`, `1121`, `1123`, `1127`, `1128`, `1129`, `1130`, `1131`, `1133`, `1135`, `1137`, `1139`, `1140`, `1142`, `1143`, `1145`, `1147`, `1149`, `1150`, `1153`, `1158`, `1160`, `1162`, `1167`, `1168`, `1169`, `1171`, `1172`, `1174`, `1176`, `1178`, `1180`, `1181`, `1182`, `1183`, `1185`, `1188`, `1191`, `1193`, `1195`, `1196`, `1197`, `1200`, `1201`, `1204`, `1205`, `1206`, `1208`, `1209`, `1211`, `1213`, `1216`, `1218`, `1220`, `1221`, `1222`, `1223`, `1225`, `1226`, `1227`, `1229`, `1230`, `1232`, `1233`, `1235`, `1236`, `1237`, `1238`, `1240`, `1241`, `1242`, `1243`, `1245`, `1247`, `1248`, `1250`, `1251`, `1252`, `1253`, `1255`, `1256`, `1257`, `1258`, `1259`, `1260`, `1261`, `1262`, `1263`, `1264`, `1267`, `1269`, `1270`, `1272`, `1274`, `523`, `1276`, `1279`, `1280`, `1281`, `1282`, `1284`, `1285`, `1287`, `1289`, `1292`, `1293`, `1294`, `1297`, `1298`, `1300`, `1301`, `1305`, `1307`, `1309`, `1310`, `1313`, `1314`, `1317`, `1318`, `1319`, `1321`, `1323`, `1324`, `1325`, `1326`, `1327`, `1329`, `1330`, `1333`, `1335`, `1337`, `1338`, `1340`, `1342`, `1344`, `1346`, `1347`, `1350`, `1351`, `1353`, `1356`, `1357`, `1358`, `1360`, `1362`, `1364`, `1367`, `1368`, `1369`, `1370`, `1371`, `1373`, `1375`, `1377`, `1378`, `1381`, `1383`, `1384`, `1386`, `1388`, `1390`, `1391`, `1392`, `1393`, `1395`, `1396`, `1398`, `1399`, `1401`, `1402`, `1403`, `1405`, `1406`, `1407`, `1408`, `1410`, `1411`, `1412`, `1413`, `1416`, `1418`, `1419`, `1422`, `1423`, `1425`, `1427`, `1428`, `1431`, `1432`, `1433`, `1434`, `1437`, `1438`, `1439`, `1441`, `1442`, `1443`, `1444`, `1445`, `1446`, `1448`, `1450`, `1452`, `1454`, `1455`, `1456`, `1457`, `1458`, `1460`, `1462`, `1466`, `1467`, `1469`, `1470`, `1474`, `1476`, `1477`, `1479`, `1481`, `1482`, `1483`, `1484`, `1485`, `1487`, `1489`, `1492`, `1493`, `1495`, `1496`, `1498`, `1499`, `1501`, `1502`, `1503`, `1506`, `1507`, `1508`, `1509`, `1511`, `1513`, `1514`, `1517`, `1518`, `1520`, `1523`, `1525`, `1527`, `1528`, `1530`, `1532`, `1534`, `1535`, `1536`, `1537`, `1539`, `1540`, `1542`, `1543`, `1545`, `1546`, `1547`, `1549`, `1551`, `1552`, `1553`, `1554`, `1557`, `1558`, `1560`, `1562`, `1564`, `1567`, `1569`, `1571`, `1572`, `1573`, `1574`, `1576`, `1577`, `1579`, `1581`, `1583`, `1584`, `1531`, `1585`, `1587`, `1588`, `1589`, `1591`, `1592`, `1595`, `1596`, `1598`, `1600`, `1601`, `1604`, `1605`, `1607`, `1608`, `1610`, `1612`, `1613`, `1616`, `1618`, `1619`, `1621`, `1623`, `1625`, `1626`, `1629`, `1630`, `1631`, `1633`, `1637`, `1639`, `1640`, `1642`, `1643`, `1645`, `1647`, `1648`, `1651`, `1652`, `1654`, `1655`, `1656`, `1657`, `1659`, `1661`, `1664`, `1665`, `1668`, `1670`, `1672`, `1673`, `1674`, `1675`, `1678`, `1679`, `1681`, `1682`, `1685`, `1688`, `1690`, `1692`, `1694`, `1695`, `1697`, `1699`, `1701`, 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`1915`, `1916`, `1918`, `1919`, `1920`, `1922`, `1925`, `1927`, `1929`, `1931`, `1933`, `1934`, `1936`, `1938`, `1939`, `1940`, `1943`, `1944`, `1945`, `1946`, `1947`, `1948`, `1950`, `1951`, `1953`, `1955`, `1956`, `1957`, `1960`, `1962`, `1963`, `1964`, `1965`, `1966`, `1969`, `1971`, `1973`, `1975`, `1976`, `1979`, `1980`, `1981`, `1982`, `1985`, `1986`, `1987`, `1988`, `1989`, `1991`, `1992`, `1993`, `1994`, `1995`, `1996`, `1999`, `2002`, `2003`, `2004`, `2006`, `2007`, `2008`, `2010`, `2011`, `2013`, `2015`, `2016`, `2017`, `2018`, `2020`, `2021`, `2023`, `2024`, `2028`, `2030`, `2031`, `2032`, `2033`, `2034`, `2037`, `2038`, `2040`, `2041`, `2043`, `2044`, `2047`, `2048`, `2049`, `2050`, `2051`, `2052`, `2053`, `2056`, `2058`, `2060`, `2062`, `2063`, `2065`, `2066`, `2067`, `2068`, `2069`, `2070`, `2071`, `2072`, `2075`, `2076`, `1806`, `2079`, `2081`, `2083`, `2086`, `2089`, `2090`, `2091`, `2092`, `2093`, `2095`, `2096`, `2097`, `2098`, `2099`, `2102`, `2103`, `2106`, `2107`, `2108`, `2110`, `2111`, `2112`, `2113`, `2114`, `2115`, `2116`, `2117`, `2118`, `2119`, `2120`, `2121`, `2122`, `2123`, `2124`, `2125`, `2127`, `2129`, `2132`, `2135`, `2136`, `2137`, `2138`, `2139`, `2141`, `2142`, `2143`, `2144`, `2147`, `2149`, `2151`, `2152`, `2153`, `2154`, `2157`, `2158`, `2159`, `2161`, `2162`, `2163`, `2166`, `2168`, `2169`, `2171`, `2174`, `2175`, `2176`, `2179`, `2181`, `2182`, `2184`, `2185`, `2186`, `2187`, `2189`, `2192`, `2193`, `2195`, `2196`, `2197`, `2199`, `2200`, `2202`, `2203`, `2206`, `2207`, `2208`, `2209`, `2212`, `2213`, `2216`, `2219`, `2220`, `2222`, `2225`, `2227`, `2228`, `2230`, `2231`, `2233`, `2235`, `2236`, `2237`, `2239`, `2241`, `2243`, `2245`, `2246`, `2248`, `2249`, `2250`, `2251`, `2252`, `2253`, `2254`, `2255`, `2256`, `2259`, `2264`, `2265`, `2269`, `2270`, `2272`, `2273`, `2274`, `2276`, `2277`, `2278`, `2279`, `2283`, `2285`, `2286`, `2288`, `2289`, `2290`, `2292`, `2293`, `2294`, `2295`, `2297`, `2299`, `2300`, `2303`, `2305`, `2306`, `2309`, `2310`, `2312`, `2314`, `2316`, `2318`, `2319`, `2320`, `2321`, `2322`, `2323`, `2325`, `2327`, `1961`, `2328`, `2329`, `2330`, `2332`, `2333`, `2334`, `2336`, `2338`, `2340`, `2342`, `2343`, `2345`, `2347`, `2349`, `2351`, `2353`, `2354`, `2357`, `2358`, `2359`, `2360`, `2362`, `2363`, `2364`, `2365`, `2366`, `2367`, `2368`, `2369`, `2372`, `2375`, `2376`, `2377`, `2379`, `2381`, `2382`, `2383`, `2384`, `2385`, `2386`, `2387`, `2388`, `2389`, `2390`, `2392`, `2393`, `2394`, `2396`, `2398`, `2399`, `2400`, `2404`, `2405`, `2406`, `2407`, `2408`, `2409`, `2410`, `2411`, `2412`, `2413`, `2414`, `2415`, `2416`, `2417`, `2418`, `2419`, `2420`, `2422`, `2423`, `2425`, `2426`, `2427`, `2428`, `2430`, `2431`, `2432`, `2435`, `2436`, `2438`, `2439`, `2441`, `2442`, `2443`, `2444`, `2446`, `2448`, `2449`, `2452`, `2454`, `2455`, `2456`, `2457`, `2458`, `2461`, `2463`, `2464`, `2467`, `2468`, `2470`, `2472`, `2475`, `2477`, `2478`, `2479`, `2481`, `2483`, `2485`, `2486`, `2488`, `2489`, `2490`, `2491`, `2492`, `2493`, `2494`, `2495`, `2496`, `2497`, `2498`, `2499`, `2500`, `2501`, `2503`, `2504`, `2505`, `2506`, `2507`, `2508`, `2510`, `2512`, `2513`, `2514`, `2515`, `2516`, `2517`, `2518`, `2519`, `2520`, `2521`, `2522`, `2523`, `2524`, `2526`, `2527`, `2528`, `2529`, `2530`, `2531`, `2534`, `2537`, `2540`, `2542`, `2543`, `2544`, `2546`, `2548`, `2549`, `2550`, `2551`, `2552`, `2553`, `2554`, `2555`, `2558`, `2560`, `2561`, `2562`, `2563`, `2565`, `2567`, `2570`, `2572`, `2573`, `2575`, `2576`, `2577`, `2578`, `2579`, `2581`, `2583`, `2584`, `2586`, `2588`, `2589`, `2592`, `2593`, `2595`, `2597`, `2598`, `2601`, `2603`, `2604`, `2605`, `2606`, `2607`, `2608`, `2610`, `2611`, `2612`, `2613`, `2614`, `2615`, `2617`, `2618`, `2619`, `2620`, `2623`, `2624`, `2626`, `2628`, `2629`, `2630`, `2631`, `2632`, `2633`, `2634`, `2636`, `2637`, `2639`, `2640`, `2642`, `2643`, `2644`, `2645`, `2646`, `2649`, `2650`, `2652`, `2653`, `2654`, `2655`, `2656`, `2657`, `2659`, `2660`, `2663`, `2664`, `2665`, `2666`, `2668`, `2669`, `2671`, `2672`, `2673`, `2675`, `2677`, `2678`, `2679`, `2680`, `2681`, `2682`, `2685`, `2686`, `2687`, `2688`, `2689`, `2690`, `2691`, `2693`, `2694`, `2695`, `2697`, `2699`, `2700`, `2701`, `2703`, `2704`, `2706`, `2707`, `2708`, `2709`, `2711`, `2712`, `2713`, `2716`, `2718`, `2720`, `2721`, `2722`, `2724`, `2725`, `2726`, `2728`, `2731`, `2732`, `2735`, `2736`, `2737`, `2740`, `2741`, `2742`, `2744`, `2746`, `2749`, `2750`, `2753`, `2756`, `2757`, `2760`, `2763`, `2764`, `2765`, `2766`, `2769`, `2771`, `2772`, `2773`, `2775`, `2778`, `2779`, `2780`, `2781`, `2437`, `2782`, `2784`, `2786`, `2787`, `2788`, `2789`, `2790`, `2792`, `2793`, `2795`, `2796`, `2797`, `2799`, `2800`, `2804`, `2805`, `2806`, `2807`, `2029`, `2808`, `2809`, `2812`, `2814`, `2816`, `2819`, `2820`, `2822`, `2823`, `2824`, `2825`, `2827`, `2829`, `2831`, `2832`, `2833`, `2835`, `2836`, `2838`, `2839`, `2395`, `2841`, `2843`, `2844`, `2846`, `2847`, `2848`, 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`4749`, `4750`, `70`, `84`, `4751`, `4752`, `4753`, `4754`, `4756`, `4758`, `4760`, `4761`, `4762`, `4764`, `4766`, `4769`, `4771`, `4772`, `4774`, `4775`, `4776`, `4778`, `4779`, `4781`, `4782` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.80 |
| `TOKEN_P` | 99.79 |
| `TOKEN_R` | 99.81 |
| `TOKEN_ACC` | 99.97 |
| `SENTS_F` | 97.77 |
| `SENTS_P` | 98.24 |
| `SENTS_R` | 97.30 |
| `TAG_ACC` | 91.59 |
| `POS_ACC` | 97.94 |
| `MORPH_ACC` | 95.69 |
| `DEP_UAS` | 91.30 |
| `DEP_LAS` | 87.75 |
| `LEMMA_ACC` | 95.39 |
|
{"language": ["lv"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/lv_udv25_latvianlvtb_trf
|
[
"spacy",
"token-classification",
"lv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"lv"
] |
TAGS
#spacy #token-classification #lv #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Latvian-LVTB
### Label Scheme
View label scheme (6012 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (6012 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #lv #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6012 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #lv #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (6012 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Norwegian-Bokmaal
| Feature | Description |
| --- | --- |
| **Name** | `nb_udv25_norwegianbokmaal_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1240 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` |
| **`morphologizer`** | `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `POS=ADP`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=PROPN`, `POS=X`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=ADV`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `POS=VERB\|VerbForm=Part`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|POS=PROPN`, `POS=NOUN`, `Gender=Masc\|POS=PROPN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=PROPN`, `POS=PART\|Polarity=Neg`, `Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Case=Gen\|Gender=Fem\|POS=PROPN`, `Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Degree=Sup\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Neut\|POS=PROPN`, `Number=Plur\|POS=DET\|PronType=Int`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Definite=Def\|POS=DET\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Abbr=Yes\|Case=Gen\|POS=PROPN`, `Animacy=Hum\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|POS=ADJ`, `POS=ADJ\|VerbForm=Part`, `Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=ADP`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=AUX\|VerbForm=Part`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `Number=Plur\|POS=DET\|PronType=Ind`, `Degree=Pos\|POS=ADJ`, `Animacy=Hum\|Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Hum\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Hum\|Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=DET\|Polarity=Neg\|PronType=Neg`, `NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=DET\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=DET\|Polarity=Neg\|PronType=Neg`, `Definite=Def\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Inf`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=DET\|PronType=Prs`, `POS=SYM`, `Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Hum\|Case=Nom\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=ADV`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|POS=DET\|PronType=Prs`, `Animacy=Hum\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Neut\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Def\|NumType=Card\|POS=NUM`, `Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Hum\|Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|Polarity=Neg\|PronType=Neg,Prs`, `Number=Plur\|POS=PRON\|Person=3\|Polarity=Neg\|PronType=Neg,Prs`, `Definite=Def\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Hum\|Number=Sing\|POS=PRON\|PronType=Art,Prs`, `Mood=Imp\|POS=AUX\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs,Tot`, `Number=Plur\|POS=ADJ`, `Gender=Masc\|POS=NOUN`, `Abbr=Yes\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `POS=INTJ`, `Animacy=Hum\|Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Hum\|Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=ADJ`, `Animacy=Hum\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Hum\|Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=PRON\|Polarity=Neg\|PronType=Neg`, `Case=Gen\|POS=NOUN`, `Definite=Ind\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|POS=PROPN`, `Animacy=Hum\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Hum\|Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Hum\|Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADJ`, `Animacy=Hum\|POS=PRON\|PronType=Int`, `POS=DET\|PronType=Ind`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs,Tot`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|Polarity=Neg\|PronType=Neg`, `Number=Plur\|POS=NOUN`, `POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Definite=Def\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem,Ind`, `Animacy=Hum\|POS=PRON\|Poss=Yes\|PronType=Int`, `Abbr=Yes\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Abbr=Yes\|Definite=Def,Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Rcp`, `Definite=Ind\|Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Abbr=Yes\|Definite=Def,Ind\|Gender=Neut\|Number=Plur,Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Tot`, `Abbr=Yes\|Definite=Def,Ind\|Gender=Masc\|Number=Plur,Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Animacy=Hum\|Case=Gen,Nom\|Number=Sing\|POS=PRON\|PronType=Art,Prs`, `Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Hum\|Case=Gen\|Number=Sing\|POS=PRON\|PronType=Art,Prs`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Ind\|Gender=Masc\|POS=NOUN`, `Definite=Def\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Abbr=Yes\|Gender=Masc\|POS=NOUN`, `Abbr=Yes\|Case=Gen\|POS=NOUN`, `Abbr=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|Degree=Pos\|POS=ADJ`, `Case=Gen\|Gender=Fem\|POS=NOUN`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=NOUN` |
| **`parser`** | `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `reparandum`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `6`, `8`, `10`, `12`, `14`, `16`, `18`, `20`, `22`, `24`, `26`, `28`, `32`, `34`, `36`, `38`, `40`, `42`, `44`, `47`, `49`, `51`, `52`, `54`, `56`, `58`, `59`, `60`, `62`, `64`, `65`, `67`, `69`, `70`, `71`, `73`, `75`, `78`, `81`, `83`, `87`, `89`, `93`, `96`, `98`, `99`, `100`, `102`, `104`, `106`, `110`, `112`, `115`, `116`, `118`, `120`, `122`, `124`, `128`, `131`, `133`, `135`, `137`, `140`, `142`, `143`, `144`, `145`, `147`, `149`, `151`, `153`, `154`, `156`, `158`, `159`, `162`, `165`, `166`, `168`, `169`, `171`, `173`, `175`, `177`, `179`, `180`, `182`, `184`, `185`, `186`, `187`, `189`, `190`, `192`, `193`, `194`, `195`, `198`, `199`, `201`, `203`, `204`, `207`, `209`, `211`, `214`, `217`, `218`, `219`, `220`, `223`, `225`, `227`, `228`, `229`, `231`, `232`, `233`, `235`, `236`, `239`, `240`, `243`, `246`, `248`, `249`, `250`, `251`, `254`, `257`, `259`, `261`, `263`, `266`, `267`, `270`, `272`, `274`, `275`, `276`, `279`, `282`, `283`, `284`, `285`, `286`, `289`, `290`, `291`, `292`, `294`, `298`, `302`, `304`, `305`, `306`, `309`, `310`, `311`, `314`, `315`, `316`, `317`, `319`, `320`, `322`, `46`, `324`, `326`, `327`, `329`, `330`, `331`, `332`, `334`, `335`, `336`, `337`, `339`, `340`, `341`, `343`, `344`, `346`, `348`, `349`, `352`, `353`, `354`, `356`, `357`, `358`, `359`, `361`, `363`, `364`, `365`, `367`, `369`, `372`, `374`, `375`, `376`, `377`, `378`, `380`, `381`, `384`, `385`, `387`, `389`, `391`, `394`, `396`, `397`, `400`, `403`, `405`, `406`, `408`, `409`, `410`, `411`, `413`, `415`, `416`, `418`, `420`, `422`, `423`, `424`, `426`, `428`, `429`, `431`, `432`, `433`, `434`, `435`, `437`, `438`, `440`, `442`, `445`, `446`, `448`, `449`, `450`, `451`, `452`, `453`, `456`, `458`, `459`, `460`, `461`, `462`, `465`, `466`, `468`, `469`, `471`, `474`, `475`, `476`, `477`, `479`, `480`, `482`, `485`, `486`, `488`, `489`, `491`, `492`, `493`, `494`, `495`, `497`, `498`, `499`, `500`, `502`, `503`, `504`, `505`, `506`, `507`, `509`, `510`, `511`, `513`, `517`, `518`, `519`, `521`, `522`, `525`, `526`, `528`, `529`, `533`, `537`, `539`, `541`, `543`, `545`, `546`, `547`, `549`, `550`, `552`, `553`, `554`, `555`, `557`, `558`, `559`, `560`, `561`, `562`, `563`, `564`, `566`, `568`, `570`, `574`, `575`, `576`, `577`, `579`, `581`, `582`, `583`, `585`, `586`, `587`, `589`, `590`, `591`, `593`, `595`, `597`, `599`, `602`, `603`, `604`, `605`, `607`, `608`, `610`, `611`, `612`, `614`, `616`, `617`, `619`, `620`, `621`, `624`, `626`, `628`, `630`, `632`, `635`, `636`, `639`, `640`, `642`, `645`, `647`, `650`, `651`, `652`, `655`, `657`, `658`, `659`, `661`, `662`, `663`, `664`, `665`, `666`, `667`, `668`, `669`, `670`, `672`, `673`, `676`, `677`, `678`, `681`, `682`, `683`, `684`, `686`, `687`, `688`, `690`, `692`, `693`, `694`, `695`, `697`, `698`, `699`, `700`, `701`, `702`, `704`, `705`, `707`, `709`, `710`, `711`, `712`, `713`, `714`, `715`, `716`, `717`, `719`, `721`, `723`, `726`, `728`, `729`, `730`, `731`, `732`, `733`, `734`, `736`, `737`, `738`, `739`, `741`, `742`, `743`, `745`, `746`, `747`, `748`, `749`, `750`, `751`, `753`, `754`, `757`, `759`, `760`, `761`, `762`, `763`, `765`, `767`, `768`, `770`, `771`, `772`, `773`, `775`, `777`, `778`, `779`, `780`, `781`, `783`, `784`, `785`, `786`, `787`, `788`, `789`, `790`, `791`, `792`, `795`, `798`, `799`, `801`, `802`, `803`, `805`, `806`, `809`, `811`, `812`, `814`, `815`, `816`, `817`, `818`, `820`, `822`, `823`, `824`, `825`, `826`, `827`, `828`, `829`, `830`, `832`, `833`, `836`, `838`, `839`, `840`, `841`, `843`, `844`, `845`, `846`, `847`, `848`, `850`, `851`, `852`, `853`, `854`, `855`, `857`, `858`, `859`, `860`, `862`, `864`, `865`, `868`, `869`, `870`, `871`, `872`, `873`, `874`, `876`, `877`, `878`, `881`, `883`, `884`, `885`, `886`, `887`, `888`, `889`, `890`, `892`, `893`, `894`, `896`, `897`, `898`, `899`, `901`, `902`, `905`, `908`, `911`, `912`, `913`, `915`, `916`, `917`, `918`, `919`, 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`1308`, `1309`, `1311`, `1312`, `1313`, `1314`, `1315`, `1316`, `1317`, `1318`, `1320`, `1321`, `1322`, `1324`, `1325`, `1326`, `1329`, `1331`, `1334`, `1336`, `1337`, `1340`, `1341`, `1342`, `1343`, `1346`, `1348`, `1349`, `1350`, `1352`, `1353`, `1355`, `1357`, `1358`, `1359`, `1361`, `965`, `1362`, `1363`, `1364`, `1366`, `1369`, `1370`, `1371`, `1372`, `1373`, `1375`, `1376`, `1377`, `1379`, `1381`, `1382`, `1383`, `1385`, `1387`, `1388`, `1390`, `1392`, `1393`, `1394`, `1395`, `1396`, `1397`, `1398`, `1399`, `1400`, `1402`, `1403`, `1405`, `1406`, `1407`, `1409`, `1411`, `1412`, `1413`, `1414`, `1418`, `1419`, `1420`, `1421`, `1423`, `1424`, `1425`, `1427`, `1428`, `1429`, `1430`, `1432`, `1433`, `1435`, `1437`, `1438`, `1441`, `1442`, `1444`, `1446`, `1447`, `1449`, `1453`, `1455`, `1457`, `1458`, `1460`, `1462`, `1463`, `1464`, `1466`, `1469`, `1470`, `1471`, `1473`, `1475`, `1476`, `1477`, `1478`, `1479`, `1482`, `1483`, `1484`, `1486`, `1487`, `1489`, `1491`, `1493`, `1494`, `1495`, `1496`, `1497`, `1498`, `1499`, `1500`, `1501`, `1502`, `1503`, `1504`, `1506`, `1507`, `1508`, `1510`, `1511`, `1512`, `1513`, `1516`, `1517`, `1518`, `1519`, `1520`, `1521`, `1522`, `1523`, `849` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 100.00 |
| `TOKEN_P` | 100.00 |
| `TOKEN_R` | 100.00 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 98.80 |
| `SENTS_P` | 98.84 |
| `SENTS_R` | 98.75 |
| `TAG_ACC` | 99.16 |
| `POS_ACC` | 99.13 |
| `MORPH_ACC` | 98.42 |
| `DEP_UAS` | 95.63 |
| `DEP_LAS` | 93.91 |
| `LEMMA_ACC` | 98.82 |
|
{"language": ["nb"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/nb_udv25_norwegianbokmaal_trf
|
[
"spacy",
"token-classification",
"nb",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"nb"
] |
TAGS
#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Norwegian-Bokmaal
### Label Scheme
View label scheme (1240 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1240 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1240 labels for 6 components)",
"### Accuracy"
] |
[
33,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1240 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Norwegian-Nynorsk
| Feature | Description |
| --- | --- |
| **Name** | `nb_udv25_norwegiannynorsk_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1400 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` |
| **`morphologizer`** | `Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=ADP`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Gender=Masc\|POS=NOUN`, `POS=CCONJ`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Animacy=Hum\|Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `POS=PROPN`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Hum\|Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=ADV`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `POS=PRON\|PronType=Rel`, `POS=VERB\|VerbForm=Part`, `Animacy=Hum\|Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|POS=NOUN`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADV`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADJ`, `POS=ADJ\|VerbForm=Part`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=DET\|PronType=Int`, `POS=AUX\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=ADJ`, `POS=PART\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=INTJ`, `Animacy=Hum\|Number=Sing\|POS=PRON\|PronType=Art,Prs`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|POS=PROPN`, `Animacy=Hum\|Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=DET\|Polarity=Neg\|PronType=Neg`, `Animacy=Hum\|Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Hum\|Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Hum\|Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PRON\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=DET\|PronType=Tot`, `Definite=Ind\|Degree=Sup\|POS=ADJ`, `NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Def\|POS=DET\|PronType=Dem`, `POS=X`, `Case=Gen\|Gender=Masc\|POS=NOUN`, `POS=AUX\|VerbForm=Part`, `Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Tot`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Hum\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `Definite=Ind\|Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `POS=SYM`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Abbr=Yes\|POS=PRON\|PronType=Prs`, `Abbr=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `POS=ADJ`, `Gender=Neut\|POS=NOUN`, `Animacy=Hum\|Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|POS=NOUN`, `Degree=Pos\|POS=ADJ`, `Definite=Def\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Hum\|Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Hum\|Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Def\|NumType=Card\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs,Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|Polarity=Neg\|PronType=Neg`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Hum\|POS=PRON\|PronType=Int`, `Mood=Imp\|POS=AUX\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Abbr=Yes\|POS=CCONJ`, `Number=Plur\|POS=PRON\|Person=3\|Polarity=Neg\|PronType=Neg,Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=DET\|Polarity=Neg\|PronType=Neg`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Polarity=Neg\|PronType=Neg,Prs`, `Abbr=Yes\|Case=Gen\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Def\|POS=ADV`, `Number=Sing\|POS=PRON\|Polarity=Neg\|PronType=Neg`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Definite=Def,Ind\|Gender=Neut\|Number=Plur,Sing\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADP`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Animacy=Hum\|Case=Nom\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Art,Prs`, `Definite=Def\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Gender=Masc\|POS=NOUN`, `Case=Gen\|POS=NOUN`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `POS=PRON\|PronType=Prs`, `POS=ADV\|VerbForm=Inf`, `Degree=Sup\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|POS=NOUN`, `Animacy=Hum\|Case=Nom\|Gender=Masc\|POS=PRON\|Person=3\|PronType=Prs`, `Abbr=Yes\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Gender=Neut\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|POS=NOUN`, `Case=Gen\|Gender=Neut\|POS=NOUN`, `Definite=Def\|POS=ADJ\|VerbForm=Part`, `POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `NumType=Card\|POS=NUM\|PronType=Dem`, `Definite=Ind\|Number=Sing\|POS=VERB\|VerbForm=Part` |
| **`parser`** | `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `reparandum`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `2`, `4`, `5`, `7`, `9`, `11`, `13`, `17`, `19`, `21`, `23`, `25`, `29`, `31`, `35`, `38`, `40`, `42`, `44`, `45`, `47`, `49`, `51`, `53`, `55`, `56`, `58`, `62`, `65`, `67`, `70`, `72`, `75`, `77`, `79`, `80`, `82`, `84`, `86`, `88`, `90`, `92`, `95`, `98`, `100`, `102`, `104`, `106`, `107`, `108`, `110`, `112`, `114`, `119`, `121`, `123`, `126`, `128`, `130`, `132`, `134`, `136`, `138`, `141`, `143`, `145`, `146`, `148`, `150`, `152`, `154`, `156`, `158`, `160`, `162`, `164`, `165`, `167`, `170`, `171`, `172`, `174`, `175`, `177`, `178`, `180`, `182`, `185`, `186`, `189`, `191`, `193`, `196`, `198`, `202`, `203`, `207`, `209`, `212`, `214`, `217`, `220`, `222`, `224`, `225`, `227`, `228`, `231`, `233`, `235`, `238`, `239`, `241`, `245`, `247`, `248`, `250`, `253`, `255`, `256`, `259`, `262`, `263`, `265`, `266`, `268`, `270`, `271`, `274`, `275`, `277`, `279`, `280`, `282`, `285`, `288`, `290`, `292`, `294`, `296`, `298`, `299`, `302`, `306`, `309`, `310`, `313`, `316`, `318`, `320`, `321`, `322`, `323`, `324`, `327`, `329`, `330`, `332`, `334`, `335`, `337`, `340`, `341`, `342`, `343`, `345`, `346`, `347`, `349`, `350`, `352`, `354`, `355`, `356`, `357`, `358`, `360`, `362`, `363`, `365`, `366`, `368`, `370`, `371`, `373`, `375`, `378`, `379`, `380`, `383`, `384`, `385`, `386`, `388`, `389`, `392`, `393`, `394`, `395`, `397`, `398`, `399`, `400`, `401`, `403`, `405`, `407`, `408`, `410`, `412`, `413`, `415`, `417`, `419`, `420`, `423`, `424`, `425`, `426`, `427`, `429`, `431`, `432`, `433`, `435`, `438`, `440`, `442`, `444`, `446`, `447`, `448`, `449`, `451`, `452`, `454`, `456`, `458`, `459`, `462`, `465`, `466`, `468`, `469`, `470`, `471`, `473`, `475`, `476`, `478`, `479`, `481`, `482`, `484`, `485`, `488`, `489`, `490`, `492`, `495`, `497`, `501`, `503`, `505`, `507`, `508`, `510`, `512`, `513`, `515`, `517`, `518`, `520`, `521`, `523`, `524`, `526`, `527`, `529`, `530`, `531`, `534`, `536`, `537`, `538`, `539`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.96 |
| `TOKEN_P` | 99.96 |
| `TOKEN_R` | 99.96 |
| `TOKEN_ACC` | 99.99 |
| `SENTS_F` | 99.10 |
| `SENTS_P` | 99.15 |
| `SENTS_R` | 99.05 |
| `TAG_ACC` | 98.33 |
| `POS_ACC` | 98.34 |
| `MORPH_ACC` | 97.91 |
| `DEP_UAS` | 94.11 |
| `DEP_LAS` | 92.14 |
| `LEMMA_ACC` | 98.28 |
|
{"language": ["nb"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/nb_udv25_norwegiannynorsk_trf
|
[
"spacy",
"token-classification",
"nb",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"nb"
] |
TAGS
#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Norwegian-Nynorsk
### Label Scheme
View label scheme (1400 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1400 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1400 labels for 6 components)",
"### Accuracy"
] |
[
33,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1400 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Dutch-Alpino
| Feature | Description |
| --- | --- |
| **Name** | `nl_udv25_dutchalpino_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1712 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ\|nom\|basis\|met-e\|mv-n`, `ADJ\|nom\|basis\|met-e\|zonder-n\|stan`, `ADJ\|nom\|basis\|zonder\|zonder-n`, `ADJ\|nom\|comp\|met-e\|mv-n`, `ADJ\|nom\|comp\|met-e\|zonder-n\|stan`, `ADJ\|nom\|sup\|met-e\|mv-n`, `ADJ\|nom\|sup\|met-e\|zonder-n\|stan`, `ADJ\|nom\|sup\|zonder\|zonder-n`, `ADJ\|postnom\|basis\|met-s`, `ADJ\|postnom\|basis\|zonder`, `ADJ\|postnom\|comp\|met-s`, `ADJ\|prenom\|basis\|met-e\|stan`, `ADJ\|prenom\|basis\|zonder`, `ADJ\|prenom\|comp\|met-e\|stan`, `ADJ\|prenom\|comp\|zonder`, `ADJ\|prenom\|sup\|met-e\|stan`, `ADJ\|vrij\|basis\|zonder`, `ADJ\|vrij\|comp\|zonder`, `ADJ\|vrij\|dim\|zonder`, `ADJ\|vrij\|sup\|zonder`, `BW`, `LET`, `LID\|bep\|dat\|evmo`, `LID\|bep\|gen\|evmo`, `LID\|bep\|gen\|rest3`, `LID\|bep\|stan\|evon`, `LID\|bep\|stan\|rest`, `LID\|onbep\|stan\|agr`, `N\|eigen\|ev\|basis\|gen`, `N\|eigen\|ev\|basis\|genus\|stan`, `N\|eigen\|ev\|basis\|onz\|stan`, `N\|eigen\|ev\|basis\|zijd\|stan`, `N\|eigen\|ev\|dim\|onz\|stan`, `N\|eigen\|mv\|basis`, `N\|soort\|ev\|basis\|dat`, `N\|soort\|ev\|basis\|gen`, `N\|soort\|ev\|basis\|genus\|stan`, `N\|soort\|ev\|basis\|onz\|stan`, `N\|soort\|ev\|basis\|zijd\|stan`, `N\|soort\|ev\|dim\|onz\|stan`, `N\|soort\|mv\|basis`, `N\|soort\|mv\|dim`, `SPEC\|afgebr`, `SPEC\|afk`, `SPEC\|deeleigen`, `SPEC\|enof`, `SPEC\|meta`, `SPEC\|symb`, `SPEC\|vreemd`, `TSW`, `TW\|hoofd\|nom\|mv-n\|basis`, `TW\|hoofd\|nom\|mv-n\|dim`, `TW\|hoofd\|nom\|zonder-n\|basis`, `TW\|hoofd\|nom\|zonder-n\|dim`, `TW\|hoofd\|prenom\|stan`, `TW\|hoofd\|vrij`, `TW\|rang\|nom\|mv-n`, `TW\|rang\|nom\|zonder-n`, `TW\|rang\|prenom\|stan`, `VG\|neven`, `VG\|onder`, `VNW\|aanw\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|aanw\|adv-pron\|stan\|red\|3\|getal`, `VNW\|aanw\|det\|dat\|nom\|met-e\|zonder-n`, `VNW\|aanw\|det\|dat\|prenom\|met-e\|evmo`, `VNW\|aanw\|det\|gen\|prenom\|met-e\|rest3`, `VNW\|aanw\|det\|stan\|nom\|met-e\|mv-n`, `VNW\|aanw\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|aanw\|det\|stan\|prenom\|met-e\|rest`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|agr`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|evon`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|rest`, `VNW\|aanw\|det\|stan\|vrij\|zonder`, `VNW\|aanw\|pron\|gen\|vol\|3m\|ev`, `VNW\|aanw\|pron\|stan\|vol\|3o\|ev`, `VNW\|aanw\|pron\|stan\|vol\|3\|getal`, `VNW\|betr\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|betr\|det\|stan\|nom\|zonder\|zonder-n`, `VNW\|betr\|pron\|stan\|vol\|3\|ev`, `VNW\|betr\|pron\|stan\|vol\|persoon\|getal`, `VNW\|bez\|det\|gen\|vol\|3\|ev\|prenom\|met-e\|rest3`, `VNW\|bez\|det\|stan\|nadr\|2v\|mv\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|red\|1\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|red\|2v\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|red\|3\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|1\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|1\|mv\|prenom\|met-e\|rest`, `VNW\|bez\|det\|stan\|vol\|1\|mv\|prenom\|zonder\|evon`, `VNW\|bez\|det\|stan\|vol\|2v\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|2\|getal\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|3m\|ev\|nom\|met-e\|zonder-n`, `VNW\|bez\|det\|stan\|vol\|3v\|ev\|nom\|met-e\|zonder-n`, `VNW\|bez\|det\|stan\|vol\|3\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|3\|mv\|prenom\|zonder\|agr`, `VNW\|onbep\|adv-pron\|gen\|red\|3\|getal`, `VNW\|onbep\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|onbep\|det\|stan\|nom\|met-e\|mv-n`, `VNW\|onbep\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|agr`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|evz`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|mv`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|rest`, `VNW\|onbep\|det\|stan\|prenom\|zonder\|agr`, `VNW\|onbep\|det\|stan\|prenom\|zonder\|evon`, `VNW\|onbep\|det\|stan\|vrij\|zonder`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|mv-n\|basis`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|mv-n\|sup`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|zonder-n\|basis`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|zonder-n\|sup`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|agr\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|agr\|comp`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|agr\|sup`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|mv\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|zonder\|agr\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|zonder\|agr\|comp`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|basis`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|comp`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|sup`, `VNW\|onbep\|pron\|gen\|vol\|3p\|ev`, `VNW\|onbep\|pron\|stan\|vol\|3o\|ev`, `VNW\|onbep\|pron\|stan\|vol\|3p\|ev`, `VNW\|pers\|pron\|gen\|vol\|2\|getal`, `VNW\|pers\|pron\|nomin\|nadr\|3m\|ev\|masc`, `VNW\|pers\|pron\|nomin\|red\|1\|mv`, `VNW\|pers\|pron\|nomin\|red\|2v\|ev`, `VNW\|pers\|pron\|nomin\|red\|2\|getal`, `VNW\|pers\|pron\|nomin\|red\|3p\|ev\|masc`, `VNW\|pers\|pron\|nomin\|red\|3\|ev\|masc`, `VNW\|pers\|pron\|nomin\|vol\|1\|ev`, `VNW\|pers\|pron\|nomin\|vol\|1\|mv`, `VNW\|pers\|pron\|nomin\|vol\|2b\|getal`, `VNW\|pers\|pron\|nomin\|vol\|2v\|ev`, `VNW\|pers\|pron\|nomin\|vol\|2\|getal`, `VNW\|pers\|pron\|nomin\|vol\|3p\|mv`, `VNW\|pers\|pron\|nomin\|vol\|3v\|ev\|fem`, `VNW\|pers\|pron\|nomin\|vol\|3\|ev\|masc`, `VNW\|pers\|pron\|obl\|nadr\|3m\|ev\|masc`, `VNW\|pers\|pron\|obl\|red\|3\|ev\|masc`, `VNW\|pers\|pron\|obl\|vol\|2v\|ev`, `VNW\|pers\|pron\|obl\|vol\|3p\|mv`, `VNW\|pers\|pron\|obl\|vol\|3\|ev\|masc`, `VNW\|pers\|pron\|obl\|vol\|3\|getal\|fem`, `VNW\|pers\|pron\|stan\|nadr\|2v\|mv`, `VNW\|pers\|pron\|stan\|red\|3\|ev\|fem`, `VNW\|pers\|pron\|stan\|red\|3\|ev\|onz`, `VNW\|pers\|pron\|stan\|red\|3\|mv`, `VNW\|pr\|pron\|obl\|nadr\|1\|ev`, `VNW\|pr\|pron\|obl\|nadr\|2v\|getal`, `VNW\|pr\|pron\|obl\|nadr\|2\|getal`, `VNW\|pr\|pron\|obl\|red\|1\|ev`, `VNW\|pr\|pron\|obl\|red\|2v\|getal`, `VNW\|pr\|pron\|obl\|vol\|1\|ev`, `VNW\|pr\|pron\|obl\|vol\|1\|mv`, `VNW\|pr\|pron\|obl\|vol\|2\|getal`, `VNW\|recip\|pron\|gen\|vol\|persoon\|mv`, `VNW\|recip\|pron\|obl\|vol\|persoon\|mv`, `VNW\|refl\|pron\|obl\|nadr\|3\|getal`, `VNW\|refl\|pron\|obl\|red\|3\|getal`, `VNW\|vb\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|vb\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|vb\|det\|stan\|prenom\|met-e\|rest`, `VNW\|vb\|det\|stan\|prenom\|zonder\|evon`, `VNW\|vb\|pron\|gen\|vol\|3m\|ev`, `VNW\|vb\|pron\|gen\|vol\|3p\|mv`, `VNW\|vb\|pron\|gen\|vol\|3v\|ev`, `VNW\|vb\|pron\|stan\|vol\|3o\|ev`, `VNW\|vb\|pron\|stan\|vol\|3p\|getal`, `VZ\|fin`, `VZ\|init`, `VZ\|versm`, `WW\|inf\|nom\|zonder\|zonder-n`, `WW\|inf\|prenom\|met-e`, `WW\|inf\|vrij\|zonder`, `WW\|od\|nom\|met-e\|mv-n`, `WW\|od\|nom\|met-e\|zonder-n`, `WW\|od\|prenom\|met-e`, `WW\|od\|prenom\|zonder`, `WW\|od\|vrij\|zonder`, `WW\|pv\|conj\|ev`, `WW\|pv\|tgw\|ev`, `WW\|pv\|tgw\|met-t`, `WW\|pv\|tgw\|mv`, `WW\|pv\|verl\|ev`, `WW\|pv\|verl\|mv`, `WW\|vd\|nom\|met-e\|mv-n`, `WW\|vd\|nom\|met-e\|zonder-n`, `WW\|vd\|prenom\|met-e`, `WW\|vd\|prenom\|zonder`, `WW\|vd\|vrij\|zonder` |
| **`morphologizer`** | `POS=PRON\|Person=3\|PronType=Dem`, `Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `POS=VERB\|VerbForm=Part`, `POS=PUNCT`, `Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=ADP`, `POS=NUM`, `Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `POS=SCONJ`, `Definite=Def\|POS=DET`, `Gender=Com\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Degree=Pos\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=PROPN`, `Gender=Com\|Number=Sing\|POS=PROPN`, `POS=AUX\|VerbForm=Inf`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `POS=DET`, `Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PRON\|Person=3\|PronType=Prs`, `POS=CCONJ`, `Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Ind`, `Degree=Cmp\|POS=ADJ`, `Case=Nom\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|POS=DET`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `POS=PRON\|PronType=Rel`, `Case=Acc\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Gender=Com,Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PROPN`, `POS=PRON\|PronType=Ind`, `POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|POS=PRON\|PronType=Rcp`, `Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=NOUN`, `POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `Abbr=Yes\|POS=X`, `Gender=Com,Neut\|Number=Sing\|POS=PROPN`, `Degree=Sup\|POS=ADJ`, `Foreign=Yes\|POS=X`, `POS=ADJ`, `Number=Sing\|POS=PROPN`, `POS=PRON\|PronType=Dem`, `POS=AUX\|VerbForm=Part`, `POS=PRON\|Person=3\|PronType=Rel`, `Number=Plur\|POS=PROPN`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=PRON\|PronType=Dem`, `Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `POS=X`, `POS=INTJ`, `Case=Gen\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Int`, `Case=Acc\|POS=PRON\|Person=2\|PronType=Prs`, `POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|POS=PRON\|Person=2\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `4`, `5`, `10`, `12`, `14`, `16`, `20`, `24`, `25`, `28`, `30`, `32`, `34`, `38`, `40`, `42`, `45`, `47`, `48`, `51`, `52`, `54`, `55`, `57`, `59`, `62`, `64`, `66`, `68`, `70`, `72`, `76`, `78`, `81`, `83`, `84`, `86`, `89`, `91`, `92`, `96`, `99`, `101`, `104`, `106`, `109`, `114`, `115`, `117`, `118`, `120`, `121`, `123`, `126`, `129`, `131`, `133`, `137`, `139`, `141`, `143`, `145`, `146`, `148`, `151`, `154`, `158`, `160`, `163`, `165`, `98`, `168`, `169`, `171`, `174`, `177`, `181`, `183`, `185`, `187`, `191`, `194`, `196`, `199`, `202`, `206`, `209`, `211`, `212`, `214`, `217`, `61`, `219`, `221`, `224`, `226`, `227`, `229`, `231`, `235`, `236`, `238`, `240`, `242`, `245`, `247`, `251`, `253`, `257`, `260`, `262`, `264`, `263`, `266`, `267`, `271`, `273`, `274`, `275`, `278`, `280`, `281`, `282`, `284`, `286`, `291`, `293`, `296`, `298`, `299`, `301`, `303`, `307`, `308`, `310`, `312`, `314`, `316`, `318`, `320`, `322`, `324`, `325`, `328`, `330`, `332`, `333`, `336`, `337`, `339`, `342`, `344`, `345`, `349`, `352`, `353`, `354`, `355`, `357`, `360`, `362`, `363`, `365`, `368`, `372`, `373`, `375`, `377`, `379`, `383`, `385`, `387`, `389`, `390`, `392`, `394`, `396`, `398`, `402`, `404`, `407`, `409`, `9`, `411`, `412`, `414`, `417`, `418`, `420`, `422`, `423`, `425`, `429`, `431`, `432`, `435`, `437`, `438`, `440`, `442`, `444`, `448`, `450`, `451`, `454`, `456`, `457`, `459`, `461`, `463`, `464`, `466`, `468`, `469`, `472`, `473`, `476`, `477`, `478`, `480`, `484`, `487`, `489`, `491`, `493`, `496`, `497`, `500`, `502`, `505`, `506`, `508`, `510`, `511`, `512`, `515`, `518`, `523`, `525`, `528`, `531`, `532`, `534`, `306`, `535`, `537`, `539`, `542`, `544`, `548`, `552`, `555`, `556`, `557`, `558`, `559`, `560`, `564`, `566`, `538`, `567`, `569`, `570`, `572`, `573`, `575`, `577`, `579`, `580`, `582`, `583`, `584`, `587`, `588`, `591`, `593`, `595`, `597`, `599`, `601`, `602`, `605`, `607`, `609`, `611`, `614`, `616`, `617`, `618`, `620`, `621`, `622`, `623`, `625`, `626`, `629`, `632`, `634`, `636`, `638`, `641`, `642`, `644`, `647`, `648`, `650`, `651`, `654`, `655`, `657`, `659`, `660`, `663`, `664`, `665`, `666`, `668`, `671`, `673`, `675`, `676`, `677`, `678`, `33`, `681`, `683`, `686`, `688`, `691`, `692`, `694`, `697`, `698`, `699`, `700`, `701`, `702`, `703`, `706`, `709`, `712`, `713`, `714`, `717`, `720`, `721`, `682`, `723`, `725`, `728`, `730`, `733`, `735`, `738`, `740`, `741`, `743`, `744`, `745`, `748`, `750`, `751`, `753`, `756`, `759`, `760`, `762`, `763`, `764`, `767`, `771`, `773`, `774`, `776`, `234`, `777`, `779`, `364`, `781`, `382`, `783`, `784`, `785`, `786`, `788`, `791`, `793`, `794`, `796`, `799`, `693`, `801`, `804`, `805`, `807`, `808`, `811`, `813`, `814`, `815`, `816`, `818`, `820`, `821`, `824`, `825`, `826`, `827`, `828`, `829`, `830`, `833`, `834`, `836`, `839`, `841`, `845`, `847`, `848`, `849`, `850`, `851`, `856`, `858`, `859`, `860`, `861`, `862`, `864`, `866`, `869`, `871`, `873`, `875`, `876`, `877`, `878`, `881`, `882`, `883`, `884`, `885`, `887`, `889`, `890`, `670`, `891`, `894`, `896`, `899`, `900`, `902`, `904`, `908`, `910`, `913`, `915`, `916`, `918`, `921`, `923`, `924`, `926`, `927`, `931`, `934`, `936`, `938`, `940`, `942`, `943`, `946`, `949`, `950`, `951`, `952`, `953`, `954`, `955`, `958`, `959`, `961`, `962`, `963`, `69`, `964`, `967`, `969`, `972`, `973`, `975`, `977`, `978`, `980`, `982`, `983`, `984`, `986`, `988`, `989`, `991`, `992`, `993`, `995`, `996`, `290`, `998`, `999`, `1000`, `1001`, `1003`, `1005`, `1007`, `1008`, `1009`, `1011`, `1014`, `1015`, `1016`, `1017`, `1018`, `1019`, `1021`, `1022`, `1023`, `1024`, `1025`, `1027`, `1030`, `1031`, `1032`, `1033`, `1036`, `1038`, `1041`, `1045`, `1046`, `1048`, `1052`, `1053`, `1055`, `1056`, `1057`, `1059`, `1060`, `1062`, `1064`, `1068`, `1069`, `1070`, `1073`, `1075`, `1076`, `1077`, `1080`, `1083`, `1086`, `1087`, `1088`, `1091`, `1092`, `1095`, `1098`, `1099`, `1100`, `1101`, `1104`, `1108`, `1109`, `1111`, `1113`, `1114`, `1115`, `1116`, `1118`, `1120`, `1121`, `1122`, `1125`, `1126`, `1129`, `1132`, `1133`, `1136`, `1137`, `1138`, `1140`, `1141`, `1142`, `1143`, `1144`, `1146`, `1147`, `1148`, `1149`, `1150`, `71`, `1151`, `1154`, `1155`, `1156`, `1158`, `1160`, `1161`, `1162`, `1163`, `1164`, `1165`, `1166`, `1168`, `1171`, `1172`, `1174`, `1175`, `1176`, `1177`, `1178`, `1180`, `1183`, `1185`, `1189`, `1192`, `1194`, `1195`, `1196`, `1198`, `1199`, `1200`, `1201`, `1202`, `981`, `1203`, `1204`, `1208`, `1209`, `1210`, `1211`, `1212`, `1213`, `1215`, `1216`, `1218`, `1219`, `1221`, `1223`, `1224`, `1225`, `1227`, `1228`, `1230`, `1231`, `1232`, `1234`, `1235`, `1236`, `1237`, `1239`, `1241`, `1243`, `1245`, `1247`, `1248`, `1249`, `1250`, `1252`, `1253`, `1254`, `1255`, `1256`, `1257`, `1258`, `1259`, `1261`, `1263`, `1265`, `1266`, `1267`, `1270`, `1271`, `1272`, `1273`, `1275`, `1276`, `1277`, `1280`, `53`, `1281`, `1285`, `1286`, `1287`, `1288`, `1291`, `1292`, `1294`, `1296`, `1298`, `1300`, `1301`, `1303`, `1305`, `1306`, `1308`, `1309`, `1311`, `1312`, `1315`, `1318`, `1321`, `1322`, `1323`, `1326`, `1328`, `1330`, `1332`, `1334`, `1335`, `1337`, `1338`, `1340`, `1342`, `1343`, `1344`, `1346`, `1347`, `1348`, `1349`, `1350`, `1351`, `1353`, `1355`, `1356`, `1357`, `1359`, `1361`, `1362`, `1364`, `1365`, `1368`, `1369`, `1370`, `1371`, `1372`, `1376`, `1377`, `1380`, `1381`, `1382`, `1385`, `1386`, `1387`, `1388`, `1389`, `1390`, `1391`, `1392`, `1393`, `1394`, `1396`, `1397`, `1399`, `1398`, `1403`, `1405`, `1407`, `1411`, `1413`, `1415`, `1416`, `1417`, `1418`, `1421`, `1422`, `1424`, `1425`, `1426`, `1427`, `1428`, `1429`, `1431`, `1432`, `1434`, `803`, `1435`, `1436`, `1437`, `1439`, `1441`, `1445`, `1448`, `1449`, `1450`, `1451`, `1453`, `1454`, `1456`, `1459`, `1460`, `1461`, `1464`, `1466`, `1467`, `1470`, `1473`, `1477`, `1479`, `1481`, `1482`, `1485`, `1487`, `1488`, `1490`, `1495`, `1496`, `1497`, `1499`, `1500`, `1501`, `1503`, `1504`, `1505`, `1506`, `1508`, `1509`, `1512`, `1514`, `1515`, `1516`, `1517`, `1269`, `1518`, `1520`, `1521`, `1523`, `1524`, `1526`, `1528`, `1529`, `1531`, `1532`, `1534`, `1536`, `1537`, `1538`, `1539`, `1540`, `1541`, `294`, `1542`, `1544`, `1546`, `1548`, `1549`, `1551`, `1554`, `1555`, `1556`, `1557`, `1559`, `1560`, `1563`, `1565`, `1566`, `1567`, `1568`, `1569`, `1570`, `1571`, `1572`, `1575`, `1576`, `1577`, `1578`, `1580`, `1582`, `1583`, `1586`, `1589`, `1592`, `1593`, `1594`, `1595`, `1596`, `1597`, `1598`, `1600`, `1601`, `1602`, `1604`, `1605`, `1606`, `1607`, `1608`, `1609`, `1610`, `1611`, `1612`, `1614`, `1615`, `1617`, `1619`, `1620`, `1621`, `1622`, `1623`, `1626`, `1628`, `1629`, `1630`, `1631`, `1632`, `1634`, `1636`, `1638`, `1639`, `1641`, `1643`, `1644`, `1646`, `1647`, `1648`, `1649`, `1222`, `1650`, `1652`, `1653`, `1655`, `1656`, `1657`, `1659`, `1661`, `1662`, `1664`, `1667`, `1668`, `1670`, `1671`, `1673`, `1676`, `1677`, `1679`, `1680`, `1682`, `1685`, `1687`, `1689`, `1691`, `1692`, `1695`, `1696`, `1699`, `1701`, `1703`, `1705`, `1707`, `1708`, `1709`, `1710`, `1712`, `1714`, `1715`, `1718`, `1720`, `1721`, `1722`, `1724`, `1725`, `1726`, `1728`, `1729`, `1731`, `1732`, `1733`, `1734`, `1736`, `1739`, `1742`, `1743`, `1746`, `1748`, `1749`, `1751`, `1752`, `1753`, `1754`, `1395`, `1756`, `1759`, `1760`, `1761`, `1762`, `1764`, `1766`, `1768`, `1770`, `1772`, `1773`, `1774`, `1775`, `1776`, `1777`, `1779`, `1233`, `1781`, `1782`, `1783`, `1785`, `1786`, `1787`, `1789`, `1790`, `1791`, `1543`, `1792`, `1794`, `1795`, `1796`, `1798`, `1800`, `1801`, `1802`, `1804`, `1806`, `1807`, `1809`, `1812`, `1814`, `1817`, `1818`, `1738`, `1819`, `1822`, `1824`, `1825`, `1827`, `1828`, `0`, `1829`, `1830`, `1831`, `1833`, `1834`, `1835`, `1837`, `1839`, `1841`, `1844`, `1845`, `1846`, `1847`, `1848`, `1581`, `1849`, `1850`, `1852`, `1854`, `1855`, `1856`, `1857`, `1858`, `1859`, `1860`, `1862`, `1864`, `1866`, `1867`, `1868`, `1869`, `1788`, `1871`, `77`, `1872`, `1873`, `1875`, `1877`, `1878`, `1879`, `1883`, `674`, `1884`, `1886`, `1887`, `1888`, `1889`, `1891`, `1892`, `1894`, `1895`, `1898`, `1899`, `1901`, `1902`, `1903`, `1905`, `1908`, `1911`, `1913`, `1915`, `1916`, `1917`, `1920`, `1921`, `1922`, `1923`, `1924`, `1925`, `1926`, `1927`, `1929`, `1930`, `1931`, `1932`, `1934`, `1935`, `1938`, `1940`, `1941`, `1942`, `1944`, `1945`, `1946`, `1948`, `1949`, `1950`, `1952`, `1953`, `1954`, `1955`, `1956`, `1957`, `1958`, `1959`, `1960`, `1962`, `1963`, `1964`, `1966`, `1968`, `1970`, `1971`, `1972`, `1973`, `1976`, `1978`, `1979`, `1980`, `1981`, `1982`, `1984`, `1985`, `1986`, `1987`, `1988`, `1990`, `237`, `1992`, `1993`, `1994`, `1995`, `1996`, `1997`, `1998`, `1999`, `2000`, `2002`, `2005`, `2007`, `2009`, `2010`, `2011`, `2012`, `2013`, `2014`, `2015`, `2016`, `2017`, `2019`, `2020`, `2021`, `2023`, `2025`, `2026`, `2028`, `2029`, `2032`, `1511`, `2034`, `2036`, `2038`, 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`2212`, `2214`, `2216`, `2217`, `2218`, `2219`, `2220`, `2221`, `1730`, `2222`, `2223`, `501`, `2224`, `2225`, `2227`, `2229`, `2230`, `2232`, `2233`, `2234`, `2235`, `2237`, `2239`, `2241`, `2243`, `2244`, `2246`, `2247`, `2248`, `2249`, `2250`, `2251`, `2253`, `2254`, `2257`, `2259`, `2261`, `2264`, `2265`, `2266`, `2269`, `2270`, `2271`, `2273`, `2276`, `2278`, `2280`, `2281`, `2283`, `2285`, `2287`, `2288`, `2289`, `2290`, `2291`, `2292`, `2294`, `2297`, `2298`, `2300`, `2301`, `2302`, `2303`, `2304`, `2305`, `2307`, `2309`, `2312`, `1933`, `2313`, `2314`, `1423`, `2315`, `2316`, `2319`, `2321`, `2322`, `2323`, `2326`, `2328`, `2330`, `2331`, `2332`, `2334`, `63`, `2335`, `2336`, `2338`, `2339`, `2341`, `2343`, `2272`, `2344`, `2346`, `2347`, `2349`, `2350`, `2351`, `2353`, `2354`, `2355`, `2356`, `2357`, `2358`, `195` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 98.65 |
| `TOKEN_P` | 98.49 |
| `TOKEN_R` | 98.82 |
| `TOKEN_ACC` | 99.87 |
| `SENTS_F` | 90.84 |
| `SENTS_P` | 92.62 |
| `SENTS_R` | 89.14 |
| `TAG_ACC` | 95.60 |
| `POS_ACC` | 97.67 |
| `MORPH_ACC` | 96.79 |
| `DEP_UAS` | 94.66 |
| `DEP_LAS` | 92.28 |
| `LEMMA_ACC` | 96.46 |
|
{"language": ["nl"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/nl_udv25_dutchalpino_trf
|
[
"spacy",
"token-classification",
"nl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"nl"
] |
TAGS
#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Dutch-Alpino
### Label Scheme
View label scheme (1712 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1712 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1712 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1712 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Dutch-LassySmall
| Feature | Description |
| --- | --- |
| **Name** | `nl_udv25_dutchlassysmall_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1070 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ\|nom\|basis\|met-e\|mv-n`, `ADJ\|nom\|basis\|met-e\|zonder-n\|bijz`, `ADJ\|nom\|basis\|met-e\|zonder-n\|stan`, `ADJ\|nom\|basis\|zonder\|mv-n`, `ADJ\|nom\|basis\|zonder\|zonder-n`, `ADJ\|nom\|comp\|met-e\|mv-n`, `ADJ\|nom\|sup\|met-e\|mv-n`, `ADJ\|nom\|sup\|met-e\|zonder-n\|stan`, `ADJ\|nom\|sup\|zonder\|zonder-n`, `ADJ\|postnom\|basis\|zonder`, `ADJ\|prenom\|basis\|met-e\|bijz`, `ADJ\|prenom\|basis\|met-e\|stan`, `ADJ\|prenom\|basis\|zonder`, `ADJ\|prenom\|comp\|met-e\|stan`, `ADJ\|prenom\|comp\|zonder`, `ADJ\|prenom\|sup\|met-e\|stan`, `ADJ\|vrij\|basis\|zonder`, `ADJ\|vrij\|comp\|zonder`, `ADJ\|vrij\|sup\|zonder`, `BW`, `LET`, `LID\|bep\|gen\|evmo`, `LID\|bep\|gen\|rest3`, `LID\|bep\|stan\|evon`, `LID\|bep\|stan\|rest`, `LID\|onbep\|stan\|agr`, `N\|eigen\|ev\|basis\|gen`, `N\|eigen\|ev\|basis\|genus\|stan`, `N\|eigen\|ev\|basis\|onz\|stan`, `N\|eigen\|ev\|basis\|zijd\|stan`, `N\|eigen\|ev\|dim\|onz\|stan`, `N\|eigen\|mv\|basis`, `N\|soort\|ev\|basis\|dat`, `N\|soort\|ev\|basis\|gen`, `N\|soort\|ev\|basis\|genus\|stan`, `N\|soort\|ev\|basis\|onz\|stan`, `N\|soort\|ev\|basis\|zijd\|stan`, `N\|soort\|ev\|dim\|onz\|stan`, `N\|soort\|mv\|basis`, `N\|soort\|mv\|dim`, `SPEC\|afgebr`, `SPEC\|afk`, `SPEC\|deeleigen`, `SPEC\|enof`, `SPEC\|symb`, `SPEC\|vreemd`, `TSW`, `TW\|hoofd\|nom\|mv-n\|basis`, `TW\|hoofd\|nom\|zonder-n\|basis`, `TW\|hoofd\|nom\|zonder-n\|dim`, `TW\|hoofd\|prenom\|stan`, `TW\|hoofd\|vrij`, `TW\|rang\|nom\|zonder-n`, `TW\|rang\|prenom\|stan`, `VG\|neven`, `VG\|onder`, `VNW\|aanw\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|aanw\|adv-pron\|stan\|red\|3\|getal`, `VNW\|aanw\|det\|stan\|nom\|met-e\|mv-n`, `VNW\|aanw\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|aanw\|det\|stan\|prenom\|met-e\|rest`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|agr`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|evon`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|rest`, `VNW\|aanw\|pron\|gen\|vol\|3m\|ev`, `VNW\|aanw\|pron\|stan\|vol\|3o\|ev`, `VNW\|aanw\|pron\|stan\|vol\|3\|getal`, `VNW\|betr\|det\|stan\|nom\|zonder\|zonder-n`, `VNW\|betr\|pron\|stan\|vol\|3\|ev`, `VNW\|betr\|pron\|stan\|vol\|persoon\|getal`, `VNW\|bez\|det\|stan\|red\|3\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|1\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|1\|mv\|prenom\|met-e\|rest`, `VNW\|bez\|det\|stan\|vol\|1\|mv\|prenom\|zonder\|evon`, `VNW\|bez\|det\|stan\|vol\|2\|getal\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|3m\|ev\|prenom\|met-e\|rest`, `VNW\|bez\|det\|stan\|vol\|3p\|mv\|prenom\|met-e\|rest`, `VNW\|bez\|det\|stan\|vol\|3v\|ev\|prenom\|met-e\|rest`, `VNW\|bez\|det\|stan\|vol\|3\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|3\|mv\|prenom\|zonder\|agr`, `VNW\|onbep\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|onbep\|det\|stan\|nom\|met-e\|mv-n`, `VNW\|onbep\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|agr`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|evz`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|mv`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|rest`, `VNW\|onbep\|det\|stan\|prenom\|zonder\|agr`, `VNW\|onbep\|det\|stan\|prenom\|zonder\|evon`, `VNW\|onbep\|det\|stan\|vrij\|zonder`, `VNW\|onbep\|grad\|gen\|nom\|met-e\|mv-n\|basis`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|mv-n\|basis`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|zonder-n\|basis`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|zonder-n\|sup`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|agr\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|agr\|sup`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|mv\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|zonder\|agr\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|zonder\|agr\|comp`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|basis`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|comp`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|sup`, `VNW\|onbep\|pron\|stan\|vol\|3o\|ev`, `VNW\|onbep\|pron\|stan\|vol\|3p\|ev`, `VNW\|pers\|pron\|nomin\|nadr\|3v\|ev\|fem`, `VNW\|pers\|pron\|nomin\|red\|1\|mv`, `VNW\|pers\|pron\|nomin\|red\|2v\|ev`, `VNW\|pers\|pron\|nomin\|red\|3p\|ev\|masc`, `VNW\|pers\|pron\|nomin\|vol\|1\|ev`, `VNW\|pers\|pron\|nomin\|vol\|1\|mv`, `VNW\|pers\|pron\|nomin\|vol\|2b\|getal`, `VNW\|pers\|pron\|nomin\|vol\|3p\|mv`, `VNW\|pers\|pron\|nomin\|vol\|3v\|ev\|fem`, `VNW\|pers\|pron\|nomin\|vol\|3\|ev\|masc`, `VNW\|pers\|pron\|obl\|nadr\|3m\|ev\|masc`, `VNW\|pers\|pron\|obl\|vol\|3p\|mv`, `VNW\|pers\|pron\|obl\|vol\|3\|ev\|masc`, `VNW\|pers\|pron\|obl\|vol\|3\|getal\|fem`, `VNW\|pers\|pron\|stan\|red\|3\|ev\|fem`, `VNW\|pers\|pron\|stan\|red\|3\|ev\|onz`, `VNW\|pers\|pron\|stan\|red\|3\|mv`, `VNW\|pr\|pron\|obl\|red\|1\|ev`, `VNW\|pr\|pron\|obl\|red\|2v\|getal`, `VNW\|pr\|pron\|obl\|vol\|1\|ev`, `VNW\|pr\|pron\|obl\|vol\|1\|mv`, `VNW\|recip\|pron\|obl\|vol\|persoon\|mv`, `VNW\|refl\|pron\|obl\|nadr\|3\|getal`, `VNW\|refl\|pron\|obl\|red\|3\|getal`, `VNW\|vb\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|vb\|pron\|stan\|vol\|3o\|ev`, `VNW\|vb\|pron\|stan\|vol\|3p\|getal`, `VZ\|fin`, `VZ\|init`, `VZ\|versm`, `WW\|inf\|nom\|zonder\|zonder-n`, `WW\|inf\|vrij\|zonder`, `WW\|od\|nom\|met-e\|mv-n`, `WW\|od\|nom\|met-e\|zonder-n`, `WW\|od\|prenom\|met-e`, `WW\|od\|prenom\|zonder`, `WW\|od\|vrij\|zonder`, `WW\|pv\|conj\|ev`, `WW\|pv\|tgw\|ev`, `WW\|pv\|tgw\|met-t`, `WW\|pv\|tgw\|mv`, `WW\|pv\|verl\|ev`, `WW\|pv\|verl\|mv`, `WW\|vd\|nom\|met-e\|mv-n`, `WW\|vd\|nom\|met-e\|zonder-n`, `WW\|vd\|prenom\|met-e`, `WW\|vd\|prenom\|zonder`, `WW\|vd\|vrij\|zonder` |
| **`morphologizer`** | `Definite=Def\|POS=DET`, `Degree=Pos\|POS=ADJ`, `POS=CCONJ`, `Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `POS=DET`, `Degree=Sup\|POS=ADJ`, `Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Gender=Com\|Number=Sing\|POS=PROPN`, `POS=SYM`, `POS=NUM`, `POS=ADP`, `Definite=Ind\|POS=DET`, `Gender=Com\|Number=Sing\|POS=NOUN`, `Degree=Cmp\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=PROPN`, `POS=ADV`, `Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=PROPN`, `Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `POS=PRON\|PronType=Rel`, `Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `POS=PRON\|PronType=Ind`, `POS=VERB\|VerbForm=Part`, `POS=ADJ`, `POS=X`, `Gender=Com,Neut\|Number=Sing\|POS=PROPN`, `Foreign=Yes\|POS=X`, `POS=PRON\|Person=3\|PronType=Prs`, `POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PROPN`, `Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Rel`, `POS=AUX\|VerbForm=Inf`, `POS=SCONJ`, `Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|POS=X`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PRON\|Person=3\|PronType=Dem`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `POS=PRON\|PronType=Dem`, `POS=PRON\|Person=3\|PronType=Int`, `Gender=Com,Neut\|Number=Sing\|POS=NOUN`, `POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|POS=NOUN`, `Case=Acc\|POS=PRON\|PronType=Rcp`, `POS=AUX\|VerbForm=Part`, `Number=Sing\|POS=PROPN`, `Case=Nom\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs`, `POS=INTJ`, `POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `3`, `4`, `6`, `8`, `10`, `13`, `15`, `17`, `19`, `21`, `22`, `25`, `27`, `29`, `30`, `32`, `36`, `37`, `39`, `41`, `42`, `45`, `47`, `49`, `51`, `53`, `55`, `58`, `59`, `61`, `63`, `65`, `66`, `70`, `72`, `74`, `76`, `78`, `79`, `80`, `82`, `84`, `86`, `89`, `92`, `94`, `96`, `97`, `99`, `101`, `104`, `107`, `109`, `110`, `112`, `114`, `116`, `117`, `118`, `119`, `120`, `121`, `124`, `126`, `127`, `130`, `133`, `134`, `135`, `137`, `139`, `142`, `143`, `145`, `148`, `152`, `154`, `156`, `159`, `160`, `163`, `165`, `167`, `168`, `172`, `175`, `176`, `178`, `182`, `184`, `187`, `189`, `190`, `192`, `194`, `195`, `197`, `199`, `200`, `203`, `205`, `207`, `208`, `209`, `212`, `214`, `215`, `217`, `219`, `220`, `221`, `224`, `227`, `228`, `230`, `232`, `233`, `237`, `238`, `240`, `241`, `242`, `244`, `245`, `248`, `249`, `250`, `251`, `252`, `255`, `258`, `259`, `260`, `262`, `266`, `268`, `270`, `272`, `275`, `278`, `280`, `281`, `282`, `283`, `285`, `287`, `290`, `291`, `293`, `297`, `299`, `300`, `301`, `302`, `306`, `307`, `309`, `310`, `311`, `313`, `314`, `316`, `318`, `319`, `320`, `324`, `329`, `332`, `333`, `335`, `337`, `339`, `343`, `346`, `347`, `348`, `352`, `353`, `357`, `358`, `359`, `360`, `362`, `363`, `366`, `369`, `372`, `374`, `377`, `378`, `379`, `381`, `382`, `386`, `387`, `391`, `395`, `397`, `399`, `400`, `401`, `403`, `406`, `407`, `408`, `409`, `410`, `411`, `412`, `414`, `415`, `417`, `419`, `421`, `423`, `426`, `427`, `428`, `431`, `433`, `435`, `437`, `439`, `441`, `444`, `446`, `448`, `451`, `453`, `455`, `457`, `458`, `460`, `462`, `463`, `465`, `467`, `469`, `470`, `472`, `474`, `475`, `478`, `482`, `483`, `485`, `489`, `491`, `492`, `493`, `495`, `499`, `500`, `502`, `506`, `508`, `511`, `514`, `518`, `520`, `522`, `525`, `527`, `528`, `532`, `534`, `535`, `538`, `540`, `541`, `544`, `546`, `547`, `548`, `551`, `552`, `556`, `558`, `559`, `560`, `563`, `565`, `567`, `569`, `570`, `573`, `577`, `579`, `581`, `584`, `587`, `589`, `591`, `595`, `597`, `599`, `600`, `601`, `602`, `606`, `608`, `610`, `612`, `614`, `615`, `616`, `618`, `619`, `620`, `621`, `622`, `626`, `628`, `629`, `631`, `632`, `634`, `635`, `636`, `637`, `639`, `641`, `644`, `649`, `653`, `654`, `656`, `657`, `658`, `661`, `663`, `664`, `665`, `666`, `667`, `668`, `669`, `670`, `674`, `676`, `678`, `679`, `682`, `685`, `687`, `689`, `692`, `694`, `696`, `699`, `702`, `703`, `704`, `705`, `706`, `708`, `709`, `711`, `712`, `714`, `715`, `717`, `718`, `719`, `722`, `725`, `729`, `730`, `733`, `736`, `738`, `739`, `743`, `745`, `746`, `749`, `750`, `328`, `752`, `754`, `755`, `757`, `760`, `761`, `762`, `764`, `767`, `769`, `770`, `773`, `777`, `778`, `781`, `783`, `784`, `785`, `786`, `789`, `790`, `793`, `794`, `795`, `798`, `800`, `162`, `803`, `806`, `809`, `812`, `813`, `815`, `817`, `818`, `819`, `821`, `823`, `824`, `825`, `827`, `830`, `832`, `834`, `836`, `838`, `648`, `839`, `841`, `843`, `844`, `846`, `848`, `849`, `851`, `852`, `853`, `854`, `855`, `857`, `859`, `860`, `861`, `863`, `865`, `867`, `869`, `872`, `873`, `875`, `877`, `879`, `881`, `883`, `885`, `886`, `887`, `888`, `890`, `893`, `894`, `896`, `899`, `901`, `902`, `904`, `906`, `908`, `911`, `913`, `915`, `918`, `919`, `920`, `921`, `926`, `928`, `930`, `931`, `932`, `933`, `934`, `396`, `935`, `936`, `938`, `939`, `940`, `942`, `945`, `946`, `947`, `948`, `950`, `951`, `954`, `956`, `957`, `960`, `962`, `964`, `967`, `969`, `970`, `971`, `975`, `976`, `977`, `978`, `979`, `980`, `981`, `982`, `983`, `984`, `985`, `988`, `990`, `991`, `995`, `997`, `998`, `840`, `999`, `1000`, `1002`, `1003`, `1004`, `1006`, `1008`, `1009`, `1013`, `1017`, `862`, `1019`, `1020`, `1021`, `1024`, `1025`, `1027`, `1028`, `1029`, `1031`, `1033`, `1036`, `1039`, `1040`, `1041`, `1043`, `1044`, `1047`, `1048`, `1052`, `1055`, `1056`, `1057`, `1061`, `1062`, `1063`, `1066`, `1069`, `507`, `1071`, `1072`, `1074`, `1075`, `1076`, `1078`, `1079`, `1080`, `1081`, `1082`, `1085`, `1086`, `1087`, `1089`, `1090`, `1091`, `1093`, `1094`, `1097`, `1100`, `1102`, `1103`, `1104`, `1106`, `1107`, `1108`, `1109`, `1111`, `1113`, `1115`, `1116`, `1119`, `1121`, `1122`, `1123`, `1125`, `1126`, `1127`, `1128`, `1129`, `1131`, `1132`, `1135`, `1138`, `1140`, `1141`, `1143`, `1144`, `1145`, `1147`, `1150`, `1151`, `1152`, `1154`, `1155`, `1158`, `1159`, `1160`, `1161`, `1162`, `1164`, `1166`, `1167`, `1169`, `1170`, `1172`, `1175`, `1177`, `510`, `1178`, `1181`, `1182`, `1183`, `1185`, `1187`, `1189`, `1190`, `1191`, `1192`, `1194`, `1197`, `1201`, `1202`, `1203`, `1206`, `1208`, `1209`, `1210`, `1213`, `1217`, `1218`, `1220`, `1221`, `1223`, `1225`, `1227`, `1229`, `1231`, `1233`, `1236`, `1238`, `1240`, `1241`, `1244`, `1245`, `1247`, `1249`, `1250`, `1252`, `1253`, `1254`, `1255`, `1257`, `1259`, `1261`, `1262`, `1264`, `1266`, `1268`, `1271`, `1273`, `1274`, `1276`, `1278`, `1279`, `48`, `1280`, `1281`, `1283`, `1248`, `1284`, `1286`, `1287`, `1289`, `1290`, `1292`, `884`, `1293`, `1295`, `1296`, `1298`, `1299`, `1300`, `1302`, `1303`, `1304`, `1305`, `1306`, `1307`, `1309`, `1311`, `1313`, `1316`, `1317`, `1318`, `1319`, `1321`, `206`, `1322`, `1323`, `1328`, `1330`, `1331`, `1332`, `1334`, `1336`, `1338`, `1341`, `1342`, `1343`, `1344`, `1345`, `1346`, `1347`, `1348`, `1350`, `1352`, `1354`, `1356`, `1357`, `1358`, `1359`, `1360`, `1361`, `864`, `1363`, `1364`, `1366`, `1367`, `1368`, `1370`, `1371`, `1372`, `1374`, `1376`, `1377`, `1378`, `1379`, `1381`, `1382`, `1383`, `1384`, `1386`, `1387`, `1389`, `1390`, `1391`, `1393`, `1396`, `1397`, `1398`, `1399`, `1403`, `1404`, `1406`, `1407`, `1410`, `1412`, `1415`, `1416`, `1419`, `1421`, `1422`, `1423`, `1424`, `1425`, `1427`, `1429`, `1432`, `1433`, `1437`, `1440`, `1442`, `1447`, `1450`, `1452`, `1454`, `1457`, `1458`, `1459`, `1460`, `1462`, `1463`, `1464`, `1466`, `1468`, `1469`, `1471`, `1473`, `1475`, `1476`, `1478`, `1479`, `1480`, `1481`, `1482`, `1483`, `1484` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.91 |
| `TOKEN_P` | 99.88 |
| `TOKEN_R` | 99.94 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 91.84 |
| `SENTS_P` | 90.52 |
| `SENTS_R` | 93.20 |
| `TAG_ACC` | 95.93 |
| `POS_ACC` | 96.37 |
| `MORPH_ACC` | 97.73 |
| `DEP_UAS` | 90.23 |
| `DEP_LAS` | 86.21 |
| `LEMMA_ACC` | 96.71 |
|
{"language": ["nl"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/nl_udv25_dutchlassysmall_trf
|
[
"spacy",
"token-classification",
"nl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"nl"
] |
TAGS
#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Dutch-LassySmall
### Label Scheme
View label scheme (1070 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1070 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1070 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1070 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Polish-LFG
| Feature | Description |
| --- | --- |
| **Name** | `pl_udv25_polishlfg_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `GPL 3.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (4947 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `adj:pl:acc:f:com`, `adj:pl:acc:f:pos`, `adj:pl:acc:f:sup`, `adj:pl:acc:m1:com`, `adj:pl:acc:m1:pos`, `adj:pl:acc:m1:sup`, `adj:pl:acc:m2:pos`, `adj:pl:acc:m3:com`, `adj:pl:acc:m3:pos`, `adj:pl:acc:m3:sup`, `adj:pl:acc:n:com`, `adj:pl:acc:n:pos`, `adj:pl:acc:n:sup`, `adj:pl:dat:f:pos`, `adj:pl:dat:m1:com`, `adj:pl:dat:m1:pos`, `adj:pl:dat:m3:pos`, `adj:pl:dat:n:pos`, `adj:pl:gen:f:com`, `adj:pl:gen:f:pos`, `adj:pl:gen:f:sup`, `adj:pl:gen:m1:com`, `adj:pl:gen:m1:pos`, `adj:pl:gen:m1:sup`, `adj:pl:gen:m2:pos`, `adj:pl:gen:m2:sup`, `adj:pl:gen:m3:com`, `adj:pl:gen:m3:pos`, `adj:pl:gen:m3:sup`, `adj:pl:gen:n:com`, `adj:pl:gen:n:pos`, `adj:pl:inst:f:pos`, `adj:pl:inst:m1:pos`, `adj:pl:inst:m2:pos`, `adj:pl:inst:m3:pos`, `adj:pl:inst:n:pos`, `adj:pl:loc:f:pos`, `adj:pl:loc:f:sup`, `adj:pl:loc:m1:com`, `adj:pl:loc:m1:pos`, `adj:pl:loc:m3:pos`, `adj:pl:loc:m3:sup`, `adj:pl:loc:n:com`, `adj:pl:loc:n:pos`, `adj:pl:nom:f:com`, `adj:pl:nom:f:pos`, `adj:pl:nom:f:sup`, 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`ppron3:sg:gen:f:ter:akc:praep`, `ppron3:sg:gen:m1:ter:akc:npraep`, `ppron3:sg:gen:m1:ter:akc:praep`, `ppron3:sg:gen:m1:ter:nakc:npraep`, `ppron3:sg:gen:m2:ter:akc:npraep`, `ppron3:sg:gen:m3:ter:akc:npraep`, `ppron3:sg:gen:m3:ter:akc:praep`, `ppron3:sg:gen:m3:ter:nakc:npraep`, `ppron3:sg:gen:n:ter:akc:npraep`, `ppron3:sg:gen:n:ter:akc:praep`, `ppron3:sg:gen:n:ter:nakc:npraep`, `ppron3:sg:inst:f:ter:akc:npraep`, `ppron3:sg:inst:f:ter:akc:praep`, `ppron3:sg:inst:m1:ter:akc:npraep`, `ppron3:sg:inst:m1:ter:akc:praep`, `ppron3:sg:inst:m2:ter:akc:npraep`, `ppron3:sg:inst:m2:ter:akc:praep`, `ppron3:sg:inst:m3:ter:akc:npraep`, `ppron3:sg:inst:m3:ter:akc:praep`, `ppron3:sg:inst:n:ter:akc:npraep`, `ppron3:sg:inst:n:ter:akc:praep`, `ppron3:sg:loc:f:ter:akc:praep`, `ppron3:sg:loc:m1:ter:akc:praep`, `ppron3:sg:loc:m2:ter:akc:praep`, `ppron3:sg:loc:m3:ter:akc:praep`, `ppron3:sg:loc:n:ter:akc:praep`, `ppron3:sg:nom:f:ter:akc:npraep`, `ppron3:sg:nom:m1:ter:akc:npraep`, `ppron3:sg:nom:m2:ter:akc:npraep`, `ppron3:sg:nom:m3:ter:akc:npraep`, `ppron3:sg:nom:n:ter:akc:npraep`, `praet:pl:f:imperf`, `praet:pl:f:perf`, `praet:pl:m1:imperf`, `praet:pl:m1:perf`, `praet:pl:m2:imperf`, `praet:pl:m2:perf`, `praet:pl:m3:imperf`, `praet:pl:m3:perf`, `praet:pl:n:imperf`, `praet:pl:n:perf`, `praet:sg:f:imperf`, `praet:sg:f:perf`, `praet:sg:m1:imperf`, `praet:sg:m1:imperf:agl`, `praet:sg:m1:imperf:nagl`, `praet:sg:m1:perf`, `praet:sg:m1:perf:agl`, `praet:sg:m1:perf:nagl`, `praet:sg:m2:imperf`, `praet:sg:m2:imperf:nagl`, `praet:sg:m2:perf`, `praet:sg:m2:perf:nagl`, `praet:sg:m3:imperf`, `praet:sg:m3:imperf:nagl`, `praet:sg:m3:perf`, `praet:sg:m3:perf:nagl`, `praet:sg:n:imperf`, `praet:sg:n:perf`, `pred`, `prep:acc`, `prep:acc:nwok`, `prep:acc:wok`, `prep:dat`, `prep:gen`, `prep:gen:nwok`, `prep:gen:wok`, `prep:inst`, `prep:inst:nwok`, `prep:inst:wok`, `prep:loc`, `prep:loc:nwok`, `prep:loc:wok`, `prep:nom`, `qub`, `qub:nwok`, `qub:wok`, `siebie:acc`, `siebie:dat`, `siebie:gen`, `siebie:inst`, `siebie:loc`, `subst:pl:acc:f`, `subst:pl:acc:m1`, `subst:pl:acc:m2`, `subst:pl:acc:m3`, `subst:pl:acc:n`, `subst:pl:dat:f`, `subst:pl:dat:m1`, `subst:pl:dat:m3`, `subst:pl:dat:n`, `subst:pl:gen:f`, `subst:pl:gen:m1`, `subst:pl:gen:m2`, `subst:pl:gen:m3`, `subst:pl:gen:n`, `subst:pl:inst:f`, `subst:pl:inst:m1`, `subst:pl:inst:m2`, `subst:pl:inst:m3`, `subst:pl:inst:n`, `subst:pl:loc:f`, `subst:pl:loc:m1`, `subst:pl:loc:m2`, `subst:pl:loc:m3`, `subst:pl:loc:n`, `subst:pl:nom:f`, `subst:pl:nom:m1`, `subst:pl:nom:m2`, `subst:pl:nom:m3`, `subst:pl:nom:n`, `subst:pl:voc:m1`, `subst:sg:acc:f`, `subst:sg:acc:m1`, `subst:sg:acc:m2`, `subst:sg:acc:m3`, `subst:sg:acc:n`, `subst:sg:dat:f`, `subst:sg:dat:m1`, `subst:sg:dat:m2`, `subst:sg:dat:m3`, `subst:sg:dat:n`, `subst:sg:gen:f`, `subst:sg:gen:m1`, `subst:sg:gen:m2`, `subst:sg:gen:m3`, `subst:sg:gen:n`, `subst:sg:inst:f`, `subst:sg:inst:m1`, `subst:sg:inst:m2`, `subst:sg:inst:m3`, `subst:sg:inst:n`, `subst:sg:loc:f`, `subst:sg:loc:m1`, `subst:sg:loc:m2`, `subst:sg:loc:m3`, `subst:sg:loc:n`, `subst:sg:nom:f`, `subst:sg:nom:m1`, `subst:sg:nom:m2`, `subst:sg:nom:m3`, `subst:sg:nom:n`, `subst:sg:voc:f`, `subst:sg:voc:m1`, `subst:sg:voc:m3`, `winien:pl:f:imperf`, `winien:pl:m1:imperf`, `winien:pl:m3:imperf`, `winien:sg:f:imperf`, `winien:sg:m1:imperf`, `winien:sg:m3:imperf`, `winien:sg:n:imperf` |
| **`morphologizer`** | `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc3`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PUNCT\|PunctType=Peri`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc3`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctType=Comm`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc1`, `Agglutination=Nagl\|Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `AdpType=Prep\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|POS=ADP\|Variant=Short`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc3`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc1`, `POS=SCONJ`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc1`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc3`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc3`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc3`, `AdpType=Post\|POS=ADP`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc1`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc1`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `POS=PUNCT`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PUNCT\|PunctType=Dash`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc3`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PUNCT\|PunctType=Excl`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc3`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `POS=CCONJ`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=2\|Variant=Short`, `Degree=Pos\|POS=ADV`, `POS=PUNCT\|PunctType=Qest`, `Mood=Cnd\|POS=AUX`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc3`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|VerbType=Quasi`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Degree=Pos\|POS=ADV\|PronType=Dem`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Number=Plur\|POS=AUX\|Person=1\|Variant=Short`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc3`, `Aspect=Imp\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc1`, `Degree=Sup\|POS=ADV`, `POS=ADV\|PronType=Dem`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|VerbType=Quasi`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=PART`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `POS=ADV\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `POS=PART\|Polarity=Neg`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `POS=PART\|PartType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc1`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc3`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc2`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc1`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc1`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Short`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|SubGender=Masc1`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=1\|Variant=Short`, `AdpType=Prep\|POS=ADP\|Variant=Long`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=1\|Variant=Long`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc1`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc2`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `POS=ADV\|PronType=Neg`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc1`, `POS=ADJ\|PrepCase=Pre`, `Degree=Pos\|POS=ADV\|PronType=Int`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc1`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int\|SubGender=Masc2`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=2\|Variant=Long`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int\|SubGender=Masc1`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Variant=Short`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Nom\|Emphatic=Yes\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `Hyph=Yes\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Long`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Short`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind\|SubGender=Masc1`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg\|SubGender=Masc1`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Quot`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg\|SubGender=Masc1`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|POS=AUX`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc2`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc2`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc3`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc2`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|POS=VERB\|Person=0\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc3`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Polite=Depr\|SubGender=Masc2`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Degree=Pos\|POS=ADV\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `POS=ADV\|PronType=Tot`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Variant=Long`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc1`, `Agglutination=Nagl\|Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc2`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc2`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Aspect=Imp\|Number=Plur\|POS=AUX\|Person=2\|Variant=Short`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc1`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc2`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Variant=Short`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc2`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc3`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind\|SubGender=Masc1`, `Case=Acc\|Emphatic=Yes\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc2`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind\|SubGender=Masc1`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc1`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Agglutination=Agl\|Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc3`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc3`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc2`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Agglutination=Nagl\|Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Short`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Agglutination=Nagl\|Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Long`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Long`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Quot`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc3`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv\|Voice=Act`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc3`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc3`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc1`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc2`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc2`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc2`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `POS=SCONJ\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot\|SubGender=Masc1`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc2`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc2`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc2`, `Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc1`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc2`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc3`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc1`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `POS=PUNCT\|PunctType=Semi`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg\|SubGender=Masc3`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc2`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc2`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Loc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc2`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Ins\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=ADJ\|Variant=Short`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc1`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc1`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int\|SubGender=Masc3`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc3`, `Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int\|SubGender=Masc1`, `Aspect=Perf\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc2\|Variant=Short`, `Case=Gen\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `Case=Ins\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc3`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc2`, `Case=Ins\|Emphatic=Yes\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc2`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Variant=Long`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc3`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|SubGender=Masc1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc3`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Variant=Long`, `Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc1`, `Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc2`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc2`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc2`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int\|SubGender=Masc1`, `Emphatic=Yes\|POS=PART\|PartType=Int`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Aspect=Perf\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Nom\|Emphatic=Yes\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc1`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg\|SubGender=Masc1`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg\|SubGender=Masc1`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int\|SubGender=Masc1`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Nom\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc3`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc3`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc3`, `Case=Ins\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN\|SubGender=Masc2`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc1`, `Aspect=Imp\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Pos\|VerbForm=Vnoun`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Aspect=Imp\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|SubGender=Masc1`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc2`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Ind\|SubGender=Masc3`, `Emphatic=Yes\|POS=ADV\|PronType=Int`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Conv\|Voice=Act`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc2`, `Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc2`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|SubGender=Masc3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Loc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|NumType=Frac\|Number=Plur\|POS=NUM\|SubGender=Masc3`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int\|SubGender=Masc1`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot\|SubGender=Masc1`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg\|SubGender=Masc3`, `Agglutination=Nagl\|Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Nom\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc2\|Variant=Short`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int\|SubGender=Masc3`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int\|SubGender=Masc3`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Emphatic=Yes\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc2`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc2`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc3`, `Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Variant=Short`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `POS=PART\|Variant=Short`, `Case=Acc\|Gender=Fem\|NumType=Frac\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc1`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `Aspect=Perf\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Agglutination=Agl\|Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Case=Ins\|Emphatic=Yes\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int\|SubGender=Masc1`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Emphatic=Yes\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|SubGender=Masc2`, `Case=Ins\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `Aspect=Imp\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc3`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|SubGender=Masc3`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Neg\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Short`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Ins\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc2`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|SubGender=Masc3`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Aspect=Perf\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc3`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Neg`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc1`, `Case=Ins\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc1`, `Case=Ins\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc2`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc2`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc1`, `Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Ind\|SubGender=Masc3`, `Case=Ins\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Short`, `Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Short`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc3`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc1`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc2`, `Case=Ins\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int`, `Case=Acc\|Emphatic=Yes\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc1`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc3`, `Aspect=Imp\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc2`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc2`, `Case=Gen\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc3`, `Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc2`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg\|SubGender=Masc3`, `Agglutination=Nagl\|Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg\|SubGender=Masc1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc1`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|SubGender=Masc1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PART\|Variant=Long`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc2`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc2\|Variant=Short`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc2`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc2`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc3`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc3`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Case=Loc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc3\|Variant=Short`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc1`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc3`, `Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Polite=Depr\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind\|SubGender=Masc1`, `Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|SubGender=Masc3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|SubGender=Masc3`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc1`, `Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|SubGender=Masc2\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc2`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|SubGender=Masc2\|Variant=Long`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Neg\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc2`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc1\|Variant=Long`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc1`, `Case=Loc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Polarity=Neg\|VerbForm=Vnoun`, `Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Short`, `Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Emphatic=Yes\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int\|SubGender=Masc3`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Variant=Long`, `Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|SubGender=Masc1`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc1`, `Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs\|SubGender=Masc3`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|SubGender=Masc3`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc2`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|SubGender=Masc1`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem\|SubGender=Masc2`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN\|SubGender=Masc2`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel\|SubGender=Masc1`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc2\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Neg\|SubGender=Masc3\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|SubGender=Masc3\|Variant=Long`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|SubGender=Masc1`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc3\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Variant=Long`, `Case=Ins\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Emphatic=Yes\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|SubGender=Masc2`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|SubGender=Masc1\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int\|SubGender=Masc2`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|SubGender=Masc1`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel\|SubGender=Masc1`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:aglt`, `aux:mood`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `ccomp:obj`, `conj`, `cop`, `cop:locat`, `csubj`, `dep`, `det`, `discourse`, `expl:impers`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `punct`, `vocative`, `xcomp`, `xcomp:obj` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `3`, `5`, `6`, `7`, `9`, `11`, `13`, `15`, `17`, `19`, `21`, `23`, `25`, `26`, `30`, `32`, `34`, `38`, `39`, `41`, `43`, `46`, `48`, `51`, `53`, `55`, `57`, `60`, `62`, `63`, `66`, `68`, `70`, `72`, `75`, `77`, `79`, `81`, `82`, `84`, `86`, `88`, `91`, `93`, `94`, `96`, `98`, `99`, `101`, `104`, `107`, `109`, `111`, `113`, `117`, `119`, `122`, `124`, `126`, `127`, `128`, `130`, `131`, `133`, `134`, `136`, `137`, `139`, `141`, `143`, `144`, `146`, `148`, `150`, `152`, `154`, `156`, `158`, `160`, `162`, `165`, `167`, `169`, `171`, `172`, `173`, `174`, `175`, `177`, `179`, `180`, `183`, `185`, `187`, `189`, `191`, `193`, `195`, `197`, `198`, `200`, `204`, `206`, `207`, `209`, `210`, `211`, `213`, `215`, `217`, `219`, `221`, `223`, `226`, `228`, `230`, `232`, `235`, `236`, `238`, `240`, `242`, `243`, `245`, `246`, `248`, `250`, `252`, `254`, `256`, `259`, `261`, `263`, `264`, `266`, `268`, `270`, `272`, `273`, `275`, `277`, `279`, `280`, 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`4777`, `4778`, `4779`, `4780`, `4781`, `4782`, `4783`, `4784`, `4785`, `4786`, `4787`, `4788`, `4789`, `4790`, `4791`, `4793`, `4795`, `4796`, `4798`, `4799`, `4801`, `4803`, `4804`, `4805`, `4806`, `4807`, `4808`, `4809`, `4811`, `4813`, `4815`, `4816`, `4817`, `4818`, `4819`, `4820`, `4821`, `4822`, `4823`, `4824`, `4825`, `4826`, `4827`, `4828` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.89 |
| `TOKEN_P` | 99.89 |
| `TOKEN_R` | 99.90 |
| `TOKEN_ACC` | 99.98 |
| `SENTS_F` | 99.89 |
| `SENTS_P` | 99.89 |
| `SENTS_R` | 99.89 |
| `TAG_ACC` | 95.62 |
| `POS_ACC` | 99.05 |
| `MORPH_ACC` | 95.42 |
| `DEP_UAS` | 97.39 |
| `DEP_LAS` | 95.55 |
| `LEMMA_ACC` | 95.92 |
|
{"language": ["pl"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/pl_udv25_polishlfg_trf
|
[
"spacy",
"token-classification",
"pl",
"license:gpl-3.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Polish-LFG
### Label Scheme
View label scheme (4947 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (4947 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4947 labels for 6 components)",
"### Accuracy"
] |
[
29,
18,
5
] |
[
"passage: TAGS\n#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (4947 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Portuguese-Bosque
| Feature | Description |
| --- | --- |
| **Name** | `pt_udv25_portuguesebosque_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (2079 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADP_ADV`, `ADP_DET`, `ADP_NUM`, `ADP_PRON`, `ADP_PROPN`, `ADV`, `ADV_PRON`, `ADV_PROPN`, `AUX`, `AUX_PRON`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PART_NOUN`, `PRON`, `PRON_PRON`, `PROPN`, `PROPN_DET`, `PROPN_PROPN`, `PUNCT`, `SCONJ`, `SCONJ_DET`, `SCONJ_PRON`, `SYM`, `VERB`, `VERB_PRON`, `X` |
| **`morphologizer`** | `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=PUNCT`, `NumType=Card\|POS=NUM`, `POS=ADV`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=ADP`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Unsp\|Number=Sing\|POS=PROPN`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=X`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Unsp\|Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=VERB\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Number=Plur\|POS=AUX\|Person=3\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Art`, `POS=VERB\|VerbForm=Part`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `NumType=Ord\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Unsp\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `Gender=Unsp\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Unsp\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `POS=AUX\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Unsp\|Number=Sing\|POS=PRON\|PronType=Rel`, `Number=Sing\|POS=DET\|PronType=Art`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Number=Plur\|POS=VERB\|Person=3\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Definite=Def\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Unsp\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=AUX`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `POS=INTJ`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Unsp\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Gender=Unsp\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Unsp\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Unsp\|POS=VERB\|PronType=Prs\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Unsp\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=AUX\|Person=3\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PART`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Unsp\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=ADV`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Number=Plur\|POS=VERB\|Person=1\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Gender=Masc\|POS=ADJ`, `POS=NOUN`, `POS=AUX\|VerbForm=Ger`, `Case=Dat\|Gender=Unsp\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Unsp\|Number=Plur\|POS=PROPN`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Gender=Unsp\|POS=PRON\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Prs`, `Gender=Unsp\|Number=Unsp\|POS=PRON\|PronType=Rel`, `POS=VERB\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=PROPN\|PronType=Art`, `Case=Dat\|Gender=Unsp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Unsp\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Unsp\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Gender=Unsp\|Number=Unsp\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADV\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Gender=Unsp\|Number=Unsp\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Unsp\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=X`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=SCONJ`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Gender=Fem\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `POS=ADP\|PronType=Dem`, `Definite=Def\|Gender=Fem\|POS=ADP\|PronType=Art`, `POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `POS=DET`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Gender=Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Art`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Unsp\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=AUX\|Person=1\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Ind`, `Case=Dat\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Plur\|POS=VERB\|Person=2\|PronType=Prs\|VerbForm=Inf`, `Gender=Unsp\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem,Masc\|Number=Sing\|POS=PROPN`, `Gender=Unsp\|Number=Unsp\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=NUM`, `POS=PRON\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `POS=SYM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=X`, `Case=Dat\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Sets\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Unsp\|POS=AUX\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Unsp\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Unsp\|Number=Plur\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Number=Sing\|POS=VERB`, `Number=Sing\|POS=DET`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `NumType=Mult\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Unsp\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Unsp\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Unsp\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Unsp\|POS=NOUN`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NOUN`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=SCONJ\|PronType=Art`, `POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Dat\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Unsp\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Gender=Unsp\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Unsp\|Number=Sing\|POS=DET\|PronType=Rel`, `Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Gender=Unsp\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Range\|POS=NUM`, `Case=Dat\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Gender=Unsp\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Number=Unsp\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Dem`, `NumType=Frac\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Ind`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Rel`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Ger`, `Mood=Cnd\|POS=VERB\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf\|Voice=Pass`, `POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Unsp\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Number=Sing\|POS=X`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Int`, `Case=Dat\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `POS=VERB`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Unsp\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Dat\|Gender=Fem,Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Unsp\|Number=Unsp\|POS=ADV\|PronType=Int`, `Gender=Unsp\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `POS=PROPN`, `Case=Acc\|Gender=Masc\|POS=AUX\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Fem\|POS=AUX\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Fem\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Unsp\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=X`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Fem\|POS=DET\|PronType=Art`, `Gender=Unsp\|Number=Sing\|POS=ADV`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Unsp\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=VERB`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `POS=DET\|PronType=Ind`, `POS=SCONJ\|VerbForm=Ger`, `Mood=Cnd\|Number=Sing\|POS=VERB\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=VERB`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|POS=PRON\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|POS=PROPN`, `Gender=Fem\|Number=Plur\|POS=DET`, `NumType=Ord\|POS=NUM`, `POS=DET\|PronType=Int`, `Case=Acc\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `POS=PART`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Inf`, `NumType=Card\|POS=ADP`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=SCONJ\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Art`, `Case=Dat\|Gender=Unsp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `dislocated`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `3`, `4`, `6`, `8`, `9`, `11`, `13`, `15`, `17`, `20`, `22`, `24`, `14`, `7`, `26`, `28`, `30`, `32`, `34`, `36`, `38`, `40`, `42`, `44`, `45`, `48`, `53`, `54`, `55`, `57`, `58`, `60`, `62`, `65`, `66`, `67`, `70`, `72`, `74`, `76`, `79`, `83`, `85`, `87`, `89`, `91`, `95`, `99`, `101`, `102`, `104`, `106`, `108`, `110`, `113`, `115`, `117`, `119`, `120`, `122`, `124`, `125`, `126`, `128`, `130`, `132`, `134`, `136`, `138`, `141`, `142`, `144`, `147`, `150`, `152`, `154`, `155`, `159`, `162`, `163`, `165`, `166`, `169`, `171`, `172`, `174`, `175`, `178`, `180`, `181`, `184`, `186`, `189`, `191`, `193`, `195`, `198`, `200`, `111`, `202`, `204`, `207`, `209`, `212`, `214`, `216`, `218`, `220`, `221`, `223`, `224`, `226`, `228`, `230`, `232`, `234`, `236`, `239`, `242`, `244`, `245`, `246`, `247`, `249`, `251`, `252`, `253`, `256`, `257`, `259`, `261`, `263`, `267`, `269`, `270`, `271`, `273`, `277`, `278`, `281`, `282`, `283`, `285`, `286`, `288`, `289`, `290`, `292`, `293`, `295`, `297`, `298`, `300`, `302`, `303`, `305`, `307`, `309`, `310`, `311`, `313`, `314`, `316`, `319`, `168`, `322`, `323`, `326`, `327`, `329`, `331`, `333`, `335`, `336`, `338`, `341`, `343`, `345`, `347`, `348`, `350`, `351`, `354`, `356`, `359`, `361`, `363`, `364`, `365`, `366`, `367`, `369`, `373`, `376`, `378`, `379`, `380`, `381`, `383`, `384`, `386`, `389`, `392`, `394`, `395`, `396`, `398`, `400`, `403`, `405`, `407`, `409`, `410`, `412`, `415`, `416`, `417`, `418`, `419`, `420`, `422`, `424`, `429`, `431`, `432`, `438`, `439`, `441`, `442`, `445`, `448`, `449`, `450`, `452`, `454`, `457`, `458`, `461`, `463`, `465`, `468`, `469`, `470`, `473`, `475`, `477`, `478`, `481`, `484`, `485`, `486`, `488`, `491`, `495`, `497`, `499`, `503`, `506`, `507`, `508`, `509`, `510`, `511`, `513`, `514`, `516`, `517`, `519`, `521`, `522`, `523`, `525`, `528`, `530`, `533`, `534`, `536`, `538`, `540`, `541`, `542`, `544`, `545`, `547`, `549`, `551`, `552`, `554`, `555`, `558`, `559`, `560`, `562`, `563`, `565`, `566`, `570`, `572`, `579`, `582`, `583`, `585`, `586`, `587`, `590`, `592`, `594`, `595`, `597`, `599`, `601`, `603`, `606`, `608`, `609`, `611`, `612`, `614`, `615`, `616`, `619`, `621`, `622`, `625`, `626`, `627`, `629`, `630`, `631`, `633`, `634`, `637`, `638`, `639`, `640`, `642`, `644`, `646`, `647`, `652`, `653`, `656`, `657`, `659`, `660`, `661`, `664`, `666`, `669`, `671`, `672`, `673`, `674`, `675`, `677`, `678`, `680`, `682`, `685`, `687`, `689`, `691`, `692`, `693`, `695`, `699`, `701`, `702`, `703`, `706`, `707`, `709`, `710`, `711`, `712`, `714`, `716`, `718`, `719`, `720`, `721`, `724`, `725`, `729`, `730`, `732`, `735`, `738`, `740`, `742`, `744`, `746`, `749`, `750`, `751`, `754`, `756`, `760`, `762`, `767`, `769`, `771`, `774`, `776`, `778`, `780`, `781`, `784`, `785`, `787`, `788`, `789`, `791`, `793`, `794`, `795`, `798`, `800`, `801`, `803`, `804`, `806`, `808`, `810`, `811`, `812`, `814`, `816`, `819`, `820`, `823`, `824`, `825`, `828`, `829`, `832`, `833`, `835`, `836`, `839`, `840`, `844`, `845`, `847`, `850`, `851`, `853`, `854`, `855`, `858`, `861`, `862`, `863`, `865`, `868`, `871`, `873`, `875`, `877`, `879`, `880`, `881`, `882`, `883`, `884`, `885`, `887`, `889`, `892`, `894`, `895`, `537`, `896`, `898`, `899`, `902`, `904`, `905`, `908`, `909`, `912`, `914`, `916`, `917`, `920`, `921`, `922`, `924`, `925`, `928`, `929`, `930`, `931`, `933`, `936`, `939`, `940`, `942`, `943`, `945`, `948`, `949`, `951`, `953`, `956`, `957`, `960`, `961`, `963`, `964`, `965`, `966`, `969`, `970`, `971`, `973`, `976`, `977`, `979`, `981`, `983`, `985`, `987`, `988`, `990`, `991`, `993`, `994`, `995`, `996`, `997`, `998`, `1000`, `1001`, `1004`, `1006`, `1007`, `1009`, `1011`, `1013`, `1014`, `1015`, `1019`, `1021`, `1023`, `1025`, `1026`, `1029`, `1030`, `1033`, `1034`, `1036`, `1037`, `1039`, `1041`, `1042`, `1044`, `1046`, `1048`, `1050`, `1051`, `1054`, `1056`, `1057`, `1059`, `1061`, `1062`, `1064`, `1066`, `1067`, `1068`, `1069`, `1071`, `1072`, `1073`, `1074`, `1075`, `1077`, `1078`, `1079`, `1081`, `1083`, `1084`, `1085`, `1086`, `1088`, `1089`, `1092`, `1093`, `1097`, `1100`, `1101`, `1103`, `1104`, `1106`, `1108`, `1110`, `1114`, `1115`, `1117`, `1118`, `1119`, `1121`, `1123`, `1124`, `1126`, `1127`, `1128`, `1130`, `1133`, `1135`, `1136`, `1140`, `1143`, `1146`, `1148`, `1149`, `1151`, `1152`, `1155`, `1157`, `1158`, `1160`, `1163`, `1164`, `1165`, `1167`, `1168`, `1170`, `1172`, `1176`, `1177`, `1178`, `1180`, `1182`, `1184`, `1186`, `1187`, `1189`, `1190`, `1193`, `1196`, `1198`, `1202`, `1203`, `1204`, `1205`, `1206`, `1207`, `1208`, `1209`, `1210`, `1211`, `1214`, `1215`, `1216`, `1218`, `1219`, `1220`, `1221`, `1223`, `1225`, `1226`, `1228`, `1229`, `1230`, `1233`, `1234`, `1236`, `1237`, `1238`, `1239`, `1240`, `1242`, `1244`, `1247`, `1248`, `1249`, `1250`, `1251`, `1254`, `1256`, `1257`, `1258`, `1260`, `1262`, `1263`, `1266`, `1271`, `1272`, `1273`, `1274`, `1275`, `1277`, `1278`, `1279`, `1280`, `1283`, `1285`, `1287`, `1288`, `1290`, `1293`, `1294`, `1296`, `1299`, `1301`, `1302`, `1304`, `1307`, `1308`, `1309`, `1311`, `1312`, `1314`, `1315`, `1317`, `1320`, `1322`, `1324`, `1325`, `1326`, `1329`, `1330`, `1332`, `1333`, `1334`, `1336`, `1338`, `1339`, `1340`, `1341`, `1344`, `1345`, `1346`, `1348`, `1350`, `1351`, `1352`, `1354`, `1356`, `1358`, `1359`, `1360`, `1361`, `1362`, `1363`, `1367`, `1370`, `1371`, `1373`, `1375`, `1377`, `1378`, `1379`, `1381`, `1382`, `1383`, `1385`, `1386`, `1388`, `1389`, `1393`, `1395`, `1399`, `1401`, `1402`, `1403`, `1404`, `1405`, `1407`, `1408`, `1411`, `1413`, `1417`, `1418`, `1419`, `1420`, `1421`, `1423`, `1424`, `1425`, `1429`, `1430`, `1431`, `1433`, `1434`, `1436`, `1437`, `1438`, `1439`, `1442`, `1444`, `1446`, `1447`, `1449`, `1451`, `1453`, `1454`, `1455`, `1458`, `1461`, `1463`, `1464`, `1465`, `1467`, `1468`, `1469`, `1470`, `1471`, `1473`, `1476`, `1477`, `1478`, `1479`, `1482`, `1483`, `1484`, `1489`, `1491`, `1492`, `1494`, `1496`, `1497`, `1499`, `1502`, `1504`, `1505`, `1506`, `1507`, `1508`, `1509`, `1511`, `1514`, `1515`, `1517`, `1520`, `1521`, `1524`, `1525`, `1528`, `1529`, `1530`, `1532`, `1533`, `1534`, `1536`, `1538`, `1539`, `1541`, `1543`, `1544`, `1545`, `1546`, `1547`, `1548`, `1552`, `1556`, `1558`, `1560`, `1562`, `1563`, `1566`, `1567`, `1569`, `1570`, `1572`, `1574`, `1577`, `761`, `1579`, `1583`, `1585`, `1586`, `1587`, `1590`, `1592`, `1593`, `1595`, `1596`, `1597`, `1599`, `1603`, `1605`, `1607`, `1609`, `1610`, `1612`, `1614`, `1615`, `1617`, `1618`, `1620`, `1621`, `1622`, `1625`, `1627`, `1629`, `1630`, `1631`, `1633`, `1634`, `1636`, `1637`, `1638`, `1640`, `1641`, `1643`, `1644`, `1646`, `1647`, `1648`, `1651`, `1652`, `1657`, `1658`, `1659`, `1661`, `1662`, `1663`, `1664`, `1666`, `1669`, `1672`, `1673`, `1675`, `1676`, `1677`, `1679`, `1682`, `1684`, `1409`, `1685`, `1686`, `1687`, `1688`, `1690`, `1692`, `1693`, `1694`, `1695`, `1697`, `1699`, `1700`, `1704`, `1707`, `1708`, `1709`, `1711`, `1712`, `1715`, `1716`, `1717`, `1718`, `1719`, `1721`, `1722`, `1723`, `1725`, `1726`, `1729`, `1730`, `1732`, `1733`, `1734`, `1735`, `1737`, `1738`, `1741`, `1743`, `1744`, `1746`, `1747`, `1748`, `1750`, `1752`, `1754`, `1755`, `1756`, `1758`, `1759`, `1760`, `1762`, `1765`, `1766`, `1768`, `1769`, `1770`, `1773`, `1774`, `1775`, `1777`, `1778`, `1781`, `1782`, `1783`, `1785`, `1786`, `1787`, `219`, `1788`, `1789`, `1791`, `1792`, `1793`, `1795`, `1799`, `1800`, `1801`, `1802`, `1803`, `1805`, `1806`, `1808`, `1809`, `1811`, `1812`, `1814`, `1815`, `1816`, `1821`, `1823`, `1824`, `1825`, `1826`, `1829`, `1830`, `1831`, `1832`, `1833`, `1835`, `1838`, `1839`, `1840`, `1842`, `1843`, `1845`, `1846`, `1848`, `1849`, `1850`, `1851`, `1855`, `1856`, `1857`, `1859`, `1861`, `1862`, `1864`, `1866`, `1867`, `1869`, `421`, `1870`, `1872`, `1873`, `1874`, `1875`, `1878`, `1879`, `1880`, `1882`, `1883`, `1884`, `1885`, `1888`, `1891`, `1894`, `1895`, `1898`, `1901`, `1903`, `1904`, `1906`, `1907`, `1910`, `1912`, `1915`, `1917`, `1918`, `1920`, `1921`, `1922`, `1924`, `1926`, `1927`, `1930`, `1932`, `1933`, `1936`, `1938`, `1940`, `1941`, `1942`, `1943`, `1945`, `1947`, `1949`, `1951`, `1952`, `1953`, `1954`, `1956`, `1957`, `1958`, `1960`, `1961`, `1963`, `1964`, `1966`, `1968`, `1971`, `1973`, `1974`, `1975`, `1977`, `1979`, `1981`, `1983`, `1985`, `1986`, `1987`, `1988`, `792`, `1990`, `790`, `1992`, `1994`, `1996`, `1998`, `1999`, `2000`, `2001`, `2002`, `2003`, `2005`, `2006`, `2008`, `2010`, `2011`, `2012`, `2014`, `2016`, `2017`, `2018`, `2019`, `2021`, `2022`, `2023`, `2024`, `2025`, `2026`, `2028`, `2029`, `2031`, `2034`, `2036`, `2038`, `2041`, `2042`, `2044`, `2045`, `2046`, `2050`, `2051`, `2052`, `2055`, `2056`, `2057`, `2059`, `2060`, `2061`, `2062`, `2064`, `2066`, `2068`, `2069`, `2070`, `2072`, `2073`, `2075`, `2076`, `2078`, `2079`, `2081`, `2083`, `2084`, `2086`, `2088`, `2089`, `2091`, `2093`, `2095`, `2097`, `2098`, `2099`, `2101`, `2102`, `2103`, `2104`, `2106`, `2107`, `2108`, `2109`, `2110`, `2111`, `2112`, `2114`, `2116`, `2117`, `2118`, `2119`, `2120`, `2121`, `2122`, `2123`, `2124`, `2125`, `2126`, `2127`, `1584`, `2128`, `2130`, `2131`, `2132`, `2134`, `2137`, `2138`, `2139`, `2141`, `2144`, `2145`, `2146`, `2147`, `2150`, `2151`, `2153`, `2154`, `2155`, `2156`, `2157`, `2159`, `2160`, `2161`, `2163`, `2164`, `2165`, `2166`, `2167`, `2168`, `2169`, `2170`, `2173`, `2174`, `2175`, `2176`, `2177`, `2179`, `2182`, `2185`, `2187`, `2188`, `2189`, `2191`, `2193`, `2194`, `2195`, `2196`, `2197`, `2198`, `2200`, `2202`, `2203`, `2204`, `2205`, `2206`, `2207`, `2208`, `2209`, `2210`, `2211`, `2212`, `2213`, `2216`, `2217`, `2219`, `2221`, `2224`, `2227`, `2229`, `2230`, `2232`, `2233`, `2234`, `2235`, `2237`, `2239`, `2240`, `2241`, `2242`, `2243`, `2244`, `2245`, `2246`, `2248`, `2249`, `2250`, `2251`, `2253`, `2254`, `2255`, `2257`, `2258`, `2260`, `2261`, `2262`, `2263`, `2264`, `2265`, `2266`, `2267`, `2268`, `2269`, `2270`, `2271`, `2272`, `2273`, `2274`, `2275`, `2277`, `2278`, `2281`, `2282`, `2283`, `2284`, `2285`, `2287`, `2288`, `2290`, `2291`, `2292`, `2293`, `2294`, `2297`, `2298`, `2299`, `2300`, `2302`, `2304`, `2305`, `2307`, `2308`, `2309`, `2310`, `2312`, `2313`, `2314`, `2315`, `2316`, `2317`, `2318`, `2319`, `2321`, `2322`, `2323`, `2327`, `2329`, `2331`, `2333`, `2335`, `2337`, `2338`, `2339`, `2341`, `2342`, `2343`, `2346`, `2348`, `2349`, `2350`, `2351`, `2352`, `2353`, `37`, `2354`, `2355`, `2357`, `2358`, `2359`, `2360`, `2361`, `2362`, `2364`, `2365`, `2367`, `2368`, `2369`, `2370`, `2372`, `2375`, `2376`, `2378`, `2379`, `2380`, `2381`, `2382`, `2383`, `2384`, `2385`, `2386`, `2389`, `2390`, `2392`, `2393`, `2394`, `2395`, `2398`, `2399`, `2400`, `2402`, `2403`, `2405`, `2406`, `2407`, `2408`, `2409`, `2410`, `2413`, `2415`, `2416`, `2417`, `2418`, `2419`, `2420`, `2422`, `2424`, `2427`, `2428`, `2429`, `2430`, `2431`, `2432`, `2433`, `2435`, `2437`, `1962`, `2438`, `2439`, `2440`, `2442`, `2443`, `2444`, `2445` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.92 |
| `TOKEN_P` | 99.93 |
| `TOKEN_R` | 99.91 |
| `TOKEN_ACC` | 99.99 |
| `SENTS_F` | 95.82 |
| `SENTS_P` | 95.40 |
| `SENTS_R` | 96.25 |
| `TAG_ACC` | 98.09 |
| `POS_ACC` | 98.14 |
| `MORPH_ACC` | 97.34 |
| `DEP_UAS` | 93.85 |
| `DEP_LAS` | 91.19 |
| `LEMMA_ACC` | 98.00 |
|
{"language": ["pt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/pt_udv25_portuguesebosque_trf
|
[
"spacy",
"token-classification",
"pt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pt"
] |
TAGS
#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Portuguese-Bosque
### Label Scheme
View label scheme (2079 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (2079 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2079 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (2079 labels for 6 components)### Accuracy"
] |
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0.10272999852895737,
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] |
null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Romanian-Nonstandard
| Feature | Description |
| --- | --- |
| **Name** | `ro_udv25_romaniannonstandard_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (7445 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `AdpType=Prep\|Case=Acc`, `Afp`, `Afpf--n`, `Afpfp-n`, `Afpfpon`, `Afpfpoy`, `Afpfprn`, `Afpfpry`, `Afpfson`, `Afpfsoy`, `Afpfsrn`, `Afpfsry`, `Afpmp-n`, `Afpmpoy`, `Afpmprn`, `Afpmpry`, `Afpmpvy`, `Afpms-n`, `Afpmsoy`, `Afpmsrn`, `Afpmsry`, `Afpmsvn`, `Afpmsvy`, `COLON`, `COMMA`, `Cccsp`, `Cccsz`, `Ccssp`, `Ccssz`, `Cscsp`, `Csssp`, `DASH`, `DBLQ`, `Dd3-po---e`, `Dd3-po---o`, `Dd3fpo`, `Dd3fpr`, `Dd3fpr---e`, `Dd3fpr---o`, `Dd3fso`, `Dd3fso---e`, `Dd3fso---o`, `Dd3fsr`, `Dd3fsr---e`, `Dd3fsr---o`, `Dd3mpo`, `Dd3mpr`, `Dd3mpr---e`, `Dd3mpr---o`, `Dd3mso`, `Dd3mso---e`, `Dd3mso---o`, `Dd3msr`, `Dd3msr---e`, `Dd3msr---o`, `Dh1mp`, `Dh1ms`, `Dh2mp`, `Dh2ms`, `Dh3fp`, `Dh3mp`, `Dh3ms`, `Di3--r`, `Di3-po`, `Di3-sr`, `Di3fp`, `Di3fpo`, `Di3fpr`, `Di3fso`, `Di3fsr`, `Di3mpr`, `Di3mso`, `Di3msr`, `Ds1fp-p`, `Ds1fp-s`, `Ds1fsop`, `Ds1fsos`, `Ds1fsrp`, `Ds1fsrs`, `Ds1mp-p`, `Ds1mp-s`, `Ds1ms-p`, `Ds1ms-s`, `Ds2fp-p`, `Ds2fp-s`, `Ds2fsop`, `Ds2fsos`, `Ds2fsrp`, `Ds2fsrs`, `Ds2mp-p`, `Ds2mp-s`, `Ds2ms-p`, `Ds2ms-s`, `Ds3fp-s`, `Ds3fsos`, `Ds3fsrs`, `Ds3mp-s`, `Ds3ms-s`, `Dw3--r`, `Dw3-po`, `Dw3fpr`, `Dw3fso`, `Dw3fsr`, `Dw3mpr`, `Dw3mso`, `Dw3msr`, `Dz3fpr`, `Dz3fsr`, `Dz3msr`, `EXCL`, `EXCLHELLIP`, `HELLIP`, `I`, `LPAR`, `M`, `Mc-p-l`, `Mcfp-l`, `Mcfpol`, `Mcfprln`, `Mcfsoln`, `Mcfsoly`, `Mcfsrln`, `Mcfsrly`, `Mcmp-l`, `Mcms-ln`, `Mcmsoly`, `Mcmsrl`, `Mcmsrly`, `Mffsrln`, `Ml-po`, `Mlfpr`, `Mlmpr`, `Mmfpr-n`, `Mmmpr-n`, `Mmmsr-n`, `Mo---l`, `Mo---ln`, `Mo-s-r`, `Mofprln`, `Mofprly`, `Mofs-l`, `Mofs-ly`, `Mofsrln`, `Mofsrly`, `Momp-ln`, `Moms-l`, `Moms-ln`, `Momsoly`, `Momsrly`, `Ncfpoy`, `Ncfprn`, `Ncfpry`, `Ncfpvy`, `Ncfson`, `Ncfsoy`, `Ncfsrn`, `Ncfsry`, `Ncfsvn`, `Ncfsvy`, `Ncmpoy`, `Ncmprn`, `Ncmpry`, `Ncmpvy`, `Ncmson`, `Ncmsoy`, `Ncmsrn`, `Ncmsry`, `Ncmsvn`, `Ncmsvy`, `Ncnsrn`, `Np`, `Npfpoy`, `Npfprn`, `Npfpry`, `Npfsoy`, `Npfsrn`, `Npfsry`, `Npfsvn`, `Npmpoy`, `Npmprn`, `Npmpry`, `Npmsoy`, `Npmsrn`, `Npmsry`, `Npmsvn`, `Npmsvy`, `PERIOD`, `Pd3-po`, `Pd3-po---o`, `Pd3fpo`, `Pd3fpr`, `Pd3fso`, `Pd3fsr`, `Pd3mpo`, `Pd3mpr`, `Pd3mso`, `Pd3msr`, `Ph1mp`, `Ph1ms`, `Ph2mp`, `Ph2ms`, `Ph3--r`, `Ph3fp`, `Ph3fsr`, `Ph3mp`, `Ph3mpo`, `Ph3mpr`, `Ph3ms`, `Ph3mso`, `Pi3--r`, `Pi3-po`, `Pi3-so`, `Pi3-sr`, `Pi3fpo`, `Pi3fpr`, `Pi3fso`, `Pi3fsr`, `Pi3mpo`, `Pi3mpr`, `Pi3mpry`, `Pi3mso`, `Pi3msr`, `Pi3msry`, `Pp1-pa--------s`, `Pp1-pa--------w`, `Pp1-pd--------s`, `Pp1-pd--------w`, `Pp1-pr`, `Pp1-sa--------s`, `Pp1-sa--------w`, `Pp1-sd--------s`, `Pp1-sd--------w`, `Pp1-sr`, `Pp2-pa--------s`, `Pp2-pa--------w`, `Pp2-pd--------s`, `Pp2-pd--------w`, `Pp2-po`, `Pp2-pr`, `Pp2-sa--------s`, `Pp2-sa--------w`, `Pp2-sd--------s`, `Pp2-sd--------w`, `Pp2-so`, `Pp2-sr`, `Pp3-pd--------s`, `Pp3-pd--------w`, `Pp3-po`, `Pp3-pr`, `Pp3-sd--------w`, `Pp3-so`, `Pp3fpa--------s`, `Pp3fpa--------w`, `Pp3fpr`, `Pp3fsa--------s`, `Pp3fsa--------w`, `Pp3fsd--------s`, `Pp3fso`, `Pp3fsoy`, `Pp3fsr`, `Pp3mpa--------s`, `Pp3mpa--------w`, `Pp3mpo`, `Pp3mpr`, `Pp3msa--------s`, `Pp3msa--------w`, `Pp3msd--------s`, `Pp3mso`, `Pp3msr`, `Pp3msry`, `Ps1fp-p`, `Ps1fp-s`, `Ps1fsrp`, `Ps1fsrs`, `Ps1mp-p`, `Ps1ms-p`, `Ps1ms-s`, `Ps2fp-p`, `Ps2fp-s`, `Ps2fsrp`, `Ps2fsrs`, `Ps2mp-s`, `Ps2ms-p`, `Ps2ms-s`, `Ps3fp-s`, `Ps3fsrs`, `Ps3mp-s`, `Ps3ms-s`, `Pw3--r`, `Pw3-po`, `Pw3-pr`, `Pw3-pry`, `Pw3-so`, `Pw3fpr`, `Pw3fpry`, `Pw3fso`, `Pw3fsr`, `Pw3fsry`, `Pw3mpr`, `Pw3mpry`, `Pw3mso`, `Pw3msr`, `Pw3msry`, `Px3--a--------s`, `Px3--a--------w`, `Px3--d--------s`, `Px3--d--------w`, `Px3--d-------w`, `Pz3-so`, `Pz3-sr`, `Pz3fsr`, `Pz3mso`, `Pz3msr`, `QUEST`, `QUOT`, `Qn`, `Qs`, `Qz`, `RPAR`, `Rg`, `Ri`, `Rw`, `Rz`, `SCOLON`, `Sp`, `Spca`, `Spcg`, `Spsa`, `Spsd`, `Spsg`, `TILDA`, `Td-po`, `Tdfpr`, `Tdfso`, `Tdfsr`, `Tdmpr`, `Tdmso`, `Tdmsr`, `Tf-so`, `Tffsr`, `Tfmso`, `Tfmsr`, `Ti-po`, `Ti-pr`, `Tifso`, `Tifsr`, `Timso`, `Timsr`, `Tsfpr`, `Tsfso`, `Tsfsr`, `Tsmpr`, `Tsmsr`, `Vag-----p`, `Vag-----z`, `Vaii1p`, `Vaii1s`, `Vaii2p`, `Vaii2s`, `Vaii3p`, `Vaii3s`, `Vail3s`, `Vaip1p`, `Vaip1s`, `Vaip2p`, `Vaip2s`, `Vaip3`, `Vaip3p`, `Vaip3s`, `Vais1p`, `Vais1s`, `Vais2p`, `Vais2s`, `Vais3p`, `Vais3s`, `Vam-2p`, `Vam-2p---l`, `Vam-2s--p`, `Vam-2s--z`, `Vam-2s-p`, `Vam-2s-z`, `Vamip3p`, `Vamip3s`, `Vamn`, `Vamsp3`, `Van`, `Van------l`, `Vap`, `Vap--sm-p`, `Vasp1p`, `Vasp1s`, `Vasp2p`, `Vasp2s`, `Vasp3`, `Vasp3s`, `Vmg-----p`, `Vmg-----z`, `Vmii1p`, `Vmii1s`, `Vmii2p`, `Vmii2s`, `Vmii3p`, `Vmii3s`, `Vmil1s`, `Vmil2p`, `Vmil2s`, `Vmil3p`, `Vmil3s`, `Vmip1p`, `Vmip1s`, `Vmip2p`, `Vmip2s`, `Vmip3`, `Vmip3p`, `Vmip3s`, `Vmis1p`, `Vmis1s`, `Vmis2p`, `Vmis2s`, `Vmis3p`, `Vmis3s`, `Vmm-2p`, `Vmm-2p---l`, `Vmm-2s--p`, `Vmm-2s--z`, `Vmn`, `Vmn------l`, `Vmp`, `Vmp--pf-p`, `Vmp--pf-z`, `Vmp--pm-p`, `Vmp--pm-z`, `Vmp--sf-p--o`, `Vmp--sf-p--r`, `Vmp--sf-z--r`, `Vmp--sm-p`, `Vmp--sm-z`, `Vmsp1p`, `Vmsp1s`, `Vmsp2p`, `Vmsp2s`, `Vmsp3`, `Vmsp3s`, `X`, `Y` |
| **`morphologizer`** | `AdpType=Prep\|Case=Acc\|POS=ADP`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `POS=ADV\|PronType=Int,Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|POS=PRON\|Person=3\|PronType=Int,Rel`, `POS=CCONJ\|Polarity=Pos`, `Compound=Yes\|POS=SCONJ\|Polarity=Pos`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PART\|PartType=Sub`, `Mood=Sub\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `POS=VERB\|VerbForm=Inf`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADV\|Polarity=Neg`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `POS=AUX\|Polarity=Pos\|VerbForm=Ger`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|Polarity=Pos\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=INTJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ\|Polarity=Pos`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `AdpType=Prep\|Case=Acc\|Compound=Yes\|POS=ADP`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat,Gen\|Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|POS=DET\|Person=3\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Voc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres`, `POS=AUX\|VerbForm=Part`, `POS=VERB\|VerbForm=Part`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `POS=PART\|PartType=Inf`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Art`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Art`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Mood=Sub\|POS=AUX\|Person=3\|Tense=Pres`, `Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `NumForm=Digit\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `POS=PROPN`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Compound=Yes\|POS=CCONJ\|Polarity=Neg`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `POS=AUX\|VerbForm=Inf`, `AdpType=Prep\|Case=Gen\|Compound=Yes\|POS=ADP`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=2\|PronType=Emp`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Polarity=Neg\|VerbForm=Ger`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Emp`, `Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Variant=Long\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Acc,Nom\|Definite=Def\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Mood=Sub\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Voc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Compound=Yes\|POS=CCONJ\|Polarity=Pos`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Voc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Art`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Emp`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Acc,Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Case=Acc,Nom\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `POS=VERB\|Variant=Long\|VerbForm=Inf`, `Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `AdpType=Prep\|Case=Dat\|POS=ADP`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|PronType=Prs`, `Compound=Yes\|POS=ADV\|Polarity=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Art`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Definite=Ind\|NumForm=Word\|NumType=Ord\|POS=NUM`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=AUX\|Variant=Long\|VerbForm=Inf`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat,Gen\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `POS=X`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Emp`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Case=Acc,Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=AUX\|Polarity=Neg\|VerbForm=Ger`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Case=Acc,Nom\|POS=DET\|Person=3\|PronType=Ind`, `Case=Voc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `POS=CCONJ\|Polarity=Neg`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Voc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Polite=Form\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past`, `Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Voc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `POS=PRON\|Polarity=Pos`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Emp`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|PronType=Emp`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|Position=Postnom\|PronType=Dem`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Emp`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Emp`, `Case=Dat,Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Compound=Yes\|POS=ADP\|Polarity=Pos`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADJ`, `Case=Voc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `POS=ADV\|PronType=Ind`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `POS=AUX\|Polarity=Pos`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres`, `NumForm=Roman\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp`, `Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Variant=Long\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Variant=Long`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Imp`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|POS=PRON\|Person=3\|PronType=Emp`, `NumForm=Word\|NumType=Ord\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=1\|PronType=Emp`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|PronType=Emp`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Art`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Emp`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past`, `Case=Dat,Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pqp`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Weak`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Weak`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Person=3\|Tense=Pres`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Strong`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|POS=ADJ`, `POS=DET`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADP`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumType=Mult\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Neg`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=ADJ`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Part`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=ADV\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Polite=Form\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advcl:tcl`, `advmod`, `advmod:tmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `ccomp:pmod`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `expl:impers`, `expl:pass`, `expl:poss`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:agent`, `nmod:pmod`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `3`, `4`, `6`, `8`, `12`, `14`, `16`, `19`, `23`, `29`, `30`, `32`, `35`, `37`, `39`, `40`, `45`, `46`, `47`, `51`, `53`, `54`, `57`, `61`, `63`, `65`, `66`, `69`, `33`, `71`, `73`, `76`, `79`, `80`, `84`, `86`, `87`, `88`, `89`, `92`, `95`, `97`, `100`, `103`, `105`, `107`, `110`, `112`, `113`, `115`, `117`, `120`, `121`, `123`, `125`, `126`, `128`, `130`, `132`, `133`, `136`, `140`, `143`, `145`, `147`, `58`, `148`, `151`, `154`, `157`, `159`, `163`, `165`, `167`, `171`, `174`, `176`, `178`, `180`, `182`, `184`, `185`, `187`, `188`, `190`, `192`, `196`, `197`, `199`, `200`, `202`, `206`, `208`, `210`, `211`, `213`, `215`, `216`, `219`, `221`, `223`, `225`, `226`, `228`, `230`, `232`, `236`, `238`, `241`, `242`, `244`, `246`, `248`, `251`, `253`, `255`, `258`, `260`, `264`, `265`, `267`, `272`, `275`, `278`, `280`, `281`, `284`, `286`, `287`, `290`, `291`, `292`, `295`, `296`, `298`, `300`, `301`, `302`, `305`, `306`, `307`, `309`, `310`, `312`, `314`, `315`, `317`, `319`, `321`, `323`, `324`, `327`, `330`, `332`, `334`, `335`, `337`, `339`, `340`, `343`, `344`, `345`, `346`, `350`, `351`, `353`, `355`, `357`, `360`, `362`, `366`, `368`, `369`, `370`, `371`, `224`, `374`, `376`, `378`, `379`, `381`, `384`, `385`, `386`, `388`, `389`, `391`, `392`, `393`, `396`, `398`, `399`, `403`, `406`, `408`, `411`, `413`, `415`, `418`, `422`, `423`, `426`, `427`, `431`, `433`, `436`, `438`, `440`, `442`, `445`, `448`, `449`, `450`, `451`, `452`, `454`, `455`, `457`, `459`, `460`, `462`, `464`, `466`, `468`, `471`, `472`, `473`, `474`, `475`, `478`, `481`, `482`, `485`, `486`, `488`, `490`, `492`, `494`, `495`, `497`, `498`, `499`, `501`, `503`, `504`, `506`, `508`, `510`, `513`, `514`, `515`, `516`, `518`, `519`, `521`, `523`, `524`, `526`, `527`, `528`, `530`, `533`, `96`, `537`, `538`, `539`, `542`, `544`, `545`, `547`, `548`, `553`, `555`, `556`, `558`, `559`, `561`, `562`, `563`, `565`, `566`, `570`, `572`, `573`, `575`, `577`, `578`, `579`, `581`, `583`, `584`, `586`, `588`, `589`, `592`, `594`, `595`, `596`, `598`, `599`, `600`, `601`, `604`, `606`, `607`, `608`, `612`, `613`, `616`, `619`, `621`, `623`, `625`, `628`, `629`, `630`, `632`, `635`, `636`, `173`, `639`, `641`, `643`, `647`, `649`, `651`, `654`, `656`, `658`, `659`, `661`, `662`, `663`, `666`, `668`, `669`, `670`, `672`, `673`, `676`, `677`, `679`, `681`, `683`, `685`, `687`, `689`, `690`, `691`, `693`, `694`, `695`, `696`, `698`, `699`, `701`, `702`, `703`, `704`, `705`, `706`, `708`, `712`, `713`, `716`, `718`, `720`, `722`, `724`, `725`, `729`, `732`, `734`, `735`, `736`, `739`, `742`, `745`, `747`, `750`, `753`, `755`, `758`, `759`, `761`, `763`, `764`, `766`, `768`, `769`, `771`, `772`, `774`, `777`, `778`, `781`, `784`, `785`, `787`, `790`, `794`, `797`, `800`, `801`, `802`, `804`, `807`, `809`, `814`, `817`, `820`, `821`, `822`, `824`, `827`, `828`, `829`, `832`, `834`, `836`, `837`, `839`, `840`, `841`, `843`, `844`, `846`, `847`, `848`, `850`, `851`, `852`, `855`, `116`, `856`, `860`, `861`, `863`, `866`, `868`, `869`, `871`, `874`, `875`, `877`, `879`, `881`, `884`, `886`, `888`, `890`, `891`, `892`, `894`, `897`, `898`, `900`, `901`, `902`, `904`, `905`, `908`, `913`, `914`, `916`, `917`, `918`, `921`, `922`, `924`, `927`, `929`, `932`, `934`, `935`, `937`, `939`, `941`, `943`, `946`, `948`, `949`, `951`, `952`, `954`, `955`, `956`, `958`, `960`, `963`, `965`, `968`, `971`, `972`, `974`, `978`, `981`, `983`, `984`, `986`, `988`, `989`, `991`, `992`, `994`, `997`, `998`, `1000`, `1001`, `1002`, `1004`, `1006`, `1007`, `1008`, `1010`, `1011`, `1013`, `1014`, `1015`, `1017`, `1019`, `1022`, `1024`, `1029`, `1030`, `1032`, `1034`, `767`, `1035`, `1036`, `1037`, `1038`, `1040`, `1041`, `1042`, `1044`, `1045`, `1046`, `1049`, `1050`, `1052`, `1053`, `1055`, `1058`, `1061`, `1065`, `1067`, `1068`, `1071`, `1072`, `1074`, `1076`, `1078`, `1080`, `1081`, `1083`, `1084`, `1086`, `1087`, `1090`, `1091`, `1093`, `1097`, `1098`, `1099`, `1100`, `1102`, `1105`, `1106`, `1107`, `1110`, `1111`, `1113`, `1116`, `1123`, `1126`, `1127`, `1128`, `1129`, `1131`, `1132`, `1133`, `1135`, `1137`, `1139`, `1141`, `1144`, `1145`, `1147`, `1149`, `1150`, `1152`, `1154`, `1155`, `1156`, `1157`, `1158`, `1115`, `1159`, `1160`, `1162`, `1163`, `1164`, `1165`, `1168`, `1170`, `1172`, `1173`, `1174`, `1175`, `1176`, `1177`, `1178`, `1179`, `1181`, `1183`, `1184`, `1186`, `1187`, `1191`, `1195`, `1197`, `1198`, `1200`, `1201`, `1203`, `1205`, `1207`, `1209`, `1211`, `1212`, `1214`, `1215`, `1217`, `1219`, `1220`, `1223`, `1225`, `1227`, `183`, `1228`, `1231`, `1232`, `1234`, `1237`, `1239`, `1240`, `1242`, `1245`, `1247`, `1248`, `1249`, `1251`, `1252`, `1254`, `1255`, `1257`, `1259`, `1261`, `1263`, `1264`, `1266`, `1268`, `1272`, `1273`, `1277`, `1278`, `1280`, `1281`, `1282`, `1285`, `1286`, `1290`, `1291`, `1294`, `1296`, `1298`, `1300`, `1301`, `1303`, `1305`, `1308`, `1309`, `1310`, `1311`, `1312`, `1314`, `1316`, `1318`, `1320`, `1322`, `1324`, `1325`, `1327`, `1329`, `1331`, `1333`, `1335`, `1337`, `1338`, `1339`, `1341`, `1342`, `1343`, `1344`, `1346`, `1347`, `1350`, `142`, `1354`, `1355`, `1357`, `1358`, `1360`, `1362`, `1365`, `1366`, `1367`, `1368`, `1369`, `744`, `1370`, `1372`, `1373`, `1374`, `1375`, `1376`, `1377`, `1378`, `1380`, `1381`, `1382`, `1383`, `1386`, `1388`, `1389`, `1390`, `1394`, `1396`, `1399`, `1402`, `1405`, `1407`, `1409`, `1411`, `1412`, `1413`, `1414`, `1418`, `1419`, `1421`, `1422`, `1423`, `1424`, `1426`, `1427`, `1430`, `1432`, `1433`, `1434`, `1436`, `1438`, `1439`, `1440`, `1441`, `1442`, `1443`, `1446`, `1447`, `1448`, `1449`, `1450`, `1454`, `1456`, `1458`, `1459`, `1460`, `1464`, `1465`, `1467`, `1468`, `1469`, `1470`, `1472`, `1473`, `1475`, `1478`, `1479`, `1481`, `1483`, `1484`, `1486`, `1003`, `1489`, `1491`, `1493`, `1496`, `1498`, `1499`, `1501`, `1503`, `1506`, `1508`, `1511`, `1514`, `1515`, `1517`, `1518`, `1521`, `1522`, `1523`, `1524`, `1525`, `1528`, `1530`, `1531`, `1532`, `1533`, `1537`, `1539`, `1541`, `1542`, `1543`, `1545`, `1546`, `1547`, `1549`, `1550`, `1551`, `1552`, `1553`, `1555`, `1558`, `1559`, `1561`, `1562`, `1564`, `1566`, `1568`, `1570`, `1572`, `1576`, `1577`, `1579`, `1580`, `1582`, `1584`, `1585`, `1588`, `1590`, `1592`, `1593`, `1594`, `1596`, `1597`, `1599`, `1600`, `1601`, `1603`, `1605`, `1607`, `1609`, `1613`, `1615`, `1617`, `1619`, `1622`, `1623`, `1624`, `1625`, `1626`, `1627`, `1628`, `1629`, `1630`, `1633`, `1636`, `1638`, `1639`, `1640`, `1641`, `1643`, `1645`, `1647`, `1649`, `1652`, `1655`, `1656`, `1658`, `1660`, `1662`, `1665`, `1667`, `1669`, `1670`, `1671`, `1673`, `1674`, `1677`, `1678`, `1679`, `1680`, `1683`, `1686`, `1688`, `1689`, `1691`, `1693`, `1694`, `1696`, `1698`, `1699`, `1703`, `1704`, `1707`, `1708`, `1710`, `1712`, `1714`, `1716`, `1718`, `1720`, `1722`, `1724`, `1725`, `1726`, `1727`, `1729`, `1730`, `1731`, `1733`, `1734`, `1736`, `1737`, `1740`, `1741`, `1743`, `1744`, `1746`, `1747`, `1749`, `1750`, `1751`, `1752`, `1754`, `1755`, `1757`, `1758`, `1760`, `1762`, `1764`, `1766`, `1767`, `1769`, `1771`, `1774`, `1777`, `1779`, `1780`, `1781`, `1783`, `1785`, `1786`, `1789`, `1790`, `1793`, `1796`, `1799`, `1800`, `1802`, `1804`, `1805`, `1807`, `1809`, `1810`, `1813`, `1815`, `1817`, `1819`, `1822`, `1823`, `1825`, `1826`, `1827`, `1829`, `1830`, `1833`, `1835`, `1837`, `1840`, `1843`, `1844`, `1846`, `1848`, `1850`, `1853`, `1854`, `1855`, `1857`, `1859`, `1863`, `1865`, `1867`, `1870`, `1872`, `1873`, `1874`, `1875`, `1876`, `1878`, `1879`, `1880`, `1882`, `1884`, `1885`, `1888`, `1889`, `1892`, `1893`, `1895`, `1896`, `1897`, `1898`, `1899`, `1901`, `1903`, `1905`, `1907`, `1909`, `1911`, `1913`, `1915`, `1916`, `1918`, `1919`, `1921`, `1923`, `1925`, `1928`, `1931`, `1933`, `1935`, `1936`, `1938`, `1940`, `1943`, `1945`, `1946`, `1948`, `1951`, `1954`, `1956`, `1957`, `1958`, `1960`, `1962`, `1963`, `1965`, `1967`, `1969`, `1971`, `1973`, `1976`, `1977`, `1979`, `1981`, `1984`, `1986`, `1988`, `1989`, `1991`, `1994`, `1996`, `1999`, `2000`, `2001`, `2003`, `2004`, `2006`, `2008`, `2010`, `2011`, `2016`, `2017`, `2019`, `2020`, `2022`, `2023`, `2024`, `2025`, `2026`, `2027`, `2029`, `2031`, `2033`, `2034`, `2035`, `2036`, `2038`, `2041`, `2042`, `2043`, `2045`, `2047`, `2048`, `2049`, `2051`, `2053`, `2055`, `2057`, `2060`, `2063`, `2064`, `2066`, `2067`, `2068`, `2070`, `2071`, `2072`, `2073`, `2074`, `2075`, `2076`, `2079`, `2080`, `2082`, `2083`, `2084`, `2085`, `2086`, `2087`, `2089`, `2092`, `2094`, `2095`, `2098`, `2100`, `2102`, `2104`, `2105`, `2107`, `2109`, `2110`, `2112`, `2115`, `2117`, `2119`, `2120`, `2121`, `2123`, `2124`, `1482`, `2125`, `2127`, `2129`, `2132`, `2134`, `2137`, `2139`, `2140`, `2143`, `2146`, `2147`, `2148`, `2149`, `2150`, `2152`, `2154`, `2156`, `2157`, `2158`, `2159`, `2160`, `2161`, `2162`, `2164`, `2166`, `2168`, `2169`, `2170`, `2171`, `2173`, `2174`, `2177`, `2178`, `2180`, `2182`, `2183`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.06 |
| `TOKEN_P` | 99.06 |
| `TOKEN_R` | 99.06 |
| `TOKEN_ACC` | 99.77 |
| `SENTS_F` | 97.00 |
| `SENTS_P` | 97.32 |
| `SENTS_R` | 96.67 |
| `TAG_ACC` | 93.85 |
| `POS_ACC` | 97.66 |
| `MORPH_ACC` | 93.64 |
| `DEP_UAS` | 92.56 |
| `DEP_LAS` | 87.49 |
| `LEMMA_ACC` | 93.99 |
|
{"language": ["ro"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/ro_udv25_romaniannonstandard_trf
|
[
"spacy",
"token-classification",
"ro",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ro"
] |
TAGS
#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Romanian-Nonstandard
### Label Scheme
View label scheme (7445 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (7445 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (7445 labels for 6 components)",
"### Accuracy"
] |
[
32,
18,
5
] |
[
"passage: TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (7445 labels for 6 components)### Accuracy"
] |
[
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0.0873875766992569,
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Romanian-RRT
| Feature | Description |
| --- | --- |
| **Name** | `ro_udv25_romanianrrt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (3096 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ARROW`, `Af`, `Afcfp-n`, `Afcfson`, `Afcfsrn`, `Afcmpoy`, `Afcms-n`, `Afp`, `Afp-p-n`, `Afp-poy`, `Afpf--n`, `Afpfp-n`, `Afpfp-ny`, `Afpfpoy`, `Afpfpry`, `Afpfson`, `Afpfsoy`, `Afpfsrn`, `Afpfsry`, `Afpm--n`, `Afpmp-n`, `Afpmpoy`, `Afpmpry`, `Afpms-n`, `Afpmsoy`, `Afpmsry`, `Afsfp-n`, `Afsfsrn`, `BULLET`, `COLON`, `COMMA`, `Ccssp`, `Ccsspy`, `Crssp`, `Csssp`, `Cssspy`, `DASH`, `DBLQ`, `Dd3-po---e`, `Dd3-po---o`, `Dd3fpo`, `Dd3fpr`, `Dd3fpr---e`, `Dd3fpr---o`, `Dd3fpr--y`, `Dd3fso`, `Dd3fso---e`, `Dd3fsr`, `Dd3fsr---e`, `Dd3fsr---o`, `Dd3fsr--yo`, `Dd3mpo`, `Dd3mpr`, `Dd3mpr---e`, `Dd3mpr---o`, `Dd3mso---e`, `Dd3msr`, `Dd3msr---e`, `Dd3msr---o`, `Dh1ms`, `Dh3fp`, `Dh3fso`, `Dh3fsr`, `Dh3mp`, `Dh3ms`, `Di3`, `Di3-----y`, `Di3--r---e`, `Di3-po`, `Di3-po---e`, `Di3-sr`, `Di3-sr---e`, `Di3-sr--y`, `Di3fp`, `Di3fpr`, `Di3fpr---e`, `Di3fso`, `Di3fso---e`, `Di3fsr`, `Di3fsr---e`, `Di3mp`, `Di3mpr`, `Di3mpr---e`, `Di3ms`, `Di3ms----e`, `Di3mso---e`, `Di3msr`, `Di3msr---e`, `Ds1fp-p`, `Ds1fp-s`, `Ds1fsop`, `Ds1fsos`, `Ds1fsrp`, `Ds1fsrs`, `Ds1fsrs-y`, `Ds1mp-p`, `Ds1mp-s`, `Ds1ms-p`, `Ds1ms-s`, `Ds1msrs-y`, `Ds2---s`, `Ds2fp-p`, `Ds2fp-s`, `Ds2fsrp`, `Ds2fsrs`, `Ds2mp-p`, `Ds2mp-s`, `Ds2ms-p`, `Ds2ms-s`, `Ds3---p`, `Ds3---s`, `Ds3fp-s`, `Ds3fsos`, `Ds3fsrs`, `Ds3mp-s`, `Ds3ms-s`, `Dw3--r---e`, `Dw3-po---e`, `Dw3fpr`, `Dw3fso---e`, `Dw3fsr`, `Dw3mpr`, `Dw3mso---e`, `Dw3msr`, `Dz3fsr---e`, `Dz3mso---e`, `Dz3msr---e`, `EQUAL`, `EXCL`, `EXCLHELLIP`, `GE`, `GT`, `HELLIP`, `I`, `LCURL`, `LPAR`, `LSQR`, `LT`, `M`, `Mc`, `Mc-p-d`, `Mc-p-l`, `Mcfp-l`, `Mcfp-ln`, `Mcfprln`, `Mcfprly`, `Mcfsoln`, `Mcfsrln`, `Mcmp-l`, `Mcms-ln`, `Mcmsrl`, `Mcmsrly`, `Mffprln`, `Mffsrln`, `Mlfpo`, `Mlfpr`, `Mlmpr`, `Mo---l`, `Mo---ln`, `Mo-s-r`, `Mofp-ln`, `Mofpoly`, `Mofprly`, `Mofs-l`, `Mofsoln`, `Mofsoly`, `Mofsrln`, `Mofsrly`, `Mompoly`, `Momprly`, `Moms-l`, `Moms-ln`, `Momsoly`, `Momsrly`, `Nc`, `Nc---n`, `Ncf--n`, `Ncfp-n`, `Ncfpoy`, `Ncfpry`, `Ncfs-n`, `Ncfson`, `Ncfsoy`, `Ncfsrn`, `Ncfsry`, `Ncfsryy`, `Ncfsvy`, `Ncm--n`, `Ncmp-n`, `Ncmpoy`, `Ncmpry`, `Ncms-n`, `Ncms-ny`, `Ncms-y`, `Ncmsoy`, `Ncmsrn`, `Ncmsry`, `Ncmsryy`, `Ncmsvn`, `Ncmsvy`, `Np`, `Npfson`, `Npfsoy`, `Npfsrn`, `Npfsry`, `Npmpoy`, `Npmpry`, `Npms-n`, `Npmsoy`, `Npmsry`, `PERCENT`, `PERIOD`, `PLUS`, `PLUSMINUS`, `Pd3-po`, `Pd3fpr`, `Pd3fso`, `Pd3fsr`, `Pd3mpo`, `Pd3mpr`, `Pd3mpr--y`, `Pd3mso`, `Pd3msr`, `Pi3`, `Pi3--r`, `Pi3-po`, `Pi3-so`, `Pi3-sr`, `Pi3fpr`, `Pi3fso`, `Pi3fsr`, `Pi3mpr`, `Pi3mso`, `Pi3msr`, `Pi3msr--y`, `Pp1-pa--------w`, `Pp1-pa--y-----w`, `Pp1-pd--------s`, `Pp1-pd--------w`, `Pp1-pd--y-----w`, `Pp1-pr--------s`, `Pp1-sa--------s`, `Pp1-sa--------w`, `Pp1-sa--y-----w`, `Pp1-sd--------s`, `Pp1-sd--------w`, `Pp1-sd--y-----w`, `Pp1-sn--------s`, `Pp2-----------s`, `Pp2-pa--------w`, `Pp2-pa--y-----w`, `Pp2-pd--------w`, `Pp2-pd--y-----w`, `Pp2-pr--------s`, `Pp2-sa--------s`, `Pp2-sa--------w`, `Pp2-sa--y-----w`, `Pp2-sd--------s`, `Pp2-sd--------w`, `Pp2-sd--y-----w`, `Pp2-sn--------s`, `Pp2-so--------s`, `Pp2-sr--------s`, `Pp3-p---------s`, `Pp3-pd--------w`, `Pp3-pd--y-----w`, `Pp3-po--------s`, `Pp3-sd--------w`, `Pp3-sd--y-----w`, `Pp3fpa--------w`, `Pp3fpa--y-----w`, `Pp3fpr--------s`, `Pp3fs---------s`, `Pp3fsa--------w`, `Pp3fsa--y-----w`, `Pp3fso--------s`, `Pp3fsr--------s`, `Pp3fsr--y-----s`, `Pp3mpa--------w`, `Pp3mpa--y-----w`, `Pp3mpr--------s`, `Pp3ms---------s`, `Pp3msa--------w`, `Pp3msa--y-----w`, `Pp3mso--------s`, `Pp3msr--------s`, `Pp3msr--y-----s`, `Ps1fp-s`, `Ps1fsrp`, `Ps1fsrs`, `Ps1mp-p`, `Ps1ms-p`, `Ps2fp-s`, `Ps2fsrp`, `Ps2fsrs`, `Ps2ms-s`, `Ps3---p`, `Ps3---s`, `Ps3fp-s`, `Ps3fsrs`, `Ps3mp-s`, `Ps3ms-s`, `Pw3--r`, `Pw3-po`, `Pw3-so`, `Pw3fpr`, `Pw3fso`, `Pw3mpr`, `Pw3mso`, `Px3--a--------s`, `Px3--a--------w`, `Px3--a--y-----w`, `Px3--d--------w`, `Px3--d--y-----w`, `Pz3-sr`, `Pz3fsr`, `QUEST`, `QUOT`, `Qf`, `Qn`, `Qs`, `Qs-y`, `Qz`, `Qz-y`, `RCURL`, `RPAR`, `RSQR`, `Rc`, `Rgc`, `Rgp`, `Rgpy`, `Rgs`, `Rp`, `Rw`, `Rw-y`, `Rz`, `SCOLON`, `SLASH`, `STAR`, `Sp`, `Spsa`, `Spsay`, `Spsd`, `Spsg`, `Td-po`, `Tdfpr`, `Tdfso`, `Tdfsr`, `Tdmpr`, `Tdmso`, `Tdmsr`, `Tf-so`, `Tffpoy`, `Tffpry`, `Tffs-y`, `Tfmpoy`, `Tfms-y`, `Tfmsoy`, `Tfmsry`, `Ti-po`, `Tifp-y`, `Tifso`, `Tifsr`, `Timso`, `Timsr`, `Tsfp`, `Tsfs`, `Tsmp`, `Tsms`, `UNDERSC`, `Va--1`, `Va--1-----y`, `Va--1p`, `Va--1s`, `Va--1s----y`, `Va--2p`, `Va--2p----y`, `Va--2s`, `Va--2s----y`, `Va--3`, `Va--3-----y`, `Va--3p`, `Va--3p----y`, `Va--3s`, `Va--3s----y`, `Vag`, `Vaii1`, `Vaii2s`, `Vaii3p`, `Vaii3s`, `Vail3p`, `Vail3s`, `Vaip1p`, `Vaip1s`, `Vaip2p`, `Vaip2s`, `Vaip3p`, `Vaip3p----y`, `Vaip3s`, `Vaip3s----y`, `Vais3p`, `Vais3s`, `Vam-2s`, `Vanp`, `Vap--sm`, `Vasp1p`, `Vasp1s`, `Vasp2p`, `Vasp2s`, `Vasp3`, `Vmg`, `Vmg-------y`, `Vmii1`, `Vmii1-----y`, `Vmii2p`, `Vmii2s`, `Vmii3p`, `Vmii3p----y`, `Vmii3s`, `Vmii3s----y`, `Vmil1`, `Vmil1p`, `Vmil2s`, `Vmil3p`, `Vmil3p----y`, `Vmil3s`, `Vmil3s----y`, `Vmip1p`, `Vmip1p----y`, `Vmip1s`, `Vmip1s----y`, `Vmip2p`, `Vmip2s`, `Vmip2s----y`, `Vmip3`, `Vmip3-----y`, `Vmip3p`, `Vmip3s`, `Vmip3s----y`, `Vmis1p`, `Vmis1s`, `Vmis3p`, `Vmis3p----y`, `Vmis3s`, `Vmis3s----y`, `Vmm-2p`, `Vmm-2s`, `Vmnp`, `Vmnp------y`, `Vmp--pf`, `Vmp--pm`, `Vmp--sf`, `Vmp--sm`, `Vmp--sm---y`, `Vmsp1p`, `Vmsp1s`, `Vmsp2s`, `Vmsp3`, `Vmsp3-----y`, `X`, `Y`, `Ya`, `Yn`, `Ynfsoy`, `Ynfsry`, `Ynmsoy`, `Ynmsry`, `Yp`, `Yp-sr`, `Yr` |
| **`morphologizer`** | `Case=Dat,Gen\|Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `AdpType=Prep\|Case=Acc\|POS=ADP`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADV\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `POS=PUNCT`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=CCONJ\|Polarity=Pos`, `Case=Acc,Nom\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Sub\|POS=PART\|Variant=Short`, `Mood=Sub\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Weak`, `POS=AUX\|Tense=Pres\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=ADV`, `Degree=Pos\|POS=ADV`, `POS=PART\|Polarity=Neg`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|POS=PART`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `POS=SCONJ\|Polarity=Pos`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `POS=AUX\|Person=3`, `POS=VERB\|Tense=Pres\|VerbForm=Inf`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADJ`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `POS=VERB\|VerbForm=Ger`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PART\|PartType=Inf`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Weak\|Variant=Short`, `Case=Acc,Nom\|POS=DET\|Person=3\|Position=Prenom\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak\|Variant=Short`, `NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat,Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `POS=ADV\|PronType=Neg`, `AdpType=Prep\|Case=Acc\|POS=ADP\|Variant=Short`, `Case=Acc,Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Number=Sing\|POS=AUX\|Person=2`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|POS=ADJ`, `Case=Dat,Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Emp`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Sub\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `NumForm=Word\|NumType=Ord\|POS=NUM`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `AdpType=Prep\|POS=PUNCT`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Weak`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `POS=VERB\|Variant=Short\|VerbForm=Ger`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Number=Sing\|POS=AUX\|Person=3`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `POS=AUX\|Person=1`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=PART\|Polarity=Neg\|Variant=Short`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Case=Acc,Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Mood=Ind\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Weak\|Variant=Short`, `Number=Plur\|POS=AUX\|Person=3`, `Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=SCONJ\|Polarity=Pos\|Variant=Short`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=PART\|Tense=Fut`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `POS=DET\|Person=3\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Emp`, `Case=Acc,Nom\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|NumForm=Word\|NumType=Ord\|POS=NUM`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art\|Variant=Short`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Definite=Ind\|Gender=Masc\|POS=NOUN`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `NumForm=Digit\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `POS=INTJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|Variant=Short\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|Variant=Short\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|POS=ADJ`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Voc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind\|Variant=Short`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `POS=CCONJ\|Polarity=Pos\|Variant=Short`, `Number=Plur\|POS=AUX\|Person=2`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art\|Variant=Short`, `POS=AUX\|VerbForm=Ger`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Ind`, `Definite=Ind\|Gender=Fem\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN\|Variant=Short`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Degree=Sup\|POS=ADV`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `POS=ADV\|PronType=Int,Rel\|Variant=Short`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|Variant=Short\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN\|Variant=Short`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art\|Variant=Short`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|Variant=Short\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Degree=Pos\|POS=ADV\|Variant=Short`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=NOUN`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Int,Rel`, `POS=NOUN`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `AdpType=Prep\|Case=Dat\|POS=ADP`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `AdpType=Prep\|POS=SYM`, `Case=Acc,Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak\|Variant=Short`, `POS=SYM`, `POS=X`, `Abbr=Yes\|POS=X`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|POS=ADV`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Int,Rel`, `NumForm=Roman\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Voc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak\|Variant=Short`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|Case=Acc,Nom\|Number=Sing\|POS=PRON`, `Foreign=Yes\|POS=PROPN`, `Definite=Ind\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Degree=Pos\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art\|Variant=Short`, `Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Definite=Ind\|Degree=Pos\|Foreign=Yes\|Gender=Fem\|POS=ADJ`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art\|Variant=Short`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art\|Variant=Short`, `Case=Acc,Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Int,Rel`, `Foreign=Yes\|POS=X`, `Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Ind`, `Foreign=Yes\|POS=NOUN`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Emp`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Neg`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Emp`, `Definite=Ind\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|PronType=Emp`, `Abbr=Yes\|POS=PRON`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number[psor]=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=AUX\|Person=1`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Emp`, `NumType=Card\|POS=NUM`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|Variant=Short\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=AUX\|Person=3\|Variant=Short`, `Number=Plur\|POS=AUX\|Person=2\|Variant=Short`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|Variant=Short\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN\|Variant=Short`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Variant=Short`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|Variant=Short\|VerbForm=Fin`, `Number=Plur\|POS=AUX\|Person=3\|Variant=Short`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|Variant=Short\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Number=Plur\|POS=AUX\|Person=1`, `POS=VERB\|Tense=Pres\|Variant=Short\|VerbForm=Inf`, `Number=Sing\|POS=AUX\|Person=2\|Variant=Short`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem\|Variant=Short`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong\|Variant=Short`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Degree=Pos\|POS=ADV\|Polarity=Neg`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong\|Variant=Short`, `POS=AUX\|Person=3\|Variant=Short`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|Variant=Short\|VerbForm=Part`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Sub\|POS=VERB\|Person=3\|Tense=Pres\|Variant=Short\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=AUX\|Person=1\|Variant=Short`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat,Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `POS=ADV\|Polarity=Neg`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=ADJ`, `AdpType=Prep\|Case=Acc\|Foreign=Yes\|POS=ADP`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|Variant=Short\|VerbForm=Fin`, `POS=AUX\|Person=1\|Variant=Short`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `AdpType=Prep\|POS=ADP`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat,Gen\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Abbr=Yes\|Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=ADJ`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Abbr=Yes\|Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=VERB\|Tense=Pres\|VerbForm=Inf`, `Foreign=Yes\|NumForm=Roman\|NumType=Ord\|Number=Sing\|POS=NUM`, `Definite=Ind\|Foreign=Yes\|Gender=Masc\|POS=NOUN`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=1\|Tense=Imp\|Variant=Short\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|Variant=Short\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|Variant=Short\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Definite=Def\|Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|Variant=Short\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|Variant=Short\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind\|Variant=Short`, `Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art\|Variant=Short`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advcl:tcl`, `advmod`, `advmod:tmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `ccomp:pmod`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `expl:impers`, `expl:pass`, `expl:poss`, `expl:pv`, `fixed`, `flat`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nmod:agent`, `nmod:pmod`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `3`, `7`, `9`, `12`, `14`, `15`, `19`, `22`, `24`, `26`, `30`, `32`, `34`, `36`, `38`, `40`, `42`, `45`, `47`, `49`, `51`, `53`, `55`, `61`, `62`, `66`, `67`, `68`, `71`, `73`, `76`, `78`, `80`, `83`, `85`, `86`, `89`, `91`, `92`, `93`, `95`, `97`, `98`, `99`, `102`, `104`, `106`, `108`, `109`, `111`, `107`, `113`, `115`, `116`, `119`, `121`, `124`, `128`, `129`, `130`, `132`, `135`, `139`, `143`, `146`, `148`, `150`, `151`, `154`, `156`, `158`, `159`, `162`, `165`, `166`, `167`, `169`, `171`, `173`, `175`, `177`, `180`, `182`, `183`, `185`, `186`, `187`, `189`, `191`, `193`, `195`, `197`, `198`, `199`, `201`, `203`, `205`, `207`, `208`, `210`, `212`, `215`, `217`, `218`, `221`, `223`, `227`, `229`, `230`, `231`, `232`, `233`, `234`, `237`, `239`, `240`, `242`, `244`, `246`, `248`, `249`, `251`, `252`, `254`, `257`, `259`, `261`, `263`, `266`, `268`, `269`, `271`, `272`, `274`, `276`, `278`, `280`, `282`, `283`, `285`, `287`, `289`, `293`, `294`, `296`, `298`, `300`, `301`, `303`, `305`, `307`, `309`, `311`, `313`, `315`, `317`, `318`, `320`, `322`, `324`, `326`, `328`, `330`, `331`, `333`, `334`, `336`, `337`, `339`, `342`, `343`, `344`, `346`, `349`, `353`, `355`, `357`, `359`, `360`, `361`, `363`, `364`, `366`, `367`, `369`, `370`, `372`, `374`, `376`, `378`, `379`, `380`, `381`, `383`, `384`, `386`, `388`, `389`, `391`, `74`, `393`, `395`, `397`, `399`, `401`, `403`, `406`, `408`, `409`, `412`, `413`, `415`, `416`, `417`, `418`, `419`, `420`, `421`, `422`, `423`, `425`, `426`, `428`, `429`, `431`, `434`, `435`, `439`, `443`, `445`, `447`, `449`, `451`, `452`, `453`, `456`, `458`, `460`, `461`, `462`, `464`, `466`, `467`, `468`, `470`, `471`, `473`, `474`, `475`, `476`, `478`, `481`, `484`, `485`, `486`, `487`, `489`, `491`, `492`, `493`, `496`, `498`, `500`, `503`, `504`, `505`, `509`, `512`, `513`, `514`, `515`, `516`, `519`, `520`, `521`, `522`, `523`, `525`, `526`, `527`, `528`, `213`, `530`, `531`, `532`, `535`, `539`, `541`, `544`, `546`, `547`, `548`, `550`, `552`, `553`, `555`, `557`, `558`, `559`, `560`, `563`, `565`, `566`, `569`, `572`, `574`, `576`, `578`, `580`, `582`, `585`, `588`, `589`, `590`, `591`, `592`, `593`, `594`, `597`, `599`, `601`, `603`, `605`, `606`, `608`, `610`, `614`, `616`, `617`, `618`, `620`, `624`, `626`, `41`, `628`, `629`, `631`, `632`, `634`, `636`, `639`, `641`, `643`, `645`, `647`, `650`, `653`, `654`, `655`, `657`, `658`, `661`, `664`, `665`, `667`, `669`, `671`, `672`, `674`, `675`, `677`, `678`, `680`, `682`, `683`, `686`, `688`, `690`, `693`, `695`, `697`, `699`, `701`, `702`, `703`, `705`, `706`, `707`, `708`, `711`, `713`, `714`, `715`, `717`, `719`, `721`, `722`, `725`, `726`, `728`, `731`, `733`, `735`, `736`, `737`, `738`, `740`, `742`, `744`, `745`, `747`, `749`, `750`, `751`, `752`, `754`, `757`, `759`, `761`, `762`, `764`, `765`, `766`, `768`, `769`, `770`, `771`, `772`, `774`, `775`, `776`, `779`, `781`, `784`, `785`, `787`, `789`, `791`, `792`, `794`, `796`, `797`, `799`, `800`, `802`, `803`, `808`, `809`, `810`, `813`, `816`, `817`, `818`, `820`, `821`, `822`, `824`, `826`, `827`, `828`, `830`, `832`, `834`, `836`, `837`, `839`, `841`, `843`, `845`, `847`, `848`, `849`, `851`, `855`, `856`, `858`, `861`, `862`, `864`, `865`, `866`, `867`, `868`, `870`, `871`, `873`, `876`, `877`, `880`, `881`, `883`, `885`, `889`, `891`, `892`, `894`, `896`, `898`, `900`, `902`, `904`, `905`, `907`, `908`, `911`, `913`, `914`, `916`, `918`, `919`, `920`, `923`, `924`, `926`, `927`, `929`, `932`, `935`, `936`, `937`, `938`, `940`, `942`, `943`, `945`, `947`, `948`, `952`, `955`, `958`, `960`, `961`, `962`, `964`, `965`, `966`, `968`, `970`, `972`, `974`, `976`, `977`, `979`, `980`, `982`, `983`, `985`, `986`, `988`, `989`, `990`, `991`, `993`, `995`, `997`, `998`, `999`, `1001`, `1002`, `1003`, `1006`, `1007`, `1012`, `1013`, `1014`, `1015`, `1016`, `1019`, `1020`, `1021`, `1022`, `1023`, `1025`, `1027`, `1029`, `1031`, `1032`, `1033`, `1036`, `1038`, `1040`, `1043`, `1044`, `1045`, `1046`, `1048`, `1050`, `1052`, `1053`, `1055`, `1057`, `1058`, `1061`, `1062`, `1064`, `1067`, `1069`, `1071`, `1074`, `1076`, `1078`, `1080`, `1083`, `1085`, `1086`, `1089`, `1090`, `1091`, `1094`, `1097`, `1098`, `1099`, `1103`, `1104`, `1106`, `1107`, `1108`, `1109`, `1110`, `1112`, `1114`, `1117`, `1118`, `1120`, `1122`, `1124`, `1125`, `1127`, `1128`, `1129`, `1132`, `1133`, `1136`, `1138`, `1139`, `1141`, `1144`, `1145`, `1147`, `1150`, `1152`, `1154`, `1155`, `1156`, `1157`, `1159`, `1161`, `1162`, `1163`, `1165`, `1166`, `1167`, `1168`, `1169`, `1171`, `1174`, `1176`, `1178`, `1179`, `1180`, `1184`, `1186`, `1187`, `1189`, `1190`, `1192`, `1193`, `1195`, `1196`, `1198`, `1201`, `1203`, `1204`, `1207`, `1210`, `1212`, `1214`, `1215`, `1216`, `1217`, `1218`, `1219`, `1222`, `1223`, `1224`, `1226`, `1227`, `1230`, `1231`, `1232`, `1233`, `1234`, `1235`, `1236`, `1238`, `1239`, `1242`, `1243`, `1244`, `1245`, `1247`, `1249`, `1250`, `1252`, `1254`, `1255`, `1256`, `1258`, `1259`, `1261`, `1262`, `1268`, `1269`, `1270`, `1271`, `1272`, `1274`, `1275`, `1277`, `1278`, `1279`, `1281`, `1282`, `1285`, `1287`, `1288`, `1289`, `1290`, `1291`, `1292`, `1295`, `1297`, `1298`, `1299`, `1300`, `1301`, `1302`, `1303`, `1304`, `1305`, `1306`, `1307`, `1312`, `1313`, `1314`, `1316`, `1317`, `1318`, `1319`, `1320`, `1315`, `1321`, `1323`, `1324`, `1325`, `1326`, `1327`, `1329`, `1337`, `1338`, `1339`, `1343`, `1344`, `1346`, `1347`, `1350`, `1351`, `1353`, `1354`, `1355`, `1358`, `1360`, `1361`, `1362`, `1365`, `1366`, `1367`, `1368`, `1369`, `1370`, `1371`, `1372`, `1373`, `1374`, `1376`, `1377`, `1379`, `1380`, `1381`, `1382`, `1384`, `1385`, `1386`, `1387`, `1389`, `1390`, `1391`, `1392`, `1393`, `1394`, `1395`, `1396`, `1400`, `1401`, `1404`, `1405`, `1406`, `1409`, `1410`, `1411`, `1413`, `1414`, `1416`, `1417`, `1418`, `1419`, `1421`, `1424`, `1425`, `1426`, `1427`, `1428`, `1430`, `1431`, `1434`, `1435`, `1436`, `1438`, `1440`, `1442`, `1443`, `1444`, `1445`, `1448`, `1449`, `1450`, `1451`, `1453`, `1454`, `1455`, `1456`, `1458`, `1459`, `1460`, `1463`, `1464`, `1466`, `1467`, `1468`, `1469`, `1470`, `1471`, `1472`, `1473`, `1474`, `1475`, `1476`, `1479`, `1480`, `1482`, `1483`, `1485`, `1487`, `1488`, `1490`, `1491`, `1492`, `1493`, `1495`, `1501`, `1504`, `1506`, `1508`, `1510`, `1512`, `1513`, `1514`, `1515`, `1516`, `1517`, `1518`, `1519`, `1520`, `1523`, `1524`, `1527`, `1530`, `1532`, `1533`, `1534`, `1536`, `1537`, `1538`, `1539`, `1540`, `1542`, `1544`, `1545`, `1546`, `1547`, `1548`, `1549`, `1550`, `1552`, `1554`, `1555`, `1556`, `1557`, `1558`, `1560`, `1563`, `1564`, `1565`, `1566`, `1567`, `1568`, `1569`, `1570`, `1572`, `1573`, `1575`, `1577`, `1578`, `1579`, `1582`, `1584`, `1585`, `1586`, `1587`, `1588`, `1589`, `1590`, `1591`, `1593`, `1594`, `1595`, `1597`, `1598`, `1600`, `1601`, `1602`, `1604`, `1605`, `1606`, `1607`, `1608`, `1610`, `1611`, `1612`, `1616`, `1617`, `1618`, `1619`, `1620`, `1621`, `1622`, `1623`, `1627`, `1628`, `1629`, `1631`, `1639`, `1641`, `1642`, `1643`, `1649`, `1650`, `1652`, `1653`, `1654`, `1656`, `1657`, `1659`, `1660`, `1661`, `1663`, `1667`, `1668`, `1669`, `1670`, `1671`, `1673`, `1675`, `1676`, `1678`, `1679`, `1681`, `1682`, `1684`, `1685`, `1686`, `1687`, `1688`, `1689`, `1690`, `1691`, `1692`, `1694`, `1695`, `1696`, `1697`, `1698`, `1700`, `1702`, `1703`, `1705`, `1706`, `1707`, `1708`, `1709`, `1710`, `1712`, `1713`, `1717`, `1718`, `1719`, `1720`, `1721`, `1725`, `1726`, `1728`, `1729`, `1730`, `1731`, `1733`, `1734`, `1735`, `1738`, `1740`, `1741`, `1742`, `1743`, `1744`, `1747`, `1749`, `1751`, `1754`, `1756`, `1757`, `1758`, `1760`, `1761`, `1762`, `1765`, `1768`, `1771`, `1772`, `1774`, `1775`, `1776`, `1777`, `1778`, `1779`, `1780`, `1781`, `1782`, `1783`, `1785`, `1787`, `1788`, `1790`, `1793`, `1794`, `1795`, `1798`, `1800`, `1801`, `1802`, `1803`, `1805`, `1806`, `1807`, `1808`, `1809`, `1810`, `1816`, `1817`, `1818`, 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`1983`, `1984`, `1985`, `1986`, `1987`, `1988`, `1989`, `1990`, `1993`, `1994`, `1995`, `1996`, `1997`, `1998`, `1999`, `2001`, `2002`, `2003`, `2004`, `2005`, `2007`, `2008`, `2009`, `2010`, `2011`, `2012`, `2013`, `2016`, `2018`, `2019`, `2020`, `2021`, `2022`, `2023`, `2024`, `2025`, `2026`, `2028`, `2029`, `2031`, `2033`, `2037`, `2038`, `2039`, `2042`, `2043`, `2045`, `2046`, `2047`, `2048`, `2049`, `2050`, `2051`, `2052`, `2053`, `2055`, `2056`, `2057`, `2059`, `2063`, `2064`, `2065`, `2066`, `2067`, `2068`, `2069`, `2070`, `2071`, `2072`, `602`, `2073`, `2074`, `2075`, `2078`, `2079`, `2080`, `2082`, `2083`, `2084`, `2085`, `2086`, `2087`, `2088`, `2089`, `2090`, `2091`, `2092`, `2093`, `2094`, `2096`, `2098`, `2099`, `2100`, `2101`, `2102`, `2103`, `2105`, `2106`, `2107`, `2108`, `2109`, `2110`, `2112`, `2113`, `2115`, `2116`, `2117`, `2118`, `2119`, `2123`, `2125`, `2126`, `2127`, `2128`, `2130`, `2131`, `2132`, `2133`, `2134`, `2135`, `2136`, `2139`, `2140`, `2141`, `2142`, 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`3171`, `3172`, `3174`, `3175`, `3177`, `3178`, `3179`, `3180`, `3181`, `3182`, `3183`, `3184`, `3185`, `3186`, `3187`, `3188`, `3190`, `3191`, `3193`, `3194`, `3195`, `3196`, `3197`, `3198`, `3199`, `3200`, `3202`, `3204`, `3205`, `3206`, `3207`, `3208`, `3209`, `3210`, `3211`, `3214`, `3215`, `3216`, `3217`, `3218`, `3219`, `3220`, `3222`, `3225`, `3226`, `3227`, `3228`, `3229`, `3231`, `3232`, `3233`, `3235`, `3238`, `3239`, `3240`, `3241`, `3242` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.79 |
| `TOKEN_P` | 99.78 |
| `TOKEN_R` | 99.80 |
| `TOKEN_ACC` | 99.96 |
| `SENTS_F` | 92.35 |
| `SENTS_P` | 94.94 |
| `SENTS_R` | 89.89 |
| `TAG_ACC` | 96.53 |
| `POS_ACC` | 97.85 |
| `MORPH_ACC` | 97.23 |
| `DEP_UAS` | 92.52 |
| `DEP_LAS` | 86.32 |
| `LEMMA_ACC` | 97.00 |
|
{"language": ["ro"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/ro_udv25_romanianrrt_trf
|
[
"spacy",
"token-classification",
"ro",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ro"
] |
TAGS
#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Romanian-RRT
### Label Scheme
View label scheme (3096 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (3096 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3096 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (3096 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Russian-GSD
| Feature | Description |
| --- | --- |
| **Name** | `ru_udv25_russiangsd_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (3014 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `!`, `''`, `'`, `(`, `)`, `,`, `-`, `--`, `.`, `.,`, `/`, `:`, `AFX`, `APOSTROPHE`, `AWP`, `CC`, `CD`, `DT`, `FW`, `IN`, `JJ`, `JJH`, `JJL`, `JJR`, `JJRL`, `JJS`, `NEG`, `NFP`, `NN`, `NNP`, `ORD`, `PRED`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `UH`, `VB`, `VBC`, `VBG`, `VBNH`, `VBNL`, `WDT`, `WP`, `WRB`, `X`, ```` |
| **`morphologizer`** | `POS=ADP`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=CCONJ`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Variant=Short`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf\|Voice=Mid`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Nom\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Card\|POS=NUM`, `Case=Nom\|NumType=Card\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET`, `POS=PART`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=ADV`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Pos\|POS=ADV`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|NumType=Card\|POS=NUM`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Conv\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=NUM`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Number=Plur\|POS=ADJ\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Mid`, `Case=Loc\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Number=Plur\|POS=PRON`, `POS=SYM`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Number=Plur\|POS=DET`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET`, `Foreign=Yes\|POS=X`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Anim\|Case=Nom\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Acc\|NumType=Card\|POS=NUM`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=PRON`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=DET`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Number=Plur\|POS=DET`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Variant=Short`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Anim\|Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET`, `Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Variant=Short`, `Degree=Cmp\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Case=Nom\|Number=Plur\|POS=PRON`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Degree=Pos\|POS=VERB`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Ins\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Ins\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Variant=Short`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Number=Plur\|POS=PRON`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Dat\|NumType=Card\|POS=NUM`, `POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Variant=Short`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `POS=X`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Abbr=Yes\|POS=PROPN`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Degree=Sup\|POS=ADV`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2`, `Case=Dat\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Foreign=Yes\|POS=NOUN`, `POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1`, `Animacy=Inan\|Case=Acc\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|POS=AUX\|VerbForm=Inf`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Number=Plur\|POS=PRON`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON`, `Aspect=Imp\|POS=AUX\|VerbForm=Conv`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=AUX`, `Case=Dat\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Number=Plur\|POS=PRON`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Dat\|Number=Plur\|POS=PRON`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|NumType=Card\|POS=NUM`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=3`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Dat\|Number=Plur\|POS=PRON`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Dat\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|POS=VERB\|VerbForm=Conv`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=ADV`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PART`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Loc\|NumType=Card\|POS=NUM`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ\|Variant=Short`, `Animacy=Anim\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Anim\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|NumType=Card\|POS=NUM`, `Case=Gen\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|Variant=Short\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Number=Plur\|POS=PRON`, `Animacy=Inan\|Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Loc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Nom\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Aspect=Perf\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Ins\|Number=Plur\|POS=PRON`, `Animacy=Anim\|Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Foreign=Yes\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Degree=Cmp\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Nom\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1`, `Animacy=Inan\|Case=Par\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|NumType=Card\|POS=SYM`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|NumType=Card\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=2`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=DET` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `dep`, `det`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `nummod:entity`, `nummod:gov`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `6`, `8`, `10`, `12`, `14`, `16`, `19`, `21`, `23`, `27`, `29`, `31`, `35`, `37`, `39`, `42`, `45`, `49`, `50`, `53`, `55`, `59`, `61`, `62`, `64`, `66`, `68`, `70`, `72`, `75`, `77`, `78`, `81`, `83`, `85`, `87`, `89`, `91`, `94`, `97`, `99`, `101`, `105`, `106`, `107`, `109`, `110`, `112`, `114`, `116`, `118`, `119`, `121`, `123`, `126`, `128`, `130`, `132`, `133`, `135`, `137`, `139`, `0`, `141`, `145`, `147`, `148`, `150`, `152`, `154`, `156`, `158`, `160`, `162`, `166`, `168`, `169`, `171`, `173`, `175`, `177`, `179`, `181`, `182`, `184`, `186`, `188`, `189`, `192`, `193`, `194`, `195`, `197`, `198`, `199`, `202`, `204`, `205`, `206`, `207`, `208`, `210`, `211`, `213`, `216`, `217`, `219`, `221`, `223`, `224`, `226`, `228`, `229`, `231`, `233`, `234`, `237`, `239`, `241`, `242`, `244`, `245`, `247`, `249`, `251`, `253`, `256`, `257`, `260`, `262`, `264`, `266`, `268`, `270`, `272`, `275`, `277`, `279`, `283`, `287`, `289`, `290`, `293`, `294`, `296`, `298`, `300`, `302`, `305`, `307`, `310`, `313`, `315`, `317`, `319`, `322`, `324`, `326`, `328`, `330`, `332`, `335`, `337`, `339`, `340`, `341`, `345`, `346`, `348`, `350`, `353`, `355`, `357`, `360`, `362`, `364`, `366`, `368`, `370`, `372`, `374`, `376`, `378`, `380`, `381`, `384`, `386`, `388`, `391`, `393`, `395`, `397`, `398`, `400`, `401`, `402`, `404`, `408`, `409`, `410`, `412`, `413`, `415`, `416`, `418`, `420`, `421`, `423`, `424`, `426`, `428`, `430`, `432`, `434`, `436`, `438`, `439`, `441`, `443`, `446`, `449`, `453`, `455`, `457`, `248`, `459`, `460`, `462`, `464`, `465`, `467`, `470`, `472`, `474`, `477`, `479`, `480`, `482`, `484`, `485`, `486`, `489`, `491`, `493`, `496`, `498`, `500`, `502`, `504`, `505`, `506`, `508`, `509`, `512`, `513`, `515`, `517`, `520`, `522`, `524`, `525`, `527`, `529`, `531`, `532`, `533`, `535`, `536`, `540`, `542`, `544`, `546`, `548`, `549`, `551`, `552`, `555`, `276`, `556`, `557`, `559`, `560`, `562`, `564`, `565`, `567`, `569`, `570`, `571`, `572`, `574`, `575`, `577`, `578`, `580`, `582`, `584`, `586`, `589`, `591`, `593`, `595`, `597`, `599`, `601`, `602`, `172`, `604`, `605`, `606`, `608`, `610`, `611`, `612`, `614`, `615`, `76`, `617`, `618`, `619`, `621`, `117`, `623`, `624`, `626`, `628`, `629`, `631`, `635`, `637`, `638`, `639`, `641`, `642`, `644`, `645`, `647`, `648`, `650`, `652`, `654`, `656`, `658`, `659`, `661`, `663`, `665`, `666`, `668`, `669`, `671`, `675`, `677`, `678`, `679`, `681`, `682`, `683`, `686`, `687`, `689`, `691`, `693`, `695`, `697`, `699`, `701`, `22`, `703`, `705`, `707`, `710`, `714`, `716`, `718`, `720`, `723`, `725`, `727`, `729`, `731`, `732`, `734`, `737`, `739`, `740`, `743`, `745`, `747`, `748`, `751`, `753`, `754`, `757`, `758`, `760`, `762`, `764`, `766`, `768`, `770`, `772`, `773`, `775`, `776`, `778`, `779`, `780`, `781`, `782`, `783`, `785`, `787`, `789`, `791`, `793`, `794`, `796`, `797`, `800`, `801`, `802`, `803`, `804`, `806`, `807`, `808`, `809`, `810`, `812`, `816`, `818`, `819`, `821`, `823`, `825`, `826`, `827`, `829`, `833`, `834`, `835`, `836`, `838`, `842`, `843`, `844`, `846`, `848`, `849`, `850`, `852`, `854`, `856`, `858`, `860`, `862`, `864`, `866`, `867`, `868`, `870`, `871`, `873`, `874`, `875`, `878`, `880`, `881`, `883`, `887`, `889`, `890`, `891`, `894`, `895`, `896`, `898`, `900`, `902`, `903`, `904`, `907`, `909`, `910`, `911`, `912`, `914`, `916`, `917`, `918`, `919`, `920`, `924`, `925`, `927`, `928`, `931`, `933`, `934`, `936`, `937`, `935`, `938`, `939`, `942`, `944`, `946`, `948`, `949`, `950`, `951`, `953`, `954`, `956`, `958`, `959`, `960`, `962`, `964`, `966`, `968`, `970`, `972`, `974`, `976`, `978`, `980`, `981`, `982`, `984`, `985`, `987`, `988`, `989`, `990`, `991`, `992`, `993`, `995`, `996`, `997`, `998`, `1000`, `1001`, `1002`, `1004`, `1006`, `1008`, `1010`, `1012`, `1013`, `1016`, `1018`, `1019`, `1021`, `1023`, `1024`, `1025`, `1028`, `1030`, `1031`, `1033`, `1034`, `1036`, `1038`, `1039`, `1040`, `1041`, `1043`, `1045`, `1046`, `1048`, `1052`, `1054`, `1055`, `1056`, `1057`, `1062`, `1064`, `1065`, `1067`, `1069`, `1070`, `1072`, `1073`, `1074`, `1075`, `1076`, `1078`, `1080`, `1081`, `1083`, `1085`, `1087`, `1088`, `1089`, `1091`, `1092`, `1093`, `1094`, `1095`, `1096`, `1097`, `1098`, `1100`, `1102`, `1104`, `1106`, `1108`, `1109`, `1110`, `1111`, `1112`, `1113`, `1116`, `1117`, `1119`, `1121`, `1123`, `1124`, `1125`, `1127`, `1129`, `1132`, `1134`, `1135`, `1138`, `1139`, `1141`, `1143`, `1144`, `1145`, `1146`, `1147`, `1149`, `1152`, `1153`, `1155`, `1156`, `1157`, `1159`, `1161`, `1163`, `1165`, `1166`, `1168`, `1169`, `1172`, `1174`, `1176`, `1177`, `1179`, `1183`, `1184`, `1185`, `1186`, `1188`, `1190`, `1193`, `1195`, `1196`, `1200`, `1203`, `1204`, `1206`, `1207`, `1208`, `1209`, `1211`, `1212`, `1214`, `1216`, `1217`, `1218`, `1219`, `1221`, `1223`, `1224`, `1225`, `1227`, `1228`, `1230`, `1232`, `1234`, `1237`, `1238`, `1239`, `1241`, `1243`, `1244`, `1246`, `1248`, `1249`, `1251`, `1252`, `1255`, `1257`, `1259`, `1261`, `1262`, `1263`, `1265`, `1267`, `1268`, `1269`, `1273`, `1275`, `1277`, `1279`, `1281`, `1283`, `1285`, `1287`, `1289`, `1291`, `1293`, `1295`, `1297`, `1299`, `1302`, `1305`, `1306`, `1309`, `1311`, `1312`, `1313`, `1314`, `1315`, `1317`, `1319`, `1321`, `1322`, `1325`, `1326`, `1328`, `1330`, `1331`, `1333`, `325`, `1334`, `1336`, `1338`, `1339`, `1341`, `1343`, `1346`, `1347`, `1348`, `1349`, `1350`, `1352`, `1353`, `1354`, `1355`, `1357`, `1358`, `1359`, `1361`, `1363`, `1365`, `1368`, `1370`, `1371`, `1372`, `1374`, `1376`, `1377`, `1378`, `1380`, `1382`, `1384`, `1385`, `1386`, `1388`, `1389`, `1391`, `1393`, `1395`, `1396`, `1398`, `1399`, `1402`, `1404`, `1405`, `1120`, `1406`, `1408`, `1409`, `1410`, `1412`, `1413`, `1414`, `1415`, `1417`, `1419`, `1421`, `1423`, `1425`, `1426`, `1427`, `1429`, `1431`, `1433`, `1434`, `1436`, `1438`, `1439`, `1441`, `1443`, `1444`, `1445`, `1447`, `1448`, `1449`, `1450`, `1451`, `1452`, `1454`, `1457`, `1458`, `1459`, `1461`, `1463`, `1465`, `1467`, `1468`, `1469`, `1470`, `1472`, `1475`, `1477`, `1479`, `1480`, `1481`, `1483`, `1484`, `1487`, `1489`, `1491`, `1492`, `1493`, `1496`, `1497`, `1499`, `1501`, `1502`, `1504`, `1506`, `1507`, `1508`, `1509`, `1511`, `1513`, `1515`, `1516`, `1517`, `1518`, `1519`, `1521`, `1522`, `1523`, `1525`, `1527`, `1529`, `1531`, `1532`, `1534`, `1535`, `1536`, `1537`, `1539`, `1541`, `1543`, `1545`, `1546`, `1548`, `1549`, `1550`, `1551`, `1552`, `1553`, `1555`, `1557`, `1558`, `1559`, `1560`, `1562`, `1564`, `1566`, `1567`, `1569`, `1571`, `1573`, `1575`, `1576`, `1578`, `1580`, `1581`, `1582`, `1583`, `1584`, `1585`, `1586`, `1588`, `1590`, `1592`, `1593`, `1595`, `1599`, `1601`, `1602`, `1604`, `1606`, `1610`, `1611`, `1613`, `1614`, `1616`, `1617`, `1618`, `1619`, `1621`, `1623`, `1624`, `1626`, `1628`, `1629`, `1631`, `1632`, `1634`, `1635`, `1636`, `1637`, `1638`, `1640`, `1642`, `1644`, `1646`, `1647`, `1649`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.49 |
| `TOKEN_P` | 99.48 |
| `TOKEN_R` | 99.50 |
| `TOKEN_ACC` | 99.94 |
| `SENTS_F` | 96.05 |
| `SENTS_P` | 95.56 |
| `SENTS_R` | 96.55 |
| `TAG_ACC` | 96.91 |
| `POS_ACC` | 98.25 |
| `MORPH_ACC` | 94.72 |
| `DEP_UAS` | 92.10 |
| `DEP_LAS` | 88.72 |
| `LEMMA_ACC` | 94.45 |
|
{"language": ["ru"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/ru_udv25_russiangsd_trf
|
[
"spacy",
"token-classification",
"ru",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#spacy #token-classification #ru #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Russian-GSD
### Label Scheme
View label scheme (3014 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (3014 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #ru #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3014 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #ru #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (3014 labels for 6 components)### Accuracy"
] |
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] |
null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Slovak-SNK
| Feature | Description |
| --- | --- |
| **Name** | `sk_udv25_slovaksnk_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (4879 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `#`, `%`, `0`, `?`, `AAfp1x`, `AAfp1x:q`, `AAfp1x:r`, `AAfp1y`, `AAfp1z`, `AAfp2x`, `AAfp2x:r`, `AAfp2y`, `AAfp2z`, `AAfp3x`, `AAfp4x`, `AAfp4y`, `AAfp6x`, `AAfp6x:q`, `AAfp6y`, `AAfp7x`, `AAfs1:r`, `AAfs1x`, `AAfs1x:q`, `AAfs1x:r`, `AAfs1y`, `AAfs1z`, `AAfs2x`, `AAfs2x:q`, `AAfs2x:r`, `AAfs2z`, `AAfs2z:r`, `AAfs3x`, `AAfs3x:r`, `AAfs3y`, `AAfs4x`, `AAfs4x:r`, `AAfs4y`, `AAfs4z`, `AAfs6x`, `AAfs6x:r`, `AAfs6y`, `AAfs7x`, `AAfs7x:r`, `AAfs7z`, `AAip1x`, `AAip1y`, `AAip2x`, `AAip2x:r`, `AAip2y`, `AAip2z`, `AAip3x`, `AAip4x`, `AAip4y`, `AAip4z`, `AAip6x`, `AAip6x:r`, `AAip6z`, `AAip7x`, `AAis1x`, `AAis1x:r`, `AAis1y`, `AAis1z`, `AAis2`, `AAis2x`, `AAis2x:r`, `AAis2y`, `AAis2z`, `AAis3x`, `AAis3x:r`, `AAis3y`, `AAis3z`, `AAis4x`, `AAis4x:r`, `AAis4y`, `AAis4z`, `AAis6x`, `AAis6x:r`, `AAis6z`, `AAis7`, `AAis7x`, `AAis7y`, `AAis7z`, `AAmp1`, `AAmp1x`, `AAmp1y`, `AAmp1z`, `AAmp2x`, `AAmp2y`, `AAmp2z`, `AAmp3x`, `AAmp3z`, `AAmp4x`, `AAmp4y`, `AAmp6x`, `AAmp7x`, `AAmp7z`, `AAms1:r`, `AAms1x`, `AAms1x:q`, `AAms1x:r`, `AAms1y`, `AAms1z`, `AAms2x`, `AAms2x:r`, `AAms3x`, `AAms4x`, `AAms4y`, `AAms4z`, `AAms6x`, `AAms7x`, `AAms7x:r`, `AAms7z`, `AAnp1x`, `AAnp1x:r`, `AAnp1y`, `AAnp2x`, `AAnp2z`, `AAnp3x`, `AAnp4x`, `AAnp4z`, `AAnp6x`, `AAnp7x`, `AAns1x`, `AAns1x:r`, `AAns1y`, `AAns1z`, `AAns2:r`, `AAns2x`, `AAns2x:r`, `AAns2y`, `AAns3x`, `AAns3y`, `AAns4x`, `AAns4x:q`, `AAns4x:r`, `AAns4y`, `AAns4z`, `AAns6x`, `AAns6x:r`, `AAns6y`, `AAns7x`, `AAns7z`, `AAop2x`, `AFfp1x:r`, `AFfp2x`, `AFfp2x:r`, `AFfp3x`, `AFfp4:r`, `AFfp4x`, `AFfp4x:r`, `AFfs1:r`, `AFfs1x`, `AFfs1x:r`, `AFfs2x`, `AFfs2x:r`, `AFfs3x:r`, `AFfs4x`, `AFfs4x:r`, `AFfs6x`, `AFfs6x:r`, `AFfs7x`, `AFfs7x:r`, `AFip1x`, `AFip1x:r`, `AFip2x:r`, `AFip4x`, `AFip4x:r`, `AFip6x:r`, `AFip7x:r`, `AFis1x`, `AFis1x:r`, `AFis2x:r`, `AFis4x`, `AFis4x:r`, `AFis6x:r`, `AFis7x`, `AFis7x:r`, `AFmp1x:r`, `AFmp1y`, `AFmp2x`, `AFmp3x:r`, `AFmp4x:r`, `AFmp7x`, `AFms1x`, `AFms1x:r`, `AFms2x`, `AFms2x:r`, `AFms4x`, 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`NNmp7`, `NNnp1`, `NNnp2`, `NNnp4`, `NNnp6`, `NNnp7`, `NNns1`, `NNop1`, `NSfs4`, `NSip2`, `NSip4`, `NSis1`, `NSis4`, `NSnp1`, `NUfp1`, `NUfp2`, `NUfp3`, `NUfp6`, `NUfp7`, `NUfs6`, `NUip2`, `NUip4`, `NUip6`, `NUip7`, `NUmp1`, `NUmp2`, `NUmp3`, `NUnp1`, `NUnp2`, `NUnp4`, `NUns1`, `NUns4`, `NX`, `O`, `O:q`, `O:r`, `OY`, `PAfp1`, `PAfp1:q`, `PAfp2`, `PAfp3`, `PAfp4`, `PAfp6`, `PAfp7`, `PAfs1`, `PAfs2`, `PAfs3`, `PAfs4`, `PAfs6`, `PAfs7`, `PAip1`, `PAip1:q`, `PAip2`, `PAip4`, `PAip6`, `PAip7`, `PAis1`, `PAis2`, `PAis3`, `PAis4`, `PAis6`, `PAis7`, `PAmp1`, `PAmp2`, `PAmp3`, `PAmp4`, `PAmp7`, `PAms1`, `PAms1:q`, `PAms2`, `PAms3`, `PAms4`, `PAms6`, `PAms7`, `PAnp1`, `PAnp2`, `PAnp4`, `PAnp6`, `PAnp7`, `PAns1`, `PAns1:q`, `PAns2`, `PAns3`, `PAns4`, `PAns6`, `PAop1`, `PAop4`, `PD`, `PFfp1`, `PFfp2`, `PFfp3`, `PFfp4`, `PFfp6`, `PFfp7`, `PFfs1`, `PFfs2`, `PFfs2:r`, `PFfs3`, `PFfs4`, `PFfs4:q`, `PFfs6`, `PFfs7`, `PFfs7:r`, `PFip1`, `PFip2`, `PFip3`, `PFip4`, `PFip6`, `PFip7`, `PFis1`, `PFis2`, 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`SAfs2:r`, `SAfs3:r`, `SAfs4`, `SAfs4:r`, `SAfs6`, `SAfs6:r`, `SAmp1`, `SAmp2`, `SAmp3`, `SAmp4`, `SAmp6`, `SAms1`, `SAms1:r`, `SAms3`, `SAms4`, `SAms4:r`, `SAms5`, `SAms6`, `SAms7`, `SAms7:r`, `SFfp1:q`, `SFfs1`, `SFfs1:r`, `SFfs3`, `SFfs4`, `SFfs4:r`, `SFfs7`, `SFfs7:r`, `SFmp1:r`, `SFms1`, `SFms1:r`, `SFms2:r`, `SFms3:r`, `SFms4:r`, `SFms5:r`, `SFms7:r`, `SSfp1`, `SSfp1:r`, `SSfp2`, `SSfp2:r`, `SSfp3`, `SSfp4`, `SSfp4:q`, `SSfp4:r`, `SSfp6`, `SSfp6:q`, `SSfp6:r`, `SSfp7`, `SSfp7:q`, `SSfs1`, `SSfs1:r`, `SSfs2`, `SSfs2:q`, `SSfs2:r`, `SSfs3`, `SSfs3:r`, `SSfs4`, `SSfs4:q`, `SSfs4:r`, `SSfs5`, `SSfs6`, `SSfs6:q`, `SSfs6:r`, `SSfs7`, `SSfs7:q`, `SSfs7:r`, `SSfs7:rq`, `SSip1`, `SSip1:r`, `SSip2`, `SSip3`, `SSip4`, `SSip6`, `SSip6:r`, `SSip7`, `SSis1`, `SSis1:q`, `SSis1:r`, `SSis2`, `SSis2:q`, `SSis2:r`, `SSis2:rq`, `SSis3`, `SSis3:r`, `SSis4`, `SSis4:q`, `SSis4:r`, `SSis6`, `SSis6:r`, `SSis7`, `SSis7:q`, `SSis7:r`, `SSmp1`, `SSmp1:q`, `SSmp1:r`, `SSmp2`, `SSmp2:r`, `SSmp3`, `SSmp3:r`, `SSmp4`, `SSmp4:r`, `SSmp5`, `SSmp6`, `SSmp7`, `SSmp7:q`, `SSmp7:r`, `SSms1`, `SSms1:q`, `SSms1:r`, `SSms1:r:q`, `SSms1:rq`, `SSms2`, `SSms2:r`, `SSms2:rq`, `SSms3`, `SSms3:r`, `SSms4`, `SSms4:r`, `SSms5`, `SSms5:r`, `SSms6`, `SSms6:r`, `SSms7`, `SSms7:r`, `SSnp1`, `SSnp1:r`, `SSnp2`, `SSnp3`, `SSnp4`, `SSnp6`, `SSnp7`, `SSns1`, `SSns1:r`, `SSns2`, `SSns2:r`, `SSns3`, `SSns4`, `SSns4:r`, `SSns6`, `SSns6:r`, `SSns7`, `SSns7:r`, `SUfs1`, `SUfs1:r`, `SUfs2`, `SUfs2:r`, `SUfs3:r`, `SUfs4`, `SUfs4:r`, `SUfs6:r`, `SUfs7:r`, `SUis1:r`, `SUms1:r`, `SUms2:r`, `SUms4:r`, `SUms7:r`, `SUnp1:r`, `SUnp4`, `SUns1`, `SUns1:r`, `SUns4`, `SUns6`, `SUns6:r`, `SUns7:r`, `Ssfs1:r`, `T`, `T:q`, `TY`, `VBdsb-`, `VBdsc+`, `VBepa+`, `VBepa-`, `VBepb+`, `VBepb-`, `VBepc+`, `VBepc-`, `VBesa+`, `VBesa-`, `VBesb+`, `VBesb-`, `VBesc+`, `VBesc-`, `VHd+`, `VHe+`, `VHe-`, `VId+`, `VId-`, `VIe+`, `VIe+:q`, `VIe-`, `VIj+`, `VKdpa+`, `VKdpa-`, `VKdpb+`, `VKdpb-`, `VKdpb-:q`, `VKdpc+`, `VKdpc+:q`, `VKdpc-`, `VKdsa+`, `VKdsa+:q`, `VKdsa-`, `VKdsb+`, `VKdsb-`, `VKdsc+`, `VKdsc-`, `VKepa+`, `VKepa-`, `VKepa-:q`, `VKepb+`, `VKepb-`, `VKepc+`, `VKepc+:q`, `VKepc-`, `VKesa+`, `VKesa-`, `VKesb+`, `VKesb-`, `VKesc+`, `VKesc-`, `VKjpa+`, `VKjpb+`, `VKjpb-`, `VKjpc+`, `VKjpc-`, `VKjsa+`, `VKjsa-`, `VKjsb+`, `VKjsc+`, `VKjsc-`, `VLdpah+`, `VLdpah-`, `VLdpbh+`, `VLdpbh-`, `VLdpbm+`, `VLdpbm-`, `VLdpc+`, `VLdpcf+`, `VLdpcf+:q`, `VLdpcf-`, `VLdpci+`, `VLdpci+:q`, `VLdpci-`, `VLdpcm+`, `VLdpcm+:q`, `VLdpcm-`, `VLdpcn+`, `VLdpcn-`, `VLdpco+`, `VLdpco+:q`, `VLdpco-`, `VLdsaf+`, `VLdsaf+:q`, `VLdsaf-`, `VLdsam+`, `VLdsam-`, `VLdsbf+`, `VLdsbf-`, `VLdsbm+`, `VLdsbm-`, `VLdsc+`, `VLdscf+`, `VLdscf-`, `VLdsci+`, `VLdsci-`, `VLdscm+`, `VLdscm-`, `VLdscn+`, `VLdscn+:q`, `VLdscn-`, `VLepah+`, `VLepah-`, `VLepam+`, `VLepam-`, `VLepbh+`, `VLepbh-`, `VLepbh-:q`, `VLepbm+`, `VLepcf+`, `VLepcf-`, `VLepci+`, `VLepci+:q`, `VLepci-`, `VLepcm+`, `VLepcm-`, `VLepcn+`, `VLepcn-`, `VLepco+`, `VLepco-`, `VLesaf+`, `VLesaf-`, `VLesam+`, `VLesam-`, `VLesbf+`, `VLesbf-`, `VLesbm+`, `VLesbm-`, `VLesc+`, `VLesc-`, `VLescf+`, `VLescf-`, `VLesci+`, `VLesci-`, `VLescm+`, `VLescm+:q`, `VLescm-`, `VLescm-:q`, `VLescn+`, `VLescn-`, `VLjpah+`, `VLjpbh+`, `VLjpcf+`, `VLjpci+`, `VLjpcm+`, `VLjpco+`, `VLjsaf+`, `VLjsaf-`, `VLjsam+`, `VLjsam-`, `VLjscf+`, `VLjscf-`, `VLjsci+`, `VLjsci-`, `VLjscm+`, `VLjscm-`, `VLjscn+`, `VLjscn-`, `VMdpa+`, `VMdpa-`, `VMdpb+`, `VMdpb+:r`, `VMdpb-`, `VMdsb+`, `VMdsb-`, `VMepa+`, `VMepa-`, `VMepb+`, `VMepb-`, `VMepc-`, `VMesb+`, `VMesb-`, `VMjpb+`, `VMjsb+`, `Vje+`, `W`, `Wms1`, `Y`, `Z`, `ZIP` |
| **`morphologizer`** | `POS=ADV\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=PART`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Cnd\|POS=SCONJ`, `POS=PRON\|PronType=Prs\|Reflex=Yes`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Animacy=Anim\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Anim\|Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|Case=Loc\|POS=ADP`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|PronType=Dem`, `Degree=Pos\|POS=ADV`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Aspect=Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Foreign=Yes\|POS=X`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `AdpType=Prep\|Case=Acc\|POS=ADP`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `AdpType=Preppron\|Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `POS=CCONJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Voc\|Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `POS=SCONJ`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `AdpType=Prep\|Case=Ins\|POS=ADP`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `AdpType=Voc\|Case=Gen\|POS=ADP`, `NumForm=Digit\|POS=NUM`, `Hyph=Yes\|POS=X`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Aspect=Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `AdpType=Prep\|Case=Dat\|POS=ADP`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Aspect=Imp,Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp,Perf\|POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `AdpType=Voc\|Case=Dat\|POS=ADP`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Gender=Neut\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NUM`, `Aspect=Perf\|POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Aspect=Imp\|Gender=Neut\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Abbr=Yes\|POS=PROPN`, `Aspect=Imp\|POS=VERB\|Polarity=Neg\|VerbForm=Inf`, `Mood=Cnd\|POS=AUX`, `Aspect=Imp\|Gender=Neut\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Aspect=Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Neut\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=X`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|Polarity=Neg\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem\|Typo=Yes`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `POS=INTJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `AdpType=Prep\|Case=Loc\|POS=ADP\|Typo=Yes`, `Animacy=Anim\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Animacy=Inan\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|POS=ADV\|Typo=Yes`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Gender=Fem\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=ADV\|PronType=Neg`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Aspect=Imp,Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=ADV\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Anim\|Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Aspect=Imp,Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|POS=AUX\|Polarity=Pos\|VerbForm=Inf`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `AdpType=Voc\|Case=Ins\|POS=ADP`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Number=Sing\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Animacy=Anim\|Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Hyph=Yes\|POS=ADV`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=ADV\|PronType=Tot`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Ins\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `NumType=Mult\|POS=ADV`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Animacy=Anim\|Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `AdpType=Preppron\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Animacy=Inan\|Case=Ins\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|Typo=Yes`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Abbr=Yes\|POS=ADV`, `Animacy=Anim\|Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|Typo=Yes`, `Case=Gen\|Gender=Fem\|NumType=Mult\|Number=Plur\|POS=ADJ`, `AdpType=Prep\|Case=Ins\|POS=ADP\|Typo=Yes`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Number=Sing\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel\|Typo=Yes`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `POS=CCONJ\|Typo=Yes`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `AdpType=Prep\|Case=Gen\|POS=ADP\|Typo=Yes`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Loc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `POS=PRON\|PronType=Prs\|Reflex=Yes\|Typo=Yes`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `AdpType=Voc\|Case=Ins\|POS=ADP\|Typo=Yes`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NUM`, `Aspect=Perf\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Animacy=Inan\|Aspect=Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Ins\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Animacy=Inan\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Anim\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp,Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `AdpType=Preppron\|Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADV`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Gender=Neut\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Imp,Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Aspect=Imp,Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `POS=NUM`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Anim\|Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp,Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Aspect=Imp,Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Aspect=Imp\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp,Perf\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Cnd\|POS=PART`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADV`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `AdpType=Preppron\|Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Gender=Neut\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp,Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADJ`, `NumType=Mult\|POS=ADV\|PronType=Ind`, `Aspect=Imp,Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Emp`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Animacy=Anim\|Case=Ins\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Neg`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem\|Typo=Yes`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Ins\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Gender=Neut\|Number=Sing\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `ConjType=Oper\|POS=SYM`, `POS=SYM`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Aspect=Imp,Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Ins\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Gender=Neut\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=DET\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NUM`, `Aspect=Imp\|Gender=Fem\|Number=Plur\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Emp`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADV`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Aspect=Imp,Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Fem\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=X`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes\|Typo=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Inan\|Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `AdpType=Prep\|Case=Acc\|POS=ADP\|Typo=Yes`, `Animacy=Inan\|Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `AdpType=Voc\|Case=Acc\|POS=ADP`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Animacy=Inan\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `AdpType=Preppron\|Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Imp\|Gender=Neut\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp,Perf\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Gender=Neut\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=ADV`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `POS=PART\|Typo=Yes`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Tot`, `Animacy=Anim\|Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `NumType=Mult\|POS=ADV\|PronType=Int,Rel`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Animacy=Anim\|Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel\|Typo=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Pos\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Anim\|Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `NumType=Card\|POS=DET\|PronType=Dem`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `AdpType=Preppron\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Dat\|Gender=Neut\|NumType=Mult\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADV`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Case=Nom\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Animacy=Anim\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel\|Typo=Yes`, `Animacy=Inan\|Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel\|Typo=Yes`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Polarity=Neg\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|POS=VERB\|Polarity=Pos`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `det:numgov`, `discourse`, `expl:pass`, `expl:pv`, `fixed`, `flat`, `flat:foreign`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:arg`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `6`, `8`, `10`, `11`, `13`, `14`, `16`, `17`, `19`, `20`, `23`, `25`, `27`, `29`, `32`, `34`, `36`, `40`, `42`, `44`, `46`, `48`, `50`, `52`, `54`, `55`, `57`, `59`, `61`, `64`, `66`, `67`, `71`, `73`, `74`, `75`, `77`, `79`, `81`, `83`, `86`, `87`, `89`, `91`, `95`, `97`, `99`, `102`, `12`, `104`, `106`, `108`, `110`, `112`, `114`, `116`, `117`, `119`, `121`, `123`, `125`, `127`, `129`, `131`, `133`, `136`, `138`, `141`, `143`, `146`, `150`, `151`, `153`, `156`, `157`, `159`, `160`, `162`, `163`, `165`, `167`, `168`, `170`, `173`, `174`, `176`, `178`, `180`, `181`, `182`, `183`, `185`, `187`, `190`, `191`, `193`, `195`, `28`, `198`, `200`, `202`, `204`, `206`, `209`, `210`, `212`, `213`, `216`, `219`, `220`, `222`, `223`, `225`, `228`, `230`, `232`, `235`, `237`, `238`, `240`, `241`, `242`, `243`, `244`, `247`, `249`, `251`, `252`, `255`, `257`, `258`, `260`, `262`, `264`, `266`, `267`, `269`, `270`, `271`, `272`, `62`, `274`, `275`, `277`, `278`, `279`, `280`, `281`, `282`, `284`, `286`, `287`, `31`, `289`, `290`, `291`, `293`, `294`, `295`, `297`, `299`, `300`, `301`, `303`, `304`, `305`, `306`, `307`, `308`, `309`, `310`, `312`, `313`, `314`, `315`, `318`, `319`, `321`, `323`, `324`, `326`, `328`, `330`, `331`, `333`, `334`, `335`, `336`, `337`, `338`, `339`, `341`, `343`, `345`, `346`, `348`, `349`, `350`, `351`, `352`, `353`, `354`, `357`, `359`, `360`, `361`, `362`, `364`, `366`, `368`, `369`, `370`, `372`, `373`, `374`, `375`, `376`, `377`, `378`, `379`, `380`, `381`, `382`, `384`, `385`, `386`, `387`, `388`, `389`, `391`, `394`, `397`, `398`, `399`, `401`, `402`, `403`, `404`, `405`, `406`, `407`, `408`, `409`, `410`, `412`, `413`, `415`, `417`, `418`, `420`, `422`, `423`, `424`, `427`, `428`, `430`, `432`, `433`, `434`, `435`, `436`, `439`, `440`, `441`, `442`, `443`, `444`, `445`, `447`, `449`, `452`, `454`, `456`, `457`, `458`, `459`, `460`, `461`, `462`, `464`, `465`, `466`, `468`, `469`, `471`, `473`, `474`, `476`, `478`, `480`, `481`, `482`, `483`, `486`, `488`, `489`, `490`, `491`, `492`, `493`, `495`, `496`, `497`, `256`, `498`, `499`, `500`, `501`, `502`, `503`, `504`, `505`, `506`, `507`, `508`, `509`, `510`, `512`, `513`, `514`, `515`, `516`, `517`, `518`, `519`, `520`, `521`, `522`, `523`, `524`, `525`, `0`, `526`, `528`, `529`, `530`, `533`, `534`, `536`, `537`, `538`, `539`, `540`, `541`, `542`, `543`, `544`, `545`, `547`, `548`, `549`, `550`, `552`, `553`, `554`, `555`, `557`, `558`, `559`, `561`, `563`, `564`, `565`, `566`, `567`, `568`, `569`, `570`, `571`, `572`, `573`, `574`, `575`, `576`, `577`, `578`, `579`, `580`, `581`, `583`, `584`, `586`, `587`, `588`, `589`, `590`, `591`, `592`, `594`, `596`, `597`, `599`, `601`, `603`, `605`, `607`, `609`, `612`, `614`, `616`, `618`, `619`, `41`, `621`, `623`, `625`, `626`, `627`, `628`, `630`, `633`, `634`, `636`, `640`, `641`, `643`, `644`, `646`, `647`, `649`, `651`, `653`, `655`, `657`, `659`, `661`, `663`, `665`, `666`, `667`, `669`, `671`, `672`, `673`, `674`, `675`, `677`, `679`, `680`, `681`, `684`, `685`, `686`, `687`, `688`, `690`, `691`, `692`, `694`, `695`, `696`, `697`, `698`, `700`, `703`, `705`, `707`, `708`, `710`, `711`, `712`, `713`, `714`, `113`, `716`, `717`, `719`, `84`, `721`, `723`, `724`, `726`, `727`, `728`, `729`, `730`, `732`, `734`, `735`, `737`, `740`, `742`, `744`, `747`, `748`, `750`, `751`, `753`, `755`, `757`, `760`, `764`, `47`, `765`, `766`, `768`, `769`, `771`, `772`, `773`, `774`, `777`, `234`, `779`, `781`, `782`, `783`, `785`, `786`, `788`, `790`, `792`, `793`, `794`, `797`, `798`, `800`, `802`, `804`, `805`, `806`, `807`, `809`, `810`, `812`, `815`, `816`, `817`, `818`, `820`, `821`, `822`, `824`, `825`, `826`, `827`, `828`, `830`, `832`, `834`, `835`, `836`, `837`, `838`, `839`, `840`, `842`, `844`, `846`, `848`, `850`, `852`, `855`, `857`, `859`, `861`, `862`, `863`, `864`, `865`, `866`, `867`, `98`, `868`, `869`, `870`, `873`, `263`, `874`, `875`, `876`, `877`, `878`, `879`, `881`, `882`, `884`, `885`, `887`, `888`, `889`, `891`, `892`, `896`, `897`, `898`, `899`, `900`, `901`, `902`, `903`, `904`, `905`, `906`, `907`, `908`, `909`, `910`, `911`, `913`, `914`, `915`, `917`, `919`, `920`, `921`, `922`, `923`, `926`, `927`, `928`, `929`, `931`, `932`, `934`, `936`, `939`, `941`, `943`, `945`, `946`, `947`, `949`, `951`, `952`, `953`, `955`, `958`, `959`, `960`, `961`, `962`, `964`, `965`, `967`, `968`, `969`, `971`, `973`, `975`, `976`, `977`, `978`, `980`, `982`, `983`, `984`, `985`, `986`, `987`, `989`, `992`, `993`, `996`, `998`, `999`, `1000`, `1001`, `1003`, `1004`, `1005`, `1006`, `1007`, `1008`, `1009`, `1010`, `1011`, `1012`, `1013`, `1015`, `1017`, `1019`, `1021`, `1022`, `1023`, `1024`, `1025`, `1027`, `1028`, `1030`, `1031`, `1035`, `1037`, `1038`, `1039`, `1042`, `1043`, `1045`, `1046`, `1047`, `1049`, `1051`, `1052`, `1054`, `1056`, `1057`, `1058`, `1060`, `1061`, `1062`, `1064`, `1065`, `1066`, `1067`, `1068`, `1069`, `1071`, `1073`, `1074`, `1076`, `1078`, `1079`, `1080`, `1081`, `1082`, `1083`, `1086`, `1088`, `1090`, `1091`, `1092`, `1094`, `1095`, `1096`, `1099`, `1100`, `1101`, `1102`, `1103`, `1104`, `1106`, `1107`, `1108`, `1109`, `1110`, `1112`, `1114`, `1116`, `1118`, `1119`, `1121`, `1122`, `1124`, `1125`, `1126`, `1127`, `107`, `1128`, `1129`, `1130`, `1132`, `1133`, `1136`, `1137`, `1139`, `1141`, `1143`, `1145`, `1146`, `1147`, `1148`, `1150`, `1151`, `1153`, `1155`, `1156`, `1157`, `1160`, `1161`, `1162`, `1163`, `1164`, `1167`, `1169`, `1170`, `1171`, `1172`, `1173`, `1174`, `1175`, `1176`, `1177`, `1178`, `1180`, `1181`, `1182`, `1183`, `1184`, `1186`, `1188`, `1189`, `1191`, `1193`, `1194`, `1195`, `1196`, `1198`, `1201`, `1203`, `1204`, `1205`, `7`, `80`, `1207`, `1209`, `1210`, `1212`, `1213`, `1215`, `1216`, `1217`, `1218`, `1219`, `1220`, `1221`, `1223`, `1225`, `1226`, `1228`, `1229`, `1232`, `1234`, `1236`, `1238`, `1239`, `1240`, `1241`, `1242`, `1243`, `1244`, `1246`, `1248`, `1250`, `1252`, `1254`, `1256`, `1257`, `1259`, `1260`, `1263`, `1265`, `1266`, `1267`, `1269`, `1272`, `1274`, `1275`, `1276`, `1277`, `1279`, `1281`, `1284`, `1285`, `1288`, `1289`, `1290`, `1291`, `1292`, `1293`, `1294`, `1295`, `1297`, `1298`, `1299`, `1301`, `1302`, `1303`, `1305`, `1307`, `1308`, `1309`, `1310`, `1311`, `1312`, `1314`, `1315`, `1316`, `1317`, `1318`, `1320`, `1322`, `1323`, `1325`, `1326`, `1328`, `1329`, `1331`, `194`, `1333`, `1335`, `1337`, `1339`, `1341`, `1342`, `1343`, `1344`, `1345`, `1347`, `1348`, `1350`, `1351`, `1353`, `1354`, `1356`, `1358`, `1359`, `1360`, `1361`, `1363`, `1364`, `1365`, `1366`, `1368`, `1370`, `1372`, `1374`, `1375`, `1377`, `1378`, `1380`, `1382`, `1383`, `1384`, `1385`, `1386`, `1387`, `1388`, `1392`, `1395`, `1397`, `1398`, `1400`, `1401`, `1402`, `1403`, `1404`, `1405`, `1406`, `1408`, `1410`, `1412`, `1413`, `1414`, `1235`, `1415`, `1417`, `1418`, `1419`, `1420`, `1421`, `1422`, `1423`, `1425`, `1426`, `1427`, `1428`, `1283`, `1430`, `1431`, `1432`, `1434`, `1435`, `1437`, `1439`, `1440`, `1442`, `1443`, `1444`, `1446`, `1447`, `1448`, `1449`, `1450`, `1452`, `1453`, `1454`, `1457`, `1458`, `1459`, `1461`, `1462`, `1464`, `1465`, `1466`, `1467`, `1469`, `1470`, `1471`, `1472`, `1473`, `1474`, `1475`, `1477`, `1478`, `1479`, `1480`, `1481`, `1483`, `1485`, `1487`, `1488`, `1489`, `1491`, `1493`, `1494`, `1496`, `1498`, `1500`, `1501`, `1503`, `1504`, `1271`, `1505`, `1506`, `1507`, `1508`, `1509`, `1511`, `1512`, `1513`, `1516`, `1517`, `1518`, `1519`, `1521`, `1522`, `1523`, `1526`, `1528`, `1530`, `1531`, `1532`, `1534`, `1535`, `1536`, `1537`, `1538`, `1539`, `1540`, `1542`, `966`, `1544`, `1545`, `1546`, `1547`, `1549`, `1551`, `1552`, `1553`, `1554`, `1555`, `1556`, `1557`, `1558`, `1559`, `950`, `1561`, `1562`, `1563`, `1564`, `1565`, `1567`, `1568`, `1569`, `1570`, `1571`, `1572`, `1574`, `1575`, `1577`, `1578`, `1580`, `1582`, `1584`, `1587`, `1590`, `1592`, `1593`, `1596`, `1598`, `1599`, `1600`, `1601`, `1602`, `1603`, `1604`, `1606`, `1607`, `1609`, `1610`, `1611`, `1612`, `1613`, `1615`, `1616`, `1618`, `1619`, `1620`, `1622`, `1624`, `1626`, `1628`, `1629`, `1631`, `1632`, `1633`, `1634`, `1635`, `1636`, `1637`, `1639`, `1640`, `1642`, `1646`, `1647`, `1648`, `1650`, `1651`, `1653`, `1654`, `1655`, `1656`, `1658`, `1660`, `1662`, `1664`, `1665`, `1666`, `1669`, `1671`, `1672`, `1673`, `1674`, `1676`, `1677`, `1678`, `1679`, `1680`, `1681`, `1683`, `1685`, `1686`, `1687`, `1688`, `1689`, `1690`, `1691`, `1692`, `1694`, `1695`, `1696`, `1700`, `1702`, `1703`, `1705`, `1706`, `1709`, `1710`, `1711`, `1712`, `1713`, `1714`, `1715`, `1716`, `1717`, `1721`, `1723`, `1725`, `1726`, `1727`, `1728`, `1729`, `1730`, `1731`, `1732`, `1733`, `1734`, `1736`, `1737`, `1738`, `1739`, `1741`, `1742`, `1745`, `1746`, `1747`, `1749`, `1751`, `1752`, `1753`, `1754`, `1755`, `1756`, `1758`, `1759`, `1760`, `1761`, `1763`, `1766`, `1767`, `1768`, `1770`, `1771`, `1772`, `1773`, `1774`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 100.00 |
| `TOKEN_P` | 100.00 |
| `TOKEN_R` | 100.00 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 92.13 |
| `SENTS_P` | 92.08 |
| `SENTS_R` | 92.17 |
| `TAG_ACC` | 91.20 |
| `POS_ACC` | 97.20 |
| `MORPH_ACC` | 93.90 |
| `DEP_UAS` | 94.14 |
| `DEP_LAS` | 90.95 |
| `LEMMA_ACC` | 91.74 |
|
{"language": ["sk"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/sk_udv25_slovaksnk_trf
|
[
"spacy",
"token-classification",
"sk",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sk"
] |
TAGS
#spacy #token-classification #sk #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Slovak-SNK
### Label Scheme
View label scheme (4879 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (4879 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #sk #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4879 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #sk #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (4879 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Serbian-SET
| Feature | Description |
| --- | --- |
| **Name** | `sr_udv25_serbianset_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (2603 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `Agcfpay`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmpny`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnply`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmsann`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpgy`, `Agsfpiy`, `Agsfpny`, `Agsfsay`, `Agsfsgy`, `Agsfsiy`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmply`, `Agsmpny`, `Agsmsayn`, `Agsmsayy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnpny`, `Agsnsgy`, `Agsnsiy`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpgy`, `Appnpiy`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsiy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsiy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmpny`, `Aspmsann`, `Aspmsayy`, `Aspmsdy`, `Aspmsgy`, `Aspmsiy`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnsay`, `Aspnsgy`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--i`, `Mlcf-a`, `Mlcf-g`, `Mlcf-n`, `Mlcfpa`, `Mlcfpg`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-n`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsd`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsl`, `Mlcnsn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompd`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsan`, `Mlomsay`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonsa`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-n`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Npfpd`, `Npfpg`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpl`, `Pd-mpn`, `Pd-msan`, `Pd-msay`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npl`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pi--sn`, `Pi-fpa`, `Pi-fpd`, `Pi-fpg`, `Pi-fpi`, `Pi-fpl`, `Pi-fpn`, `Pi-fsa`, `Pi-fsd`, `Pi-fsg`, `Pi-fsi`, `Pi-fsl`, `Pi-fsn`, `Pi-mpa`, `Pi-mpd`, `Pi-mpg`, `Pi-mpi`, `Pi-mpl`, `Pi-mpn`, `Pi-msan`, `Pi-msay`, `Pi-msd`, `Pi-msg`, `Pi-msi`, `Pi-msl`, `Pi-msn`, `Pi-npa`, `Pi-npd`, `Pi-npg`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-n`, `Pi3n-a`, `Pi3n-g`, `Pi3n-i`, `Pi3n-l`, `Pi3n-n`, `Pp1-pa`, `Pp1-pd`, `Pp1-pg`, `Pp1-pi`, `Pp1-pl`, `Pp1-pn`, `Pp1-sa`, `Pp1-sd`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pl`, `Pp2-pn`, `Pp3-pa`, `Pp3-pd`, `Pp3-pg`, `Pp3-pi`, `Pp3-pl`, `Pp3fpn`, `Pp3fsa`, `Pp3fsd`, `Pp3fsg`, `Pp3fsi`, `Pp3fsl`, `Pp3fsn`, `Pp3mpn`, `Pp3msa`, `Pp3msd`, `Pp3msg`, `Pp3msi`, `Pp3msl`, `Pp3msn`, `Pp3npn`, `Pp3nsa`, `Pp3nsn`, `Pq-fpa`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3n-n`, `Ps1fpa`, `Ps1fpd`, `Ps1fpg`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsl`, `Ps1fsn`, `Ps1mpa`, `Ps1mpd`, `Ps1mpg`, `Ps1mpl`, `Ps1mpn`, `Ps1msan`, `Ps1msd`, `Ps1msg`, `Ps1msn`, `Ps1nsa`, `Ps1nsg`, `Ps1nsl`, `Ps1nsn`, `Ps2fpl`, `Ps2fpn`, `Ps2msan`, `Ps2nsl`, `Ps2nsn`, `Ps3fpa`, `Ps3fpg`, `Ps3fpl`, `Ps3fpn`, `Ps3fsa`, `Ps3fsd`, `Ps3fsg`, `Ps3fsi`, `Ps3fsl`, `Ps3fsn`, `Ps3mpa`, `Ps3mpd`, `Ps3mpg`, `Ps3mpl`, `Ps3mpn`, `Ps3msan`, `Ps3msd`, `Ps3msg`, `Ps3msi`, `Ps3msl`, `Ps3msn`, `Ps3npa`, `Ps3npg`, `Ps3npl`, `Ps3nsa`, `Ps3nsg`, `Ps3nsl`, `Ps3nsn`, `Px--sa`, `Px--sd`, `Px--sg`, `Px--si`, `Px--sl`, `Px-fpa`, `Px-fpg`, `Px-fpi`, `Px-fpl`, `Px-fsa`, `Px-fsd`, `Px-fsg`, `Px-fsi`, `Px-fsl`, `Px-mpa`, `Px-mpd`, `Px-mpg`, `Px-mpi`, `Px-mpl`, `Px-msan`, `Px-msay`, `Px-msd`, `Px-msg`, `Px-msi`, `Px-msl`, `Px-npa`, `Px-npg`, `Px-npl`, `Px-nsa`, `Px-nsg`, `Qo`, `Qq`, `Qz`, `Rgc`, `Rgp`, `Rgs`, `Rr`, `Sa`, `Sd`, `Sg`, `Si`, `Sl`, `Vaa1p`, `Vaa1s`, `Vaa3p`, `Vaa3s`, `Vaf3p`, `Vaf3s`, `Van`, `Vap-pf`, `Vap-pm`, `Vap-pn`, `Vap-sf`, `Vap-sm`, `Vap-sn`, `Var1p`, `Var1s`, `Var2p`, `Var3p`, `Var3s`, `Vma3s`, `Vmf1p`, `Vmf1s`, `Vmf2p`, `Vmf3p`, `Vmf3s`, `Vmm1p`, `Vmm2p`, `Vmn`, `Vmp-pf`, `Vmp-pm`, `Vmp-pn`, `Vmp-sf`, `Vmp-sm`, `Vmp-sn`, `Vmr1p`, `Vmr1s`, `Vmr2p`, `Vmr3p`, `Vmr3s`, `X`, `Xf`, `Y`, `Z` |
| **`morphologizer`** | `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `POS=CCONJ`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=X`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `NumType=Ord\|POS=NUM`, `Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PART\|Polarity=Neg`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|POS=ADP`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Degree=Pos\|POS=ADV\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `POS=PART`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADV\|Tense=Past\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|POS=ADV\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Degree=Cmp\|POS=DET`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Degree=Pos\|POS=ADV\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Pos\|POS=ADV\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `NumType=Ord\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=ADP`, `Degree=Sup\|POS=ADV`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|POS=DET`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Degree=Pos\|POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `POS=ADV\|Tense=Pres\|VerbForm=Conv`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Degree=Pos\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Degree=Sup\|POS=DET`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Ins\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `POS=SYM`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `POS=INTJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Ins\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=ADV\|VerbForm=Part`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:numgov`, `discourse`, `fixed`, `flat`, `list`, `mark`, `nmod`, `nsubj`, `nummod`, `nummod:gov`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `3`, `5`, `7`, `9`, `10`, `12`, `14`, `16`, `17`, `19`, `21`, `23`, `25`, `27`, `29`, `31`, `33`, `35`, `38`, `40`, `41`, `43`, `45`, `47`, `49`, `51`, `53`, `56`, `58`, `60`, `62`, `64`, `66`, `68`, `70`, `73`, `74`, `77`, `80`, `82`, `84`, `86`, `88`, `90`, `92`, `94`, `96`, `98`, `100`, `104`, `105`, `107`, `109`, `111`, `113`, `116`, `118`, `120`, `121`, `124`, `126`, `128`, `130`, `132`, `134`, `136`, `138`, `141`, `143`, `145`, `147`, `150`, `153`, `155`, `157`, `159`, `160`, `162`, `164`, `166`, `167`, `168`, `170`, `172`, `174`, `175`, `176`, `178`, `180`, `182`, `184`, `188`, `190`, `191`, `194`, `196`, `198`, `199`, `201`, `202`, `205`, `207`, `209`, `211`, `214`, `217`, `219`, `221`, `223`, `225`, `227`, `229`, `231`, `233`, `237`, `239`, `241`, `243`, `245`, `83`, `246`, `247`, `249`, `253`, `255`, `258`, `260`, `262`, `263`, `265`, `269`, `271`, `272`, `274`, `275`, `276`, `277`, `278`, `280`, `282`, `283`, `285`, `287`, `289`, `291`, `292`, `293`, `294`, `295`, `297`, `298`, `299`, `301`, `302`, `304`, `306`, `308`, `310`, `312`, `314`, `315`, `317`, `320`, `321`, `323`, `325`, `327`, `328`, `330`, `332`, `333`, `335`, `337`, `338`, `340`, `341`, `342`, `343`, `346`, `250`, `348`, `349`, `350`, `351`, `353`, `354`, `356`, `358`, `360`, `362`, `364`, `365`, `367`, `369`, `371`, `373`, `375`, `376`, `378`, `380`, `382`, `384`, `385`, `386`, `388`, `391`, `395`, `398`, `400`, `402`, `404`, `406`, `409`, `413`, `415`, `419`, `421`, `424`, `426`, `427`, `428`, `429`, `430`, `431`, `432`, `434`, `436`, `438`, `440`, `442`, `444`, `446`, `447`, `449`, `450`, `452`, `454`, `455`, `457`, `459`, `461`, `462`, `463`, `465`, `466`, `468`, `470`, `472`, `474`, `476`, `477`, `478`, `480`, `483`, `485`, `486`, `489`, `491`, `492`, `497`, `498`, `500`, `501`, `502`, `503`, `504`, `507`, `508`, `509`, `510`, `512`, `513`, `515`, `516`, `518`, `519`, `521`, `523`, `524`, `526`, `527`, `529`, `531`, `532`, `533`, `535`, `538`, `540`, `542`, `543`, `545`, `547`, `550`, `552`, `553`, `556`, `557`, `558`, `561`, `562`, `563`, `566`, `567`, `569`, `571`, `572`, `574`, `576`, `578`, `580`, `582`, `583`, `586`, `588`, `590`, `592`, `594`, `596`, `600`, `601`, `603`, `606`, `607`, `609`, `610`, `611`, `613`, `614`, `615`, `616`, `618`, `620`, `623`, `624`, `626`, `627`, `629`, `630`, `632`, `635`, `637`, `639`, `641`, `642`, `643`, `645`, `647`, `648`, `649`, `652`, `654`, `655`, `658`, `660`, `662`, `665`, `667`, `668`, `670`, `672`, `674`, `675`, `676`, `678`, `680`, `682`, `683`, `684`, `685`, `687`, `688`, `690`, `691`, `693`, `694`, `696`, `697`, `699`, `701`, `703`, `705`, `707`, `708`, `709`, `710`, `711`, `713`, `714`, `716`, `717`, `718`, `721`, `723`, `725`, `726`, `730`, `732`, `734`, `735`, `736`, `737`, `739`, `740`, `741`, `742`, `744`, `746`, `747`, `749`, `750`, `752`, `754`, `755`, `756`, `757`, `760`, `761`, `762`, `763`, `765`, `768`, `769`, `771`, `772`, `773`, `774`, `775`, `777`, `780`, `781`, `783`, `784`, `785`, `787`, `789`, `790`, `791`, `792`, `794`, `797`, `798`, `800`, `802`, `803`, `806`, `808`, `811`, `813`, `815`, `817`, `819`, `822`, `825`, `827`, `829`, `831`, `833`, `835`, `836`, `838`, `839`, `842`, `845`, `848`, `850`, `851`, `852`, `853`, `855`, `856`, `858`, `860`, `862`, `864`, `866`, `867`, `868`, `871`, `874`, `875`, `876`, `877`, `878`, `880`, `883`, `884`, `885`, `887`, `889`, `890`, `894`, `895`, `896`, `898`, `899`, `901`, `902`, `903`, `904`, `905`, `908`, `909`, `910`, `911`, `913`, `915`, `916`, `917`, `919`, `920`, `921`, `922`, `923`, `925`, `927`, `928`, `929`, `931`, `932`, `934`, `935`, `937`, `938`, `941`, `943`, `945`, `946`, `947`, `948`, `950`, `951`, `952`, `953`, `954`, `955`, `957`, `959`, `962`, `964`, `965`, `966`, `969`, `971`, `972`, `973`, `975`, `977`, `978`, `980`, `982`, `983`, `985`, `986`, `987`, `989`, `990`, `992`, `994`, `996`, `998`, `999`, `1001`, `1002`, `1005`, `1007`, `1009`, `1012`, `1014`, `1016`, `1018`, `1020`, `1022`, `1025`, `1026`, `1028`, `1029`, `1031`, `1033`, `1035`, `1036`, `374`, `1038`, `1039`, `1040`, `1041`, `1042`, `1043`, `1045`, `1047`, `1050`, `1053`, `1054`, `1055`, `1056`, `1057`, `1059`, `1060`, `1061`, `1063`, `1064`, `1065`, `1066`, `1068`, `1070`, `1072`, `1074`, `1075`, `1077`, `1079`, `1080`, `1082`, `1083`, `1084`, `1087`, `1090`, `1091`, `1092`, `1093`, `1095`, `1096`, `1097`, `1100`, `1101`, `1102`, `1103`, `1104`, `1106`, `1108`, `1110`, `1112`, `1114`, `1115`, `1116`, `1117`, `1118`, `1119`, `1120`, `1122`, `1123`, `1125`, `1126`, `1127`, `1129`, `1130`, `1131`, `1132`, `1133`, `1134`, `1135`, `1136`, `1137`, `1139`, `1141`, `1142`, `1143`, `1144`, `1146`, `1148`, `1150`, `1151`, `1152`, `1153`, `1155`, `1157`, `1158`, `1159`, `1160`, `1162`, `1165`, `1167`, `1168`, `1169`, `1170`, `1172`, `1174`, `1177`, `1179`, `1181`, `1183`, `1184`, `1187`, `1189`, `1190`, `1193`, `1194`, `1195`, `1196`, `1197`, `1199`, `1200`, `1201`, `1202`, `1203`, `1205`, `1207`, `1210`, `1211`, `1212`, `1213`, `1215`, `1216`, `1218`, `1219`, `1221`, `1222`, `1223`, `1224`, `1227`, `1228`, `1230`, `1232`, `1235`, `1236`, `1237`, `1239`, `1241`, `1242`, `1244`, `1246`, `1248`, `1250`, `1253`, `1255`, `1256`, `1258`, `1259`, `1260`, `1261`, `1262`, `1263`, `1265`, `1266`, `1267`, `1269`, `1272`, `1275`, `1277`, `1279`, `1281`, `1283`, `1285`, `1287`, `1289`, `1291`, `1292`, `1293`, `1295`, `1296`, `1297`, `1298`, `1300`, `1303`, `1304`, `1306`, `1308`, `1310`, `1311`, `1312`, `1313`, `1315`, `1316`, `1317`, `1318`, `1319`, `1320`, `1321`, `1322`, `1323`, `1325`, `1326`, `1327`, `1329`, `1331`, `1332`, `1333`, `1334`, `1335`, `1336`, `1338`, `1339`, `1340`, `1342`, `1344`, `1346`, `1347`, `1349`, `1350`, `1353`, `1356`, `1357`, `1358`, `1359`, `1360`, `1361`, `1362`, `1363`, `1364`, `1367`, `1368`, `1369`, `1370`, `1371`, `1372`, `1373`, `1374`, `1376`, `1377`, `1379`, `1381`, `1382`, `1384`, `1385`, `1386`, `1387`, `1388`, `1390`, `1391`, `1392`, `1393`, `1395`, `1396`, `1398`, `1399`, `1401`, `1402`, `1404`, `1405`, `1406`, `1408`, `1409`, `1410`, `1412`, `1413`, `1415`, `1417`, `1418`, `1420`, `1421`, `1422`, `1423`, `1424`, `1425`, `1426`, `1428`, `1429`, `1430`, `1431`, `1433`, `1434`, `1435`, `1436`, `1437`, `1438`, `1439`, `1441`, `1442`, `1444`, `1445`, `1447`, `1448`, `1449`, `1450`, `1451`, `1452`, `1453`, `1454`, `1456`, `1458`, `1461`, `1462`, `1463`, `1464`, `1467`, `1469`, `1471`, `1472`, `1474`, `1475`, `1477`, `1479`, `1481`, `1483`, `1484`, `1485`, `1486`, `1487`, `1488`, `1490`, `1492`, `1493`, `1496`, `1497`, `1499`, `1500`, `1502`, `1503`, `1504`, `1505`, `1052`, `1507`, `1508`, `1510`, `1513`, `1515`, `1516`, `1518`, `1519`, `1520`, `1521`, `1523`, `1524`, `1525`, `1526`, `1527`, `1528`, `1529`, `1531`, `1533`, `1534`, `1535`, `1536`, `1537`, `1538`, `1540`, `1541`, `1542`, `1543`, `1544`, `1545`, `1546`, `1547`, `1548`, `1549`, `1550`, `1551`, `1552`, `1553`, `1554`, `1556`, `1557`, `1558`, `1560`, `1561`, `1562`, `1563`, `1564`, `1565`, `1566`, `1567`, `1568`, `1569`, `1571`, `1573`, `1574`, `1576`, `1578`, `1580`, `1581`, `1583`, `1585`, `1586`, `1587`, `1588`, `1589`, `1591`, `1592`, `1593`, `1594`, `1596`, `1597`, `1598`, `1600`, `1602`, `1605`, `1606`, `1607`, `1609`, `1611`, `1612`, `1613`, `1614`, `1616`, `1619`, `1623`, `1624`, `1626`, `1627`, `1628`, `1629`, `1631`, `1633`, `1635`, `1637`, `1638`, `1639`, `1640`, `1641`, `1642`, `1643`, `1645`, `1646`, `1648`, `1649`, `1650`, `1651`, `1652`, `1653`, `1654`, `1655`, `1656`, `1658`, `1659`, `1660`, `1661`, `1662`, `1663`, `1664`, `1665`, `1666`, `1667`, `1669`, `1670`, `1672`, `1673`, `1674`, `1677`, `372`, `1678`, `1680`, `1682`, `1683`, `1684`, `1685`, `1687`, `1689`, `1690`, `1692`, `1694`, `1695`, `1696`, `1697`, `1698`, `1700`, `1702`, `1703`, `1704`, `1706`, `1708`, `1709`, `1710`, `1712`, `1713`, `1715`, `1717`, `1720`, `1722`, `1724`, `1726`, `1728`, `1730`, `1731`, `1732`, `1734`, `1735`, `1737`, `1739`, `1741`, `1743`, `1744`, `1745`, 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`1923`, `1924`, `1925`, `1926`, `1928`, `1931`, `1932`, `1933`, `1934`, `1935`, `1936`, `1937`, `1939`, `1940`, `1943`, `1945`, `1946`, `1947`, `1949`, `1950`, `1952`, `1955`, `1956`, `1957`, `1958`, `1959`, `1960`, `1962`, `1964`, `1965`, `1968`, `1969`, `1970`, `1971`, `1972`, `1973`, `1975`, `1977`, `1979`, `1980`, `1982`, `1984`, `1986`, `1989`, `1990`, `1992`, `1993`, `1995`, `1997`, `1999`, `2001`, `2003`, `2005`, `2006`, `2008`, `2009`, `2010`, `2011`, `2012`, `2014`, `2016`, `2017`, `2018`, `2019`, `2021`, `2022`, `2024`, `2026`, `2027`, `2030`, `2032`, `2035`, `2037`, `2039`, `2040`, `2041`, `2042`, `2043`, `2045`, `2047` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.92 |
| `TOKEN_P` | 99.91 |
| `TOKEN_R` | 99.94 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 98.04 |
| `SENTS_P` | 98.31 |
| `SENTS_R` | 97.76 |
| `TAG_ACC` | 95.86 |
| `POS_ACC` | 98.56 |
| `MORPH_ACC` | 96.05 |
| `DEP_UAS` | 93.72 |
| `DEP_LAS` | 90.25 |
| `LEMMA_ACC` | 95.94 |
|
{"language": ["sr"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/sr_udv25_serbianset_trf
|
[
"spacy",
"token-classification",
"sr",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sr"
] |
TAGS
#spacy #token-classification #sr #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Serbian-SET
### Label Scheme
View label scheme (2603 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (2603 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #sr #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2603 labels for 6 components)",
"### Accuracy"
] |
[
33,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #sr #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (2603 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Swedish-Talbanken
| Feature | Description |
| --- | --- |
| **Name** | `sv_udv25_swedishtalbanken_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1206 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `AB`, `AB\|AN`, `AB\|KOM`, `AB\|POS`, `AB\|SMS`, `AB\|SUV`, `DT\|NEU\|SIN\|DEF`, `DT\|NEU\|SIN\|IND`, `DT\|NEU\|SIN\|IND/DEF`, `DT\|UTR/NEU\|PLU\|DEF`, `DT\|UTR/NEU\|PLU\|IND`, `DT\|UTR/NEU\|PLU\|IND/DEF`, `DT\|UTR/NEU\|SIN/PLU\|IND`, `DT\|UTR/NEU\|SIN\|DEF`, `DT\|UTR/NEU\|SIN\|IND`, `DT\|UTR\|SIN\|DEF`, `DT\|UTR\|SIN\|IND`, `DT\|UTR\|SIN\|IND/DEF`, `HA`, `HD\|NEU\|SIN\|IND`, `HD\|UTR/NEU\|PLU\|IND`, `HD\|UTR\|SIN\|IND`, `HP\|-\|-\|-`, `HP\|NEU\|SIN\|IND`, `HP\|UTR/NEU\|PLU\|IND`, `HP\|UTR\|SIN\|IND`, `HS\|DEF`, `IE`, `IN`, `JJ`, `JJ\|AN`, `JJ\|KOM\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|MAS\|SIN\|DEF\|GEN`, `JJ\|POS\|MAS\|SIN\|DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|SIN\|DEF\|NOM`, `JJ\|POS\|UTR\|-\|-\|SMS`, `JJ\|POS\|UTR\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|UTR\|SIN\|IND\|GEN`, `JJ\|POS\|UTR\|SIN\|IND\|NOM`, `JJ\|SUV\|MAS\|SIN\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|IND\|NOM`, `KN`, `MAD`, `MID`, `NN`, `NN\|-\|-\|-\|-`, `NN\|AN`, `NN\|NEU\|-\|-\|SMS`, `NN\|NEU\|PLU\|DEF\|GEN`, `NN\|NEU\|PLU\|DEF\|NOM`, `NN\|NEU\|PLU\|IND\|GEN`, `NN\|NEU\|PLU\|IND\|NOM`, `NN\|NEU\|SIN\|DEF\|GEN`, `NN\|NEU\|SIN\|DEF\|NOM`, `NN\|NEU\|SIN\|IND`, `NN\|NEU\|SIN\|IND\|GEN`, `NN\|NEU\|SIN\|IND\|NOM`, `NN\|SMS`, `NN\|UTR\|-\|-\|-`, `NN\|UTR\|-\|-\|SMS`, `NN\|UTR\|PLU\|DEF\|GEN`, `NN\|UTR\|PLU\|DEF\|NOM`, `NN\|UTR\|PLU\|IND\|GEN`, `NN\|UTR\|PLU\|IND\|NOM`, `NN\|UTR\|SIN\|DEF\|GEN`, `NN\|UTR\|SIN\|DEF\|NOM`, `NN\|UTR\|SIN\|IND\|GEN`, `NN\|UTR\|SIN\|IND\|NOM`, `PAD`, `PC\|PRF\|NEU\|SIN\|IND\|NOM`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `PC\|PRF\|UTR/NEU\|SIN\|DEF\|NOM`, `PC\|PRF\|UTR\|SIN\|IND\|NOM`, `PC\|PRS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `PL`, `PM`, `PM\|GEN`, `PM\|NOM`, `PM\|SMS`, `PN\|MAS\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|DEF`, `PN\|NEU\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|SUB`, `PN\|UTR/NEU\|PLU\|DEF\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|SIN/PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|NOM`, `PN\|UTR\|SIN\|DEF\|OBJ`, `PN\|UTR\|SIN\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|SUB/OBJ`, `PN\|UTR\|SIN\|IND\|NOM`, `PN\|UTR\|SIN\|IND\|SUB`, `PN\|UTR\|SIN\|IND\|SUB/OBJ`, `PP`, `PS\|NEU\|SIN\|DEF`, `PS\|UTR/NEU\|PLU\|DEF`, `PS\|UTR/NEU\|SIN/PLU\|DEF`, `PS\|UTR\|SIN\|DEF`, `RG\|NEU\|SIN\|IND\|NOM`, `RG\|NOM`, `RG\|SMS`, `RG\|UTR\|SIN\|IND\|NOM`, `RO\|MAS\|SIN\|IND/DEF\|NOM`, `RO\|NOM`, `SN`, `UO`, `VB\|AN`, `VB\|IMP\|AKT`, `VB\|IMP\|SFO`, `VB\|INF\|AKT`, `VB\|INF\|SFO`, `VB\|KON\|PRS\|AKT`, `VB\|KON\|PRT\|AKT`, `VB\|PRS\|AKT`, `VB\|PRS\|SFO`, `VB\|PRT\|AKT`, `VB\|PRT\|SFO`, `VB\|SUP\|AKT`, `VB\|SUP\|SFO` |
| **`morphologizer`** | `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `POS=PUNCT`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|POS=ADV`, `POS=SCONJ`, `POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Com\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=CCONJ`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Pos\|POS=ADV`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=VERB\|VerbForm=Sup\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Degree=Pos\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Case=Nom\|NumType=Card\|POS=NUM`, `Abbr=Yes\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Sup\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `POS=AUX\|VerbForm=Sup\|Voice=Act`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=VERB\|VerbForm=Sup\|Voice=Pass`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|POS=ADJ\|Tense=Pres\|VerbForm=Part`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Dem`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `Case=Nom\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Int`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|POS=PROPN`, `POS=PROPN`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=VERB\|VerbForm=Sup`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=SYM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Degree=Sup\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Sub\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|Gender=Com\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|POS=DET\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Abbr=Yes\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `NumType=Card\|POS=NUM`, `POS=INTJ`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int`, `Degree=Sup\|POS=ADV\|Polarity=Neg`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Int`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Ind`, `Foreign=Yes\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Dem`, `Abbr=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `Foreign=Yes\|POS=CCONJ`, `POS=DET\|PronType=Art`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Degree=Pos\|POS=ADV\|Polarity=Neg`, `Mood=Sub\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PRON\|PronType=Ind`, `Definite=Ind\|POS=DET\|PronType=Neg`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Neg`, `POS=CCONJ\|Polarity=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Imp\|POS=AUX\|VerbForm=Fin\|Voice=Act`, `Foreign=Yes\|POS=ADV`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rcp`, `Case=Acc\|Definite=Def\|POS=PRON\|Polarity=Neg\|PronType=Ind` |
| **`parser`** | `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `expl`, `fixed`, `flat:name`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `6`, `8`, `10`, `13`, `15`, `17`, `18`, `20`, `22`, `24`, `27`, `30`, `32`, `34`, `37`, `39`, `41`, `43`, `45`, `47`, `50`, `54`, `56`, `60`, `62`, `64`, `66`, `68`, `70`, `72`, `0`, `73`, `76`, `77`, `79`, `81`, `83`, `85`, `87`, `88`, `90`, `92`, `94`, `97`, `99`, `102`, `104`, `105`, `107`, `108`, `109`, `110`, `111`, `112`, `114`, `116`, `117`, `119`, `120`, `122`, `123`, `125`, `126`, `129`, `130`, `131`, `134`, `139`, `140`, `141`, `143`, `146`, `148`, `149`, `151`, `153`, `155`, `157`, `158`, `160`, `162`, `164`, `166`, `167`, `169`, `173`, `176`, `178`, `179`, `181`, `182`, `183`, `184`, `186`, `189`, `193`, `195`, `197`, `198`, `199`, `203`, `204`, `205`, `206`, `207`, `208`, `209`, `210`, `212`, `215`, `217`, `218`, `219`, `222`, `224`, `225`, `227`, `229`, `232`, `233`, `234`, `236`, `238`, `240`, `241`, `243`, `246`, `248`, `249`, `250`, `252`, `255`, `257`, `260`, `262`, `264`, `265`, `266`, `269`, `271`, `274`, `276`, `278`, `279`, `281`, `282`, `285`, `286`, `288`, `290`, `292`, `293`, `295`, `296`, `298`, `300`, `301`, `302`, `303`, `304`, `305`, `306`, `307`, `309`, `311`, `312`, `315`, `316`, `317`, `319`, `322`, `323`, `324`, `326`, `329`, `331`, `333`, `334`, `335`, `337`, `339`, `341`, `342`, `343`, `345`, `347`, `348`, `350`, `351`, `353`, `354`, `356`, `357`, `359`, `360`, `362`, `363`, `365`, `369`, `372`, `374`, `377`, `378`, `380`, `381`, `383`, `384`, `386`, `388`, `389`, `390`, `392`, `395`, `397`, `398`, `399`, `401`, `402`, `403`, `404`, `405`, `406`, `407`, `408`, `410`, `411`, `412`, `413`, `414`, `415`, `418`, `419`, `420`, `421`, `423`, `424`, `425`, `426`, `428`, `430`, `431`, `432`, `433`, `434`, `436`, `440`, `442`, `444`, `446`, `448`, `449`, `453`, `454`, `457`, `458`, `459`, `460`, `462`, `463`, `464`, `466`, `468`, `469`, `471`, `472`, `474`, `475`, `478`, `479`, `480`, `481`, `482`, `483`, `486`, `487`, `488`, `489`, `490`, `492`, `494`, `495`, `498`, `500`, `501`, `502`, `503`, `504`, `506`, `507`, `508`, `509`, `513`, `514`, `516`, `517`, `519`, `520`, `521`, `522`, `523`, `525`, `526`, `528`, `530`, `534`, `536`, `537`, `538`, `539`, `540`, `543`, `545`, `547`, `549`, `550`, `551`, `552`, `554`, `555`, `557`, `559`, `560`, `562`, `565`, `568`, `571`, `574`, `575`, `576`, `577`, `578`, `582`, `583`, `585`, `586`, `588`, `589`, `591`, `592`, `594`, `596`, `598`, `601`, `602`, `604`, `605`, `606`, `607`, `608`, `609`, `610`, `611`, `612`, `613`, `615`, `616`, `617`, `618`, `620`, `622`, `623`, `624`, `625`, `627`, `628`, `629`, `631`, `633`, `635`, `637`, `638`, `640`, `641`, `644`, `645`, `649`, `650`, `652`, `653`, `655`, `656`, `658`, `660`, `662`, `663`, `664`, `666`, `669`, `671`, `672`, `676`, `677`, `680`, `681`, `682`, `685`, `687`, `688`, `690`, `691`, `693`, `694`, `696`, `697`, `698`, `699`, `700`, `702`, `703`, `704`, `706`, `709`, `711`, `712`, `713`, `714`, `715`, `716`, `718`, `719`, `720`, `723`, `724`, `726`, `728`, `730`, `731`, `732`, `734`, `735`, `736`, `737`, `738`, `739`, `740`, `742`, `743`, `745`, `746`, `748`, `750`, `751`, `752`, `753`, `754`, `756`, `757`, `758`, `760`, `762`, `763`, `764`, `765`, `766`, `767`, `768`, `769`, `770`, `771`, `772`, `774`, `776`, `777`, `779`, `780`, `781`, `782`, `783`, `784`, `785`, `787`, `788`, `789`, `790`, `791`, `793`, `794`, `797`, `799`, `801`, `802`, `803`, `806`, `808`, `809`, `810`, `812`, `813`, `815`, `816`, `817`, `819`, `820`, `822`, `824`, `825`, `826`, `828`, `829`, `832`, `833`, `835`, `837`, `839`, `840`, `841`, `842`, `843`, `845`, `846`, `849`, `851`, `854`, `857`, `858`, `861`, `862`, `863`, `865`, `866`, `867`, `868`, `869`, `870`, `871`, `873`, `875`, `876`, `878`, `880`, `883`, `884`, `887`, `888`, `889`, `890`, `891`, `893`, `894`, `897`, `898`, `529`, `900`, `901`, `902`, `903`, `904`, `905`, `906`, `909`, `911`, `913`, `914`, `915`, `916`, `918`, `919`, `920`, `922`, `923`, `925`, `926`, `927`, `928`, `929`, `931`, `932`, `934`, `936`, `938`, `939`, `940`, `941`, `942`, `943`, `944`, `945`, `946`, `947`, `948`, `949`, `950`, `952`, `953`, `954`, `956`, `957`, `958`, `959`, `961`, `962`, `965`, `967`, `968`, `970`, `971`, `972`, `973`, `976`, `977`, `979`, `982`, `983`, `984`, `985`, `986`, `988`, `989`, `990`, `993`, `994`, `996`, `998`, `999`, `1001`, `1002`, `1003`, `1005`, `1006`, `1007`, `1009`, `1010`, `1012`, `1016`, `1018`, `1020`, `1021`, `1023`, `1024`, `1026`, `1027`, `1029`, `1030`, `1031`, `1032`, `1033`, `1034`, `1036`, `1037`, `1038`, `1040`, `1042`, `1044`, `223`, `1045`, `1046`, `1049`, `1052`, `1054`, `1057`, `1058`, `1061`, `1062`, `1063`, `1064`, `1065`, `1067`, `1068`, `1069`, `1070`, `1071`, `1072`, `1074`, `1077`, `1079`, `1080`, `1081`, `1083`, `1084`, `1086`, `1087`, `1088`, `1090`, `1092`, `1093`, `1094`, `1095`, `1096`, `1097`, `1098`, `1099`, `1100`, `1102`, `1105`, `1106`, `1107`, `1109`, `1110`, `1111`, `1112`, `1113`, `1114`, `1115`, `1116`, `1117`, `1118`, `1121`, `1123`, `1126`, `1128`, `1129`, `1130`, `1131`, `1132`, `1133`, `1135`, `1136`, `1137`, `1138`, `1139`, `1141`, `1142`, `1143`, `1144`, `1145`, `1148`, `1149`, `1150`, `1152`, `1154`, `1155`, `1157`, `1158`, `1159`, `1160`, `1162`, `1163`, `1164`, `1166`, `1167`, `1168`, `1170`, `1173`, `1174`, `1176`, `1178`, `1179`, `1180`, `1182`, `1183`, `1184`, `1186`, `1187`, `1188`, `1191`, `1192`, `1193`, `1194`, `1195`, `1196`, `1197`, `1198`, `1199`, `1200`, `1201`, `1203`, `1204`, `1206`, `1207`, `1209`, `1211`, `1212`, `1213`, `1214`, `1215`, `1216`, `1218`, `1219`, `1220`, `1221`, `1224`, `1225`, `1227`, `1228`, `1229`, `1231`, `1232`, `1233`, `1235`, `1237`, `1240`, `1243`, `1246`, `1248`, `1249`, `1251`, `1252`, `1254`, `1257`, `1258`, `1260`, `1263`, `1264`, `1265`, `1267`, `1269`, `1270`, `1272`, `1273`, `1275`, `1276`, `1278`, `1280`, `1281`, `1282`, `1284`, `297`, `1285`, `1287`, `1289`, `1291`, `1292`, `1293`, `1294`, `1295`, `1297`, `1299`, `1301`, `1303`, `1305`, `1308`, `1309`, `1310`, `1312` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.95 |
| `TOKEN_P` | 99.95 |
| `TOKEN_R` | 99.96 |
| `TOKEN_ACC` | 99.99 |
| `SENTS_F` | 98.02 |
| `SENTS_P` | 98.02 |
| `SENTS_R` | 98.02 |
| `TAG_ACC` | 97.87 |
| `POS_ACC` | 98.83 |
| `MORPH_ACC` | 97.97 |
| `DEP_UAS` | 92.14 |
| `DEP_LAS` | 89.39 |
| `LEMMA_ACC` | 97.37 |
|
{"language": ["sv"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/sv_udv25_swedishtalbanken_trf
|
[
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sv"
] |
TAGS
#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Swedish-Talbanken
### Label Scheme
View label scheme (1206 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1206 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1206 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (1206 labels for 6 components)### Accuracy"
] |
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] |
null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Vietnamese-VTB
| Feature | Description |
| --- | --- |
| **Name** | `vi_udv25_vietnamesevtb_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (81 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `!`, `"`, `,`, `-`, `.`, `...`, `:`, `;`, `?`, `@`, `A`, `C`, `CC`, `E`, `I`, `L`, `LBKT`, `M`, `N`, `NP`, `Nb`, `Nc`, `Np`, `Nu`, `Ny`, `P`, `R`, `RBKT`, `T`, `V`, `VP`, `X`, `Y`, `Z` |
| **`morphologizer`** | `POS=NOUN`, `POS=ADP`, `POS=X\|Polarity=Neg`, `POS=VERB`, `POS=ADJ`, `POS=PUNCT`, `POS=X`, `POS=SCONJ`, `NumType=Card\|POS=NUM`, `POS=DET`, `POS=CCONJ`, `POS=PROPN`, `POS=AUX`, `POS=PART`, `POS=INTJ` |
| **`parser`** | `ROOT`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 87.90 |
| `TOKEN_P` | 86.84 |
| `TOKEN_R` | 89.00 |
| `TOKEN_ACC` | 98.42 |
| `SENTS_F` | 94.33 |
| `SENTS_P` | 96.23 |
| `SENTS_R` | 92.50 |
| `TAG_ACC` | 88.05 |
| `POS_ACC` | 90.19 |
| `MORPH_ACC` | 96.95 |
| `DEP_UAS` | 68.08 |
| `DEP_LAS` | 60.64 |
| `LEMMA_ACC` | 89.35 |
|
{"language": ["vi"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/vi_udv25_vietnamesevtb_trf
|
[
"spacy",
"token-classification",
"vi",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"vi"
] |
TAGS
#spacy #token-classification #vi #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Vietnamese-VTB
### Label Scheme
View label scheme (81 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (81 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #vi #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (81 labels for 6 components)",
"### Accuracy"
] |
[
32,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #vi #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (81 labels for 6 components)### Accuracy"
] |
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null | null |
spacy
|
UD v2.5 benchmarking pipeline for UD_Old_French-SRCMF
| Feature | Description |
| --- | --- |
| **Name** | `xx_udv25_oldfrenchsrcmf_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (16214 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJQUA`, `ADJcar`, `ADJind`, `ADJord`, `ADJpos`, `ADJqua`, `ADVgen`, `ADVgen.PROadv`, `ADVgen.PROper`, `ADVing`, `ADVint`, `ADVneg`, `ADVneg.PROper`, `ADVsub`, `CONcoo`, `CONsub`, `CONsub.PROper`, `CONsub_o`, `CONsub_pre`, `DETcar`, `DETdef`, `DETdem`, `DETind`, `DETint`, `DETndf`, `DETord`, `DETpos`, `DETrel`, `DETrel_o`, `ETR`, `INJ`, `NOMcom`, `NOMcom.PROper`, `NOMpro`, `PRE`, `PRE.DETdef`, `PRE.PROdem`, `PRE.PROper`, `PROadv`, `PROcar`, `PROdem`, `PROimp`, `PROind`, `PROint`, `PROint.PROper`, `PROint_adv`, `PROord`, `PROper`, `PROper.PROper`, `PROpos`, `PROrel`, `PROrel.ADVneg`, `PROrel.PROadv`, `PROrel.PROper`, `PROrel_adv`, `RED`, `VERcjg`, `VERinf`, `VERppa`, `VERppe` |
| **`morphologizer`** | `POS=CCONJ`, `Definite=Def\|POS=DET\|PronType=Art`, `POS=NOUN`, `POS=PRON\|PronType=Prs`, `POS=VERB\|VerbForm=Fin`, `POS=PROPN`, `POS=PRON\|PronType=Prs,Rel`, `POS=ADV`, `POS=ADP`, `POS=ADV\|PronType=Dem`, `POS=PRON\|PronType=Dem`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=AUX\|VerbForm=Fin`, `POS=DET\|PronType=Int`, `POS=ADJ`, `POS=PRON\|PronType=Ind`, `POS=DET\|PronType=Ind`, `Morph=VPar\|POS=ADJ`, `POS=DET\|Poss=Yes`, `POS=ADV\|Polarity=Neg`, `Definite=Def\|POS=ADP\|PronType=Art`, `POS=PRON\|PronType=Int`, `POS=SCONJ`, `POS=VERB\|VerbForm=Inf`, `NumType=Card\|POS=PRON`, `POS=PRON`, `NumType=Card\|POS=DET`, `POS=PRON\|Polarity=Neg\|PronType=Prs`, `POS=ADJ\|Poss=Yes`, `POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|POS=DET\|PronType=Art`, `POS=DET\|PronType=Dem`, `POS=AUX\|VerbForm=Inf`, `POS=ADJ\|PronType=Ind`, `Morph=VPar\|POS=NOUN`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Morph=VPar\|POS=PROPN`, `Morph=VInf\|POS=NOUN`, `NumType=Ord\|POS=PRON`, `POS=INTJ`, `POS=SCONJ\|PronType=Prs`, `Morph=VFin\|POS=NOUN`, `POS=DET\|PronType=Rel`, `NumType=Card\|POS=ADJ`, `POS=ADJ\|PronType=Ord`, `Morph=VFin\|POS=ADV`, `Morph=VFin\|POS=PROPN`, `POS=DET`, `Morph=VPar\|POS=ADP`, `Morph=VPar\|POS=ADV`, `NumType=Ord\|POS=DET`, `Morph=VFin\|POS=ADP`, `Morph=VFin\|POS=CCONJ`, `Morph=VInf\|POS=ADJ`, `POS=ADP\|PronType=Dem`, `POS=ADV\|Polarity=Int`, `Morph=VFin\|POS=INTJ` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `case:det`, `cc`, `cc:nc`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `dislocated`, `expl`, `flat`, `iobj`, `mark`, `mark:advmod`, `mark:obj`, `mark:obl`, `nmod`, `nsubj`, `nsubj:obj`, `nummod`, `obj`, `obj:advmod`, `obl`, `obl:advmod`, `parataxis`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9`, `10`, `11`, `12`, `13`, `14`, `15`, `16`, `17`, `18`, `19`, `20`, `21`, `22`, `23`, `24`, `25`, `26`, `27`, `28`, `29`, `30`, `31`, `32`, `33`, `34`, `35`, `36`, `37`, `38`, `39`, `40`, `41`, `42`, `43`, `44`, `45`, `46`, `47`, `48`, `49`, `50`, `51`, `52`, `53`, `54`, `55`, `56`, `57`, `58`, `59`, `60`, `61`, `62`, `63`, `64`, `65`, `66`, `67`, `68`, `69`, `70`, `71`, `72`, `73`, `74`, `75`, `76`, `77`, `78`, `79`, `80`, `81`, `82`, `83`, `84`, `85`, `86`, `87`, `88`, `89`, `90`, `91`, `92`, `93`, `94`, `95`, `96`, `97`, `98`, `99`, `100`, `101`, `102`, `103`, `104`, `105`, `106`, `107`, `108`, `109`, `110`, `111`, `112`, `113`, `114`, `115`, `116`, `117`, `118`, `119`, `120`, `121`, `122`, `123`, `124`, `125`, `126`, `127`, `128`, `129`, `130`, `131`, `132`, `133`, `134`, `135`, `136`, `137`, `138`, `139`, `140`, `141`, `142`, `143`, `144`, `145`, `146`, `147`, `148`, `149`, `150`, `151`, 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`15227`, `15228`, `15229`, `15230`, `15231`, `15232`, `15233`, `15234`, `15235`, `15236`, `15237`, `15238`, `15239`, `15240`, `15241`, `15242`, `15243`, `15244`, `15245`, `15246`, `15247`, `15248`, `15249`, `15250`, `15251`, `15252`, `15253`, `15254`, `15255`, `15256`, `15257`, `15258`, `15259`, `15260`, `15261`, `15262`, `15263`, `15264`, `15265`, `15266`, `15267`, `15268`, `15269`, `15270`, `15271`, `15272`, `15273`, `15274`, `15275`, `15276`, `15277`, `15278`, `15279`, `15280`, `15281`, `15282`, `15283`, `15284`, `15285`, `15286`, `15287`, `15288`, `15289`, `15290`, `15291`, `15292`, `15293`, `15294`, `15295`, `15296`, `15297`, `15298`, `15299`, `15300`, `15301`, `15302`, `15303`, `15304`, `15305`, `15306`, `15307`, `15308`, `15309`, `15310`, `15311`, `15312`, `15313`, `15314`, `15315`, `15316`, `15317`, `15318`, `15319`, `15320`, `15321`, `15322`, `15323`, `15324`, `15325`, `15326`, `15327`, `15328`, `15329`, `15330`, `15331`, `15332`, `15333`, `15334`, `15335`, `15336`, `15337`, `15338`, `15339`, `15340`, `15341`, `15342`, `15343`, `15344`, `15345`, `15346`, `15347`, `15348`, `15349`, `15350`, `15351`, `15352`, `15353`, `15354`, `15355`, `15356`, `15357`, `15358`, `15359`, `15360`, `15361`, `15362`, `15363`, `15364`, `15365`, `15366`, `15367`, `15368`, `15369`, `15370`, `15371`, `15372`, `15373`, `15374`, `15375`, `15376`, `15377`, `15378`, `15379`, `15380`, `15381`, `15382`, `15383`, `15384`, `15385`, `15386`, `15387`, `15388`, `15389`, `15390`, `15391`, `15392`, `15393`, `15394`, `15395`, `15396`, `15397`, `15398`, `15399`, `15400`, `15401`, `15402`, `15403`, `15404`, `15405`, `15406`, `15407`, `15408`, `15409`, `15410`, `15411`, `15412`, `15413`, `15414`, `15415`, `15416`, `15417`, `15418`, `15419`, `15420`, `15421`, `15422`, `15423`, `15424`, `15425`, `15426`, `15427`, `15428`, `15429`, `15430`, `15431`, `15432`, `15433`, `15434`, `15435`, `15436`, `15437`, `15438`, `15439`, `15440`, `15441`, `15442`, `15443`, `15444`, `15445`, `15446`, `15447`, `15448`, 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`16004`, `16005`, `16006`, `16007`, `16008`, `16009`, `16010`, `16011`, `16012`, `16013`, `16014`, `16015`, `16016`, `16017`, `16018`, `16019`, `16020`, `16021`, `16022`, `16023`, `16024`, `16025`, `16026`, `16027`, `16028`, `16029`, `16030`, `16031`, `16032`, `16033`, `16034`, `16035`, `16036`, `16037`, `16038`, `16039`, `16040`, `16041`, `16042`, `16043`, `16044`, `16045`, `16046`, `16047`, `16048`, `16049`, `16050`, `16051`, `16052`, `16053`, `16054`, `16055`, `16056`, `16057`, `16058` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 100.00 |
| `TOKEN_P` | 100.00 |
| `TOKEN_R` | 100.00 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 81.11 |
| `SENTS_P` | 79.75 |
| `SENTS_R` | 82.52 |
| `TAG_ACC` | 96.41 |
| `POS_ACC` | 96.52 |
| `MORPH_ACC` | 97.74 |
| `DEP_UAS` | 90.21 |
| `DEP_LAS` | 85.42 |
| `LEMMA_ACC` | 90.34 |
|
{"language": ["multilingual"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
explosion/xx_udv25_oldfrenchsrcmf_trf
|
[
"spacy",
"token-classification",
"multilingual",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual"
] |
TAGS
#spacy #token-classification #multilingual #license-cc-by-sa-4.0 #model-index #region-us
|
UD v2.5 benchmarking pipeline for UD\_Old\_French-SRCMF
### Label Scheme
View label scheme (16214 labels for 6 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (16214 labels for 6 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #multilingual #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (16214 labels for 6 components)",
"### Accuracy"
] |
[
34,
18,
5
] |
[
"passage: TAGS\n#spacy #token-classification #multilingual #license-cc-by-sa-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (16214 labels for 6 components)### Accuracy"
] |
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] |
null | null |
transformers
|
#peppa pig chat bot
|
{"tags": ["conversational"]}
|
text-generation
|
f00d4tehg0dz/Peppa
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#peppa pig chat bot
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
#yoda chat bot
|
{"tags": ["conversational"]}
|
text-generation
|
f00d4tehg0dz/Yoda
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#yoda chat bot
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
56
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# Italian Legal Named Entity Recognition (NER)
ELECTRA-based model trained to extract entities of interest from Italian civil judgments issued by the Corte Suprema di Cassazione.
## Dataset
The model has been fine-tuned on 9000 judgments from 2016 to 2021 (1500 per year), labeled with a combination of rule-based and manual approaches.
It can be used to extract the following named entities from the text.
| Tag | Italian name | English name |
|:----|:-------------|:-------------|
| RIC | ricorso | appeal |
| RCR | ricorrente | petitioner |
| CTR | controricorrente | respondent |
| AVV | avvocato | lawyer |
| CNS | consigliere | counselor |
| PMI | pubblico ministero | prosecutor |
| DOM | domicilio | domicile |
| CDA | corte d’appello | appeal court |
| SNT | sentenza | judgment|
|
{"language": ["it"], "tags": ["legal"], "widget": [{"text": "la seguente SENTENZA sul ricorso 24817-2015 proposto da: ANDREA FORMISANO, elettivamente domiciliato in ROMA VIA S. TOMMASO D'AQUINO 7, presso lo studio dell'avvocato CARLO BORELLO, che lo rappresenta e difende giusta delega in calce; - ricorrente - contro SOGET SPA, CAMERA DI COMMERCIO DI PESCARA; - intimati - avverso la sentenza n. 169/2012 della COMM.TRIB.REG.SEZ.DIST. di PESCARA, depositata il 13/03/2012; udita la relazione della causa svolta nella pubblica udienza del 04/04/2018 dal Consigliere Dott. MILENA BALSAMO; udito il P.M. in persona del Sostituto Procuratore Generale Dott. GIOVANNI GIACALONE che ha concluso per l'inammissibilit\u00e0 in subordine rigetto del ricorso.", "example_title": "Judgment example"}]}
|
token-classification
|
fabiod20/italian-legal-ner
|
[
"transformers",
"pytorch",
"electra",
"token-classification",
"legal",
"it",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"it"
] |
TAGS
#transformers #pytorch #electra #token-classification #legal #it #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Italian Legal Named Entity Recognition (NER)
============================================
ELECTRA-based model trained to extract entities of interest from Italian civil judgments issued by the Corte Suprema di Cassazione.
Dataset
-------
The model has been fine-tuned on 9000 judgments from 2016 to 2021 (1500 per year), labeled with a combination of rule-based and manual approaches.
It can be used to extract the following named entities from the text.
|
[] |
[
"TAGS\n#transformers #pytorch #electra #token-classification #legal #it #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
46
] |
[
"passage: TAGS\n#transformers #pytorch #electra #token-classification #legal #it #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on the wmt16 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "opus-mt-en-de-finetuned-en-to-de-wd01-fp16false", "results": []}]}
|
text2text-generation
|
fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
[
"# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] |
[
61,
61,
6,
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8,
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103,
30
] |
[
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-ag_news
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3284
- Accuracy: 0.9375
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 7425
- training_steps: 74250
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5773 | 0.13 | 2000 | 0.3627 | 0.8875 |
| 0.3101 | 0.27 | 4000 | 0.2938 | 0.9208 |
| 0.3076 | 0.4 | 6000 | 0.3114 | 0.9092 |
| 0.3114 | 0.54 | 8000 | 0.4545 | 0.9008 |
| 0.3154 | 0.67 | 10000 | 0.3875 | 0.9083 |
| 0.3095 | 0.81 | 12000 | 0.3390 | 0.9142 |
| 0.2948 | 0.94 | 14000 | 0.3341 | 0.9133 |
| 0.2557 | 1.08 | 16000 | 0.4573 | 0.9092 |
| 0.258 | 1.21 | 18000 | 0.3356 | 0.9217 |
| 0.2455 | 1.35 | 20000 | 0.3348 | 0.9283 |
| 0.2361 | 1.48 | 22000 | 0.3218 | 0.93 |
| 0.254 | 1.62 | 24000 | 0.3814 | 0.9033 |
| 0.2528 | 1.75 | 26000 | 0.3628 | 0.9158 |
| 0.2282 | 1.89 | 28000 | 0.3302 | 0.9308 |
| 0.224 | 2.02 | 30000 | 0.3967 | 0.9225 |
| 0.174 | 2.15 | 32000 | 0.3669 | 0.9333 |
| 0.1848 | 2.29 | 34000 | 0.3435 | 0.9283 |
| 0.19 | 2.42 | 36000 | 0.3552 | 0.93 |
| 0.1865 | 2.56 | 38000 | 0.3996 | 0.9258 |
| 0.1877 | 2.69 | 40000 | 0.3749 | 0.9258 |
| 0.1951 | 2.83 | 42000 | 0.3963 | 0.9258 |
| 0.1702 | 2.96 | 44000 | 0.3655 | 0.9317 |
| 0.1488 | 3.1 | 46000 | 0.3942 | 0.9292 |
| 0.1231 | 3.23 | 48000 | 0.3998 | 0.9267 |
| 0.1319 | 3.37 | 50000 | 0.4292 | 0.9242 |
| 0.1334 | 3.5 | 52000 | 0.4904 | 0.9192 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["ag_news"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-ag_news", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ag_news", "type": "ag_news", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9375, "name": "Accuracy"}]}]}]}
|
text-classification
|
fabriceyhc/bert-base-uncased-ag_news
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:ag_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-ag_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-ag\_news
==========================
This model is a fine-tuned version of bert-base-uncased on the ag\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3284
* Accuracy: 0.9375
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 7425
* training\_steps: 74250
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 7425\n* training\\_steps: 74250",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-ag_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 7425\n* training\\_steps: 74250",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
65,
117,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-ag_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 7425\n* training\\_steps: 74250### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-amazon_polarity
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2945
- Accuracy: 0.9465
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1782000
- training_steps: 17820000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.7155 | 0.0 | 2000 | 0.7060 | 0.4622 |
| 0.7054 | 0.0 | 4000 | 0.6925 | 0.5165 |
| 0.6842 | 0.0 | 6000 | 0.6653 | 0.6116 |
| 0.6375 | 0.0 | 8000 | 0.5721 | 0.7909 |
| 0.4671 | 0.0 | 10000 | 0.3238 | 0.8770 |
| 0.3403 | 0.0 | 12000 | 0.3692 | 0.8861 |
| 0.4162 | 0.0 | 14000 | 0.4560 | 0.8908 |
| 0.4728 | 0.0 | 16000 | 0.5071 | 0.8980 |
| 0.5111 | 0.01 | 18000 | 0.5204 | 0.9015 |
| 0.4792 | 0.01 | 20000 | 0.5193 | 0.9076 |
| 0.544 | 0.01 | 22000 | 0.4835 | 0.9133 |
| 0.4745 | 0.01 | 24000 | 0.4689 | 0.9170 |
| 0.4403 | 0.01 | 26000 | 0.4778 | 0.9177 |
| 0.4405 | 0.01 | 28000 | 0.4754 | 0.9163 |
| 0.4375 | 0.01 | 30000 | 0.4808 | 0.9175 |
| 0.4628 | 0.01 | 32000 | 0.4340 | 0.9244 |
| 0.4488 | 0.01 | 34000 | 0.4162 | 0.9265 |
| 0.4608 | 0.01 | 36000 | 0.4031 | 0.9271 |
| 0.4478 | 0.01 | 38000 | 0.4502 | 0.9253 |
| 0.4237 | 0.01 | 40000 | 0.4087 | 0.9279 |
| 0.4601 | 0.01 | 42000 | 0.4133 | 0.9269 |
| 0.4153 | 0.01 | 44000 | 0.4230 | 0.9306 |
| 0.4096 | 0.01 | 46000 | 0.4108 | 0.9301 |
| 0.4348 | 0.01 | 48000 | 0.4138 | 0.9309 |
| 0.3787 | 0.01 | 50000 | 0.4066 | 0.9324 |
| 0.4172 | 0.01 | 52000 | 0.4812 | 0.9206 |
| 0.3897 | 0.02 | 54000 | 0.4013 | 0.9325 |
| 0.3787 | 0.02 | 56000 | 0.3837 | 0.9344 |
| 0.4253 | 0.02 | 58000 | 0.3925 | 0.9347 |
| 0.3959 | 0.02 | 60000 | 0.3907 | 0.9353 |
| 0.4402 | 0.02 | 62000 | 0.3708 | 0.9341 |
| 0.4115 | 0.02 | 64000 | 0.3477 | 0.9361 |
| 0.3876 | 0.02 | 66000 | 0.3634 | 0.9373 |
| 0.4286 | 0.02 | 68000 | 0.3778 | 0.9378 |
| 0.422 | 0.02 | 70000 | 0.3540 | 0.9361 |
| 0.3732 | 0.02 | 72000 | 0.3853 | 0.9378 |
| 0.3641 | 0.02 | 74000 | 0.3951 | 0.9386 |
| 0.3701 | 0.02 | 76000 | 0.3582 | 0.9388 |
| 0.4498 | 0.02 | 78000 | 0.3268 | 0.9375 |
| 0.3587 | 0.02 | 80000 | 0.3825 | 0.9401 |
| 0.4474 | 0.02 | 82000 | 0.3155 | 0.9391 |
| 0.3598 | 0.02 | 84000 | 0.3666 | 0.9388 |
| 0.389 | 0.02 | 86000 | 0.3745 | 0.9377 |
| 0.3625 | 0.02 | 88000 | 0.3776 | 0.9387 |
| 0.3511 | 0.03 | 90000 | 0.4275 | 0.9336 |
| 0.3428 | 0.03 | 92000 | 0.4301 | 0.9336 |
| 0.4042 | 0.03 | 94000 | 0.3547 | 0.9359 |
| 0.3583 | 0.03 | 96000 | 0.3763 | 0.9396 |
| 0.3887 | 0.03 | 98000 | 0.3213 | 0.9412 |
| 0.3915 | 0.03 | 100000 | 0.3557 | 0.9409 |
| 0.3378 | 0.03 | 102000 | 0.3627 | 0.9418 |
| 0.349 | 0.03 | 104000 | 0.3614 | 0.9402 |
| 0.3596 | 0.03 | 106000 | 0.3834 | 0.9381 |
| 0.3519 | 0.03 | 108000 | 0.3560 | 0.9421 |
| 0.3598 | 0.03 | 110000 | 0.3485 | 0.9419 |
| 0.3642 | 0.03 | 112000 | 0.3754 | 0.9395 |
| 0.3477 | 0.03 | 114000 | 0.3634 | 0.9426 |
| 0.4202 | 0.03 | 116000 | 0.3071 | 0.9427 |
| 0.3656 | 0.03 | 118000 | 0.3155 | 0.9441 |
| 0.3709 | 0.03 | 120000 | 0.2923 | 0.9433 |
| 0.374 | 0.03 | 122000 | 0.3272 | 0.9441 |
| 0.3142 | 0.03 | 124000 | 0.3348 | 0.9444 |
| 0.3452 | 0.04 | 126000 | 0.3603 | 0.9436 |
| 0.3365 | 0.04 | 128000 | 0.3339 | 0.9434 |
| 0.3353 | 0.04 | 130000 | 0.3471 | 0.9450 |
| 0.343 | 0.04 | 132000 | 0.3508 | 0.9418 |
| 0.3174 | 0.04 | 134000 | 0.3753 | 0.9436 |
| 0.3009 | 0.04 | 136000 | 0.3687 | 0.9422 |
| 0.3785 | 0.04 | 138000 | 0.3818 | 0.9396 |
| 0.3199 | 0.04 | 140000 | 0.3291 | 0.9438 |
| 0.4049 | 0.04 | 142000 | 0.3372 | 0.9454 |
| 0.3435 | 0.04 | 144000 | 0.3315 | 0.9459 |
| 0.3814 | 0.04 | 146000 | 0.3462 | 0.9401 |
| 0.359 | 0.04 | 148000 | 0.3981 | 0.9361 |
| 0.3552 | 0.04 | 150000 | 0.3226 | 0.9469 |
| 0.345 | 0.04 | 152000 | 0.3731 | 0.9384 |
| 0.3228 | 0.04 | 154000 | 0.2956 | 0.9471 |
| 0.3637 | 0.04 | 156000 | 0.2869 | 0.9477 |
| 0.349 | 0.04 | 158000 | 0.3331 | 0.9430 |
| 0.3374 | 0.04 | 160000 | 0.4159 | 0.9340 |
| 0.3718 | 0.05 | 162000 | 0.3241 | 0.9459 |
| 0.315 | 0.05 | 164000 | 0.3544 | 0.9391 |
| 0.3215 | 0.05 | 166000 | 0.3311 | 0.9451 |
| 0.3464 | 0.05 | 168000 | 0.3682 | 0.9453 |
| 0.3495 | 0.05 | 170000 | 0.3193 | 0.9469 |
| 0.305 | 0.05 | 172000 | 0.4132 | 0.9389 |
| 0.3479 | 0.05 | 174000 | 0.3465 | 0.947 |
| 0.3537 | 0.05 | 176000 | 0.3277 | 0.9449 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["amazon_polarity"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-amazon_polarity", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_polarity", "type": "amazon_polarity", "args": "amazon_polarity"}, "metrics": [{"type": "accuracy", "value": 0.94647, "name": "Accuracy"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_polarity", "type": "amazon_polarity", "config": "amazon_polarity", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9464875, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.9528844934702675, "name": "Precision", "verified": true}, {"type": "recall", "value": 0.939425, "name": "Recall", "verified": true}, {"type": "auc", "value": 0.9863499156250001, "name": "AUC", "verified": true}, {"type": "f1", "value": 0.9461068798388619, "name": "F1", "verified": true}, {"type": "loss", "value": 0.2944573760032654, "name": "loss", "verified": true}]}]}]}
|
text-classification
|
fabriceyhc/bert-base-uncased-amazon_polarity
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:amazon_polarity",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-amazon_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-amazon\_polarity
==================================
This model is a fine-tuned version of bert-base-uncased on the amazon\_polarity dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2945
* Accuracy: 0.9465
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1782000
* training\_steps: 17820000
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1782000\n* training\\_steps: 17820000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-amazon_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1782000\n* training\\_steps: 17820000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
68,
118,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-amazon_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1782000\n* training\\_steps: 17820000### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-dbpedia_14
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the dbpedia_14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0547
- Accuracy: 0.9903
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 34650
- training_steps: 346500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7757 | 0.03 | 2000 | 0.2732 | 0.9880 |
| 0.1002 | 0.06 | 4000 | 0.0620 | 0.9891 |
| 0.0547 | 0.09 | 6000 | 0.0723 | 0.9879 |
| 0.0558 | 0.12 | 8000 | 0.0678 | 0.9875 |
| 0.0534 | 0.14 | 10000 | 0.0554 | 0.9896 |
| 0.0632 | 0.17 | 12000 | 0.0670 | 0.9888 |
| 0.0612 | 0.2 | 14000 | 0.0733 | 0.9873 |
| 0.0667 | 0.23 | 16000 | 0.0623 | 0.9896 |
| 0.0636 | 0.26 | 18000 | 0.0836 | 0.9868 |
| 0.0705 | 0.29 | 20000 | 0.0776 | 0.9855 |
| 0.0726 | 0.32 | 22000 | 0.0805 | 0.9861 |
| 0.0778 | 0.35 | 24000 | 0.0713 | 0.9870 |
| 0.0713 | 0.38 | 26000 | 0.1277 | 0.9805 |
| 0.0965 | 0.4 | 28000 | 0.0810 | 0.9855 |
| 0.0881 | 0.43 | 30000 | 0.0910 | 0.985 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["dbpedia_14"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-dbpedia_14", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "dbpedia_14", "type": "dbpedia_14", "args": "dbpedia_14"}, "metrics": [{"type": "accuracy", "value": 0.9902857142857143, "name": "Accuracy"}]}]}]}
|
text-classification
|
fabriceyhc/bert-base-uncased-dbpedia_14
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:dbpedia_14",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-dbpedia_14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-dbpedia\_14
=============================
This model is a fine-tuned version of bert-base-uncased on the dbpedia\_14 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0547
* Accuracy: 0.9903
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 34650
* training\_steps: 346500
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 34650\n* training\\_steps: 346500",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-dbpedia_14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 34650\n* training\\_steps: 346500",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
66,
118,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-dbpedia_14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 34650\n* training\\_steps: 346500### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-imdb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4942
- Accuracy: 0.9126
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1546
- training_steps: 15468
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3952 | 0.65 | 2000 | 0.4012 | 0.86 |
| 0.2954 | 1.29 | 4000 | 0.4535 | 0.892 |
| 0.2595 | 1.94 | 6000 | 0.4320 | 0.892 |
| 0.1516 | 2.59 | 8000 | 0.5309 | 0.896 |
| 0.1167 | 3.23 | 10000 | 0.4070 | 0.928 |
| 0.0624 | 3.88 | 12000 | 0.5055 | 0.908 |
| 0.0329 | 4.52 | 14000 | 0.4342 | 0.92 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.91264, "name": "Accuracy"}]}]}]}
|
text-classification
|
fabriceyhc/bert-base-uncased-imdb
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
bert-base-uncased-imdb
======================
This model is a fine-tuned version of bert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4942
* Accuracy: 0.9126
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1546
* training\_steps: 15468
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1546\n* training\\_steps: 15468",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1546\n* training\\_steps: 15468",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
68,
117,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1546\n* training\\_steps: 15468### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-yahoo_answers_topics
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the yahoo_answers_topics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8092
- Accuracy: 0.7499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 86625
- training_steps: 866250
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.162 | 0.01 | 2000 | 1.7444 | 0.5681 |
| 1.3126 | 0.02 | 4000 | 1.0081 | 0.7054 |
| 0.9592 | 0.03 | 6000 | 0.9021 | 0.7234 |
| 0.8903 | 0.05 | 8000 | 0.8827 | 0.7276 |
| 0.8685 | 0.06 | 10000 | 0.8540 | 0.7341 |
| 0.8422 | 0.07 | 12000 | 0.8547 | 0.7365 |
| 0.8535 | 0.08 | 14000 | 0.8264 | 0.7372 |
| 0.8178 | 0.09 | 16000 | 0.8331 | 0.7389 |
| 0.8325 | 0.1 | 18000 | 0.8242 | 0.7411 |
| 0.8181 | 0.12 | 20000 | 0.8356 | 0.7437 |
| 0.8171 | 0.13 | 22000 | 0.8090 | 0.7451 |
| 0.8092 | 0.14 | 24000 | 0.8469 | 0.7392 |
| 0.8057 | 0.15 | 26000 | 0.8185 | 0.7478 |
| 0.8085 | 0.16 | 28000 | 0.8090 | 0.7467 |
| 0.8229 | 0.17 | 30000 | 0.8225 | 0.7417 |
| 0.8151 | 0.18 | 32000 | 0.8262 | 0.7419 |
| 0.81 | 0.2 | 34000 | 0.8149 | 0.7383 |
| 0.8073 | 0.21 | 36000 | 0.8225 | 0.7441 |
| 0.816 | 0.22 | 38000 | 0.8037 | 0.744 |
| 0.8217 | 0.23 | 40000 | 0.8409 | 0.743 |
| 0.82 | 0.24 | 42000 | 0.8286 | 0.7385 |
| 0.8101 | 0.25 | 44000 | 0.8282 | 0.7413 |
| 0.8254 | 0.27 | 46000 | 0.8170 | 0.7414 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["yahoo_answers_topics"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-yahoo_answers_topics", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "yahoo_answers_topics", "type": "yahoo_answers_topics", "args": "yahoo_answers_topics"}, "metrics": [{"type": "accuracy", "value": 0.7499166666666667, "name": "Accuracy"}]}]}]}
|
text-classification
|
fabriceyhc/bert-base-uncased-yahoo_answers_topics
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:yahoo_answers_topics",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yahoo_answers_topics #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-yahoo\_answers\_topics
========================================
This model is a fine-tuned version of bert-base-uncased on the yahoo\_answers\_topics dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8092
* Accuracy: 0.7499
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 86625
* training\_steps: 866250
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 86625\n* training\\_steps: 866250",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yahoo_answers_topics #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 86625\n* training\\_steps: 866250",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
69,
119,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yahoo_answers_topics #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 86625\n* training\\_steps: 866250### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-yelp_polarity
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the yelp_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3222
- Accuracy: 0.9516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 277200
- training_steps: 2772000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8067 | 0.0 | 2000 | 0.8241 | 0.4975 |
| 0.5482 | 0.01 | 4000 | 0.3507 | 0.8591 |
| 0.3427 | 0.01 | 6000 | 0.3750 | 0.9139 |
| 0.4133 | 0.01 | 8000 | 0.5520 | 0.9016 |
| 0.4301 | 0.02 | 10000 | 0.3803 | 0.9304 |
| 0.3716 | 0.02 | 12000 | 0.4168 | 0.9337 |
| 0.4076 | 0.03 | 14000 | 0.5042 | 0.9170 |
| 0.3674 | 0.03 | 16000 | 0.4806 | 0.9268 |
| 0.3813 | 0.03 | 18000 | 0.4227 | 0.9261 |
| 0.3723 | 0.04 | 20000 | 0.3360 | 0.9418 |
| 0.3876 | 0.04 | 22000 | 0.3255 | 0.9407 |
| 0.3351 | 0.04 | 24000 | 0.3283 | 0.9404 |
| 0.34 | 0.05 | 26000 | 0.3489 | 0.9430 |
| 0.3006 | 0.05 | 28000 | 0.3302 | 0.9464 |
| 0.349 | 0.05 | 30000 | 0.3853 | 0.9375 |
| 0.3696 | 0.06 | 32000 | 0.2992 | 0.9454 |
| 0.3301 | 0.06 | 34000 | 0.3484 | 0.9464 |
| 0.3151 | 0.06 | 36000 | 0.3529 | 0.9455 |
| 0.3682 | 0.07 | 38000 | 0.3052 | 0.9420 |
| 0.3184 | 0.07 | 40000 | 0.3323 | 0.9466 |
| 0.3207 | 0.08 | 42000 | 0.3133 | 0.9532 |
| 0.3346 | 0.08 | 44000 | 0.3826 | 0.9414 |
| 0.3008 | 0.08 | 46000 | 0.3059 | 0.9484 |
| 0.3306 | 0.09 | 48000 | 0.3089 | 0.9475 |
| 0.342 | 0.09 | 50000 | 0.3611 | 0.9486 |
| 0.3424 | 0.09 | 52000 | 0.3227 | 0.9445 |
| 0.3044 | 0.1 | 54000 | 0.3130 | 0.9489 |
| 0.3278 | 0.1 | 56000 | 0.3827 | 0.9368 |
| 0.288 | 0.1 | 58000 | 0.3080 | 0.9504 |
| 0.3342 | 0.11 | 60000 | 0.3252 | 0.9471 |
| 0.3737 | 0.11 | 62000 | 0.4250 | 0.9343 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["yelp_polarity"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-yelp_polarity", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "yelp_polarity", "type": "yelp_polarity", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.9516052631578947, "name": "Accuracy"}]}]}]}
|
text-classification
|
fabriceyhc/bert-base-uncased-yelp_polarity
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:yelp_polarity",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yelp_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-yelp\_polarity
================================
This model is a fine-tuned version of bert-base-uncased on the yelp\_polarity dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3222
* Accuracy: 0.9516
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 277200
* training\_steps: 2772000
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 277200\n* training\\_steps: 2772000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yelp_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 277200\n* training\\_steps: 2772000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
[
68,
119,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yelp_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 277200\n* training\\_steps: 2772000### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.7.1\n* Datasets 1.6.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# BART (base-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/bart).
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
## Intended uses & limitations
You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import BartTokenizer, BartModel
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
model = BartModel.from_pretrained('facebook/bart-base')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1910-13461,
author = {Mike Lewis and
Yinhan Liu and
Naman Goyal and
Marjan Ghazvininejad and
Abdelrahman Mohamed and
Omer Levy and
Veselin Stoyanov and
Luke Zettlemoyer},
title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
Generation, Translation, and Comprehension},
journal = {CoRR},
volume = {abs/1910.13461},
year = {2019},
url = {http://arxiv.org/abs/1910.13461},
eprinttype = {arXiv},
eprint = {1910.13461},
timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"language": "en", "license": "apache-2.0"}
|
feature-extraction
|
facebook/bart-base
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1910.13461"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #safetensors #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# BART (base-sized model)
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository.
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
## Intended uses & limitations
You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the model hub to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model in PyTorch:
### BibTeX entry and citation info
|
[
"# BART (base-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nBART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).",
"## Intended uses & limitations\n\nYou can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the model hub to look for fine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model in PyTorch:",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# BART (base-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nBART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).",
"## Intended uses & limitations\n\nYou can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the model hub to look for fine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model in PyTorch:",
"### BibTeX entry and citation info"
] |
[
63,
103,
132,
63,
17,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# BART (base-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nBART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).## Intended uses & limitations\n\nYou can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the model hub to look for fine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model in PyTorch:### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# BART (large-sized model), fine-tuned on CNN Daily Mail
BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart).
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.
## Intended uses & limitations
You can use this model for text summarization.
### How to use
Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html):
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
2010 marriage license application, according to court documents.
Prosecutors said the marriages were part of an immigration scam.
On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.
"""
print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))
>>> [{'summary_text': 'Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002. She is believed to still be married to four men.'}]
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1910-13461,
author = {Mike Lewis and
Yinhan Liu and
Naman Goyal and
Marjan Ghazvininejad and
Abdelrahman Mohamed and
Omer Levy and
Veselin Stoyanov and
Luke Zettlemoyer},
title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
Generation, Translation, and Comprehension},
journal = {CoRR},
volume = {abs/1910.13461},
year = {2019},
url = {http://arxiv.org/abs/1910.13461},
eprinttype = {arXiv},
eprint = {1910.13461},
timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"language": ["en"], "license": "mit", "datasets": ["cnn_dailymail"], "pipeline_tag": "summarization", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "model-index": [{"name": "facebook/bart-large-cnn", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailymail", "config": "3.0.0", "split": "train"}, "metrics": [{"type": "rouge", "value": 42.9486, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 20.8149, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 30.6186, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 40.0376, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 2.529000997543335, "name": "loss", "verified": true}, {"type": "gen_len", "value": 78.5866, "name": "gen_len", "verified": true}]}]}]}
|
summarization
|
facebook/bart-large-cnn
|
[
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"arxiv:1910.13461",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1910.13461"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #rust #safetensors #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BART (large-sized model), fine-tuned on CNN Daily Mail
BART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository (URL
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.
## Intended uses & limitations
You can use this model for text summarization.
### How to use
Here is how to use this model with the [pipeline API:
### BibTeX entry and citation info
|
[
"# BART (large-sized model), fine-tuned on CNN Daily Mail \n\nBART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository (URL \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nBART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.",
"## Intended uses & limitations\n\nYou can use this model for text summarization.",
"### How to use\n\nHere is how to use this model with the [pipeline API:",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #rust #safetensors #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BART (large-sized model), fine-tuned on CNN Daily Mail \n\nBART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository (URL \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nBART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.",
"## Intended uses & limitations\n\nYou can use this model for text summarization.",
"### How to use\n\nHere is how to use this model with the [pipeline API:",
"### BibTeX entry and citation info"
] |
[
88,
123,
158,
20,
20,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #rust #safetensors #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n# BART (large-sized model), fine-tuned on CNN Daily Mail \n\nBART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository (URL \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nBART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.## Intended uses & limitations\n\nYou can use this model for text summarization.### How to use\n\nHere is how to use this model with the [pipeline API:### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# bart-large-mnli
This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset.
Additional information about this model:
- The [bart-large](https://huggingface.co/facebook/bart-large) model page
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
](https://arxiv.org/abs/1910.13461)
- [BART fairseq implementation](https://github.com/pytorch/fairseq/tree/master/fairseq/models/bart)
## NLI-based Zero Shot Text Classification
[Yin et al.](https://arxiv.org/abs/1909.00161) proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of `This text is about politics.`. The probabilities for entailment and contradiction are then converted to label probabilities.
This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See [this blog post](https://joeddav.github.io/blog/2020/05/29/ZSL.html) for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.
#### With the zero-shot classification pipeline
The model can be loaded with the `zero-shot-classification` pipeline like so:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="facebook/bart-large-mnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
#{'labels': ['travel', 'dancing', 'cooking'],
# 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],
# 'sequence': 'one day I will see the world'}
```
If more than one candidate label can be correct, pass `multi_label=True` to calculate each class independently:
```python
candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_label=True)
#{'labels': ['travel', 'exploration', 'dancing', 'cooking'],
# 'scores': [0.9945111274719238,
# 0.9383890628814697,
# 0.0057061901316046715,
# 0.0018193122232332826],
# 'sequence': 'one day I will see the world'}
```
#### With manual PyTorch
```python
# pose sequence as a NLI premise and label as a hypothesis
from transformers import AutoModelForSequenceClassification, AutoTokenizer
nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')
premise = sequence
hypothesis = f'This example is {label}.'
# run through model pre-trained on MNLI
x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
truncation_strategy='only_first')
logits = nli_model(x.to(device))[0]
# we throw away "neutral" (dim 1) and take the probability of
# "entailment" (2) as the probability of the label being true
entail_contradiction_logits = logits[:,[0,2]]
probs = entail_contradiction_logits.softmax(dim=1)
prob_label_is_true = probs[:,1]
```
|
{"license": "mit", "datasets": ["multi_nli"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
facebook/bart-large-mnli
|
[
"transformers",
"pytorch",
"jax",
"rust",
"safetensors",
"bart",
"text-classification",
"zero-shot-classification",
"dataset:multi_nli",
"arxiv:1910.13461",
"arxiv:1909.00161",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1910.13461",
"1909.00161"
] |
[] |
TAGS
#transformers #pytorch #jax #rust #safetensors #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1910.13461 #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bart-large-mnli
This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.
Additional information about this model:
- The bart-large model page
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
- BART fairseq implementation
## NLI-based Zero Shot Text Classification
Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of 'This text is about politics.'. The probabilities for entailment and contradiction are then converted to label probabilities.
This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.
#### With the zero-shot classification pipeline
The model can be loaded with the 'zero-shot-classification' pipeline like so:
You can then use this pipeline to classify sequences into any of the class names you specify.
If more than one candidate label can be correct, pass 'multi_label=True' to calculate each class independently:
#### With manual PyTorch
|
[
"# bart-large-mnli\n\nThis is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.\n\nAdditional information about this model:\n- The bart-large model page\n- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension\n\n- BART fairseq implementation",
"## NLI-based Zero Shot Text Classification\n\nYin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class \"politics\", we could construct a hypothesis of 'This text is about politics.'. The probabilities for entailment and contradiction are then converted to label probabilities.\n\nThis method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.",
"#### With the zero-shot classification pipeline\n\nThe model can be loaded with the 'zero-shot-classification' pipeline like so:\n\n\n\nYou can then use this pipeline to classify sequences into any of the class names you specify.\n\n\n\nIf more than one candidate label can be correct, pass 'multi_label=True' to calculate each class independently:",
"#### With manual PyTorch"
] |
[
"TAGS\n#transformers #pytorch #jax #rust #safetensors #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1910.13461 #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bart-large-mnli\n\nThis is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.\n\nAdditional information about this model:\n- The bart-large model page\n- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension\n\n- BART fairseq implementation",
"## NLI-based Zero Shot Text Classification\n\nYin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class \"politics\", we could construct a hypothesis of 'This text is about politics.'. The probabilities for entailment and contradiction are then converted to label probabilities.\n\nThis method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.",
"#### With the zero-shot classification pipeline\n\nThe model can be loaded with the 'zero-shot-classification' pipeline like so:\n\n\n\nYou can then use this pipeline to classify sequences into any of the class names you specify.\n\n\n\nIf more than one candidate label can be correct, pass 'multi_label=True' to calculate each class independently:",
"#### With manual PyTorch"
] |
[
87,
90,
232,
84,
7
] |
[
"passage: TAGS\n#transformers #pytorch #jax #rust #safetensors #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1910.13461 #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# bart-large-mnli\n\nThis is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.\n\nAdditional information about this model:\n- The bart-large model page\n- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension\n\n- BART fairseq implementation## NLI-based Zero Shot Text Classification\n\nYin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class \"politics\", we could construct a hypothesis of 'This text is about politics.'. The probabilities for entailment and contradiction are then converted to label probabilities.\n\nThis method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.#### With the zero-shot classification pipeline\n\nThe model can be loaded with the 'zero-shot-classification' pipeline like so:\n\n\n\nYou can then use this pipeline to classify sequences into any of the class names you specify.\n\n\n\nIf more than one candidate label can be correct, pass 'multi_label=True' to calculate each class independently:#### With manual PyTorch"
] |
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] |
null | null |
transformers
|
### Bart model finetuned on xsum
docs: https://huggingface.co/transformers/model_doc/bart.html
finetuning: examples/seq2seq/ (as of Aug 20, 2020)
Metrics: ROUGE > 22 on xsum.
variants: search for distilbart
paper: https://arxiv.org/abs/1910.13461
|
{"language": ["en"], "license": "mit", "tags": ["summarization"], "model-index": [{"name": "facebook/bart-large-xsum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailymail", "config": "3.0.0", "split": "test"}, "metrics": [{"type": "rouge", "value": 25.2697, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 7.6638, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 17.1808, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 21.7933, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 3.5042972564697266, "name": "loss", "verified": true}, {"type": "gen_len", "value": 27.4462, "name": "gen_len", "verified": true}]}, {"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "xsum", "type": "xsum", "config": "default", "split": "test"}, "metrics": [{"type": "rouge", "value": 45.4525, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 22.3455, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 37.2302, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 37.2323, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 2.3128726482391357, "name": "loss", "verified": true}, {"type": "gen_len", "value": 25.5435, "name": "gen_len", "verified": true}]}, {"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "samsum", "type": "samsum", "config": "samsum", "split": "train"}, "metrics": [{"type": "rouge", "value": 24.7852, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 5.2533, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 18.6792, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 20.629, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 3.746837854385376, "name": "loss", "verified": true}, {"type": "gen_len", "value": 23.1206, "name": "gen_len", "verified": true}]}, {"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "samsum", "type": "samsum", "config": "samsum", "split": "test"}, "metrics": [{"type": "rouge", "value": 24.9158, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 5.5837, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 18.8935, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 20.76, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 3.775235891342163, "name": "loss", "verified": true}, {"type": "gen_len", "value": 23.0928, "name": "gen_len", "verified": true}]}]}]}
|
summarization
|
facebook/bart-large-xsum
|
[
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bart",
"text2text-generation",
"summarization",
"en",
"arxiv:1910.13461",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1910.13461"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #rust #bart #text2text-generation #summarization #en #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
### Bart model finetuned on xsum
docs: URL
finetuning: examples/seq2seq/ (as of Aug 20, 2020)
Metrics: ROUGE > 22 on xsum.
variants: search for distilbart
paper: URL
|
[
"### Bart model finetuned on xsum\n\ndocs: URL\n\nfinetuning: examples/seq2seq/ (as of Aug 20, 2020)\n\nMetrics: ROUGE > 22 on xsum.\n\nvariants: search for distilbart\n\npaper: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #rust #bart #text2text-generation #summarization #en #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Bart model finetuned on xsum\n\ndocs: URL\n\nfinetuning: examples/seq2seq/ (as of Aug 20, 2020)\n\nMetrics: ROUGE > 22 on xsum.\n\nvariants: search for distilbart\n\npaper: URL"
] |
[
74,
59
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #rust #bart #text2text-generation #summarization #en #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Bart model finetuned on xsum\n\ndocs: URL\n\nfinetuning: examples/seq2seq/ (as of Aug 20, 2020)\n\nMetrics: ROUGE > 22 on xsum.\n\nvariants: search for distilbart\n\npaper: URL"
] |
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] |
null | null |
transformers
|
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/bart).
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
## Intended uses & limitations
You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import BartTokenizer, BartModel
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
model = BartModel.from_pretrained('facebook/bart-large')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1910-13461,
author = {Mike Lewis and
Yinhan Liu and
Naman Goyal and
Marjan Ghazvininejad and
Abdelrahman Mohamed and
Omer Levy and
Veselin Stoyanov and
Luke Zettlemoyer},
title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
Generation, Translation, and Comprehension},
journal = {CoRR},
volume = {abs/1910.13461},
year = {2019},
url = {http://arxiv.org/abs/1910.13461},
eprinttype = {arXiv},
eprint = {1910.13461},
timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"language": "en", "license": "apache-2.0"}
|
feature-extraction
|
facebook/bart-large
|
[
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1910.13461"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #rust #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository.
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
## Intended uses & limitations
You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the model hub to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model in PyTorch:
### BibTeX entry and citation info
|
[
"# BART (large-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nBART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).",
"## Intended uses & limitations\n\nYou can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the model hub to look for fine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model in PyTorch:",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #rust #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# BART (large-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nBART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).",
"## Intended uses & limitations\n\nYou can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the model hub to look for fine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model in PyTorch:",
"### BibTeX entry and citation info"
] |
[
60,
104,
132,
63,
17,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #rust #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# BART (large-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nBART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.\n\nBART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).## Intended uses & limitations\n\nYou can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the model hub to look for fine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model in PyTorch:### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]}
|
text-generation
|
facebook/blenderbot-1B-distill
|
[
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1907.06616"
] |
[
"en"
] |
TAGS
#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
[
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
"TAGS\n#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
85,
22,
234
] |
[
"passage: TAGS\n#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
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] |
null | null |
transformers
|
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]}
|
text-generation
|
facebook/blenderbot-3B
|
[
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1907.06616"
] |
[
"en"
] |
TAGS
#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
[
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
"TAGS\n#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
85,
22,
234
] |
[
"passage: TAGS\n#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
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] |
null | null |
transformers
|
## Model description
+ Paper: [Recipes for building an open-domain chatbot]( https://arxiv.org/abs/2004.13637)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]}
|
text-generation
|
facebook/blenderbot-400M-distill
|
[
"transformers",
"pytorch",
"tf",
"jax",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:2004.13637",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13637"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-2004.13637 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
[
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-2004.13637 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
91,
22,
234
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-2004.13637 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
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] |
null | null |
transformers
|
# 🚨🚨**IMPORTANT**🚨🚨
**This model is deprecated! Please use the identical model** **https://huggingface.co/facebook/blenderbot_small-90M instead**
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]}
|
text-generation
|
facebook/blenderbot-90M
|
[
"transformers",
"pytorch",
"blenderbot-small",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1907.06616"
] |
[
"en"
] |
TAGS
#transformers #pytorch #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# IMPORTANT
This model is deprecated! Please use the identical model URL instead
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
[
"# IMPORTANT\n\nThis model is deprecated! Please use the identical model URL instead",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
"TAGS\n#transformers #pytorch #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# IMPORTANT\n\nThis model is deprecated! Please use the identical model URL instead",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
88,
19,
22,
234
] |
[
"passage: TAGS\n#transformers #pytorch #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# IMPORTANT\n\nThis model is deprecated! Please use the identical model URL instead## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
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] |
null | null |
transformers
|
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]}
|
text-generation
|
facebook/blenderbot_small-90M
|
[
"transformers",
"pytorch",
"tf",
"jax",
"blenderbot-small",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1907.06616"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
[
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
[
94,
22,
234
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models."
] |
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] |
null | null |
transformers
|
This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
## Usage (HuggingFace Transformers)
Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding.
```python
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('facebook/contriever-msmarco')
model = AutoModel.from_pretrained('facebook/contriever-msmarco')
sentences = [
"Where was Marie Curie born?",
"Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
"Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]
# Apply tokenizer
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
outputs = model(**inputs)
# Mean pooling
def mean_pooling(token_embeddings, mask):
token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)
sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]
return sentence_embeddings
embeddings = mean_pooling(outputs[0], inputs['attention_mask'])
```
|
{"tags": ["feature-extraction"], "pipeline_tag": "feature-extraction"}
|
feature-extraction
|
facebook/contriever-msmarco
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2112.09118",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2112.09118"
] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2112.09118 #endpoints_compatible #has_space #region-us
|
This model is the finetuned version of the pre-trained contriever model available here URL following the approach described in Towards Unsupervised Dense Information Retrieval with Contrastive Learning. The associated GitHub repository is available here URL
## Usage (HuggingFace Transformers)
Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding.
|
[
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2112.09118 #endpoints_compatible #has_space #region-us \n",
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
[
42,
41
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2112.09118 #endpoints_compatible #has_space #region-us \n## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
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] |
null | null |
transformers
|
This model has been trained without supervision following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
## Usage (HuggingFace Transformers)
Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding.
```python
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('facebook/contriever')
model = AutoModel.from_pretrained('facebook/contriever')
sentences = [
"Where was Marie Curie born?",
"Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
"Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]
# Apply tokenizer
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
outputs = model(**inputs)
# Mean pooling
def mean_pooling(token_embeddings, mask):
token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)
sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]
return sentence_embeddings
embeddings = mean_pooling(outputs[0], inputs['attention_mask'])
```
|
{}
| null |
facebook/contriever
|
[
"transformers",
"pytorch",
"bert",
"arxiv:2112.09118",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2112.09118"
] |
[] |
TAGS
#transformers #pytorch #bert #arxiv-2112.09118 #endpoints_compatible #has_space #region-us
|
This model has been trained without supervision following the approach described in Towards Unsupervised Dense Information Retrieval with Contrastive Learning. The associated GitHub repository is available here URL
## Usage (HuggingFace Transformers)
Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding.
|
[
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
[
"TAGS\n#transformers #pytorch #bert #arxiv-2112.09118 #endpoints_compatible #has_space #region-us \n",
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
[
36,
41
] |
[
"passage: TAGS\n#transformers #pytorch #bert #arxiv-2112.09118 #endpoints_compatible #has_space #region-us \n## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
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] |
null | null |
transformers
|
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-base-224-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-384-224-1k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-base-224-22k-1k
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
81,
106,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-base-224-22k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224-22k")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 22k ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-base-224-22k
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
72,
94,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-base-224")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-base-224
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
72,
94,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-base-384-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-384-22k-1k")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-base-384-22k-1k
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
76,
107,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-base-384")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-384")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-base-384
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
68,
95,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-large-224-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-224-22k-1k")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1k ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-large-224-22k-1k
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
68,
107,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-large-224-22k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-224-22k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 22k ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-large-224-22k
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
73,
95,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-large-224")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-224")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-large-224
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
68,
95,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-large-384-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-384-22k-1k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-large-384-22k-1k
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
76,
108,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-large-384")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-384")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-large-384
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
73,
96,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-small-224")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-small-224")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-small-224
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
73,
95,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# ConvNeXT (tiny-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-tiny-224
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ConvNeXT (tiny-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (tiny-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ConvNeXT (tiny-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
72,
94,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# ConvNeXT (tiny-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-xlarge-224-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-xlarge-224-22k-1k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-xlarge-224-22k-1k
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
76,
96,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-xlarge-224-22k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-xlarge-224-22k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 22k ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-xlarge-224-22k
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
68,
96,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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0.009515167213976383,
0.06519102305173874,
-0.06537006050348282,
-0.05315595865249634
] |
null | null |
transformers
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-xlarge-384-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-xlarge-384-22k-1k")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
facebook/convnext-xlarge-384-22k-1k
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2201.03545"
] |
[] |
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
!model image
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
### BibTeX entry and citation info
|
[
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"### BibTeX entry and citation info"
] |
[
80,
109,
65,
41,
45,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# ConvNeXT (xlarge-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and \"modernized\" its design by taking the Swin Transformer as inspiration.\n\n!model image## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# Data2Vec-Audio-Base-100h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2202.03555)
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
**Abstract**
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec .
# Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Data2VecForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-100h")
model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-100h")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]}
|
automatic-speech-recognition
|
facebook/data2vec-audio-base-100h
|
[
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03555"
] |
[
"en"
] |
TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Base-100h
Facebook's Data2Vec
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
Abstract
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under URL .
# Pre-Training method
!model image
For more information, please take a look at the official paper.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
|
[
"# Data2Vec-Audio-Base-100h\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .",
"# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.",
"# Usage\n\nTo transcribe audio files the model can be used as a standalone acoustic model as follows:"
] |
[
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Base-100h\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .",
"# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.",
"# Usage\n\nTo transcribe audio files the model can be used as a standalone acoustic model as follows:"
] |
[
69,
315,
22,
25
] |
[
"passage: TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n# Data2Vec-Audio-Base-100h\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.# Usage\n\nTo transcribe audio files the model can be used as a standalone acoustic model as follows:"
] |
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] |
null | null |
transformers
|
# Data2Vec-Audio-Base-10m
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2202.03555)
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
**Abstract**
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec .
# Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Data2VecForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-10m")
model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-10m")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]}
|
automatic-speech-recognition
|
facebook/data2vec-audio-base-10m
|
[
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03555"
] |
[
"en"
] |
TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Base-10m
Facebook's Data2Vec
The base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
Abstract
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under URL .
# Pre-Training method
!model image
For more information, please take a look at the official paper.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
|
[
"# Data2Vec-Audio-Base-10m\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .",
"# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.",
"# Usage\n\nTo transcribe audio files the model can be used as a standalone acoustic model as follows:"
] |
[
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Base-10m\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .",
"# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.",
"# Usage\n\nTo transcribe audio files the model can be used as a standalone acoustic model as follows:"
] |
[
69,
315,
22,
25
] |
[
"passage: TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n# Data2Vec-Audio-Base-10m\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.# Usage\n\nTo transcribe audio files the model can be used as a standalone acoustic model as follows:"
] |
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null | null |
transformers
|
# Data2Vec-Audio-Base-960h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2202.03555)
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
**Abstract**
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec .
# Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Data2VecForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h")
model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **facebook/data2vec-audio-base-960h** on LibriSpeech's "clean" and "other" test data.
```python
from transformers import Wav2Vec2Processor, Data2VecForCTC
from datasets import load_dataset
import torch
from jiwer import wer
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h").to("cuda")
model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
def map_to_pred(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
with torch.no_grad():
logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 2.77 | 7.08 |
|
{"language": "en", "license": "apache-2.0", "tags": ["speech", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingface.co/speech_samples/sample2.flac"}], "model-index": [{"name": "data2vec-audio-base-960h", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (clean)", "type": "librispeech_asr", "config": "clean", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 2.77, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (other)", "type": "librispeech_asr", "config": "other", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 7.08, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
facebook/data2vec-audio-base-960h
|
[
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03555"
] |
[
"en"
] |
TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Data2Vec-Audio-Base-960h
========================
Facebook's Data2Vec
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
Abstract
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under URL .
Pre-Training method
===================
!model image
For more information, please take a look at the official paper.
Usage
=====
To transcribe audio files the model can be used as a standalone acoustic model as follows:
Evaluation
----------
This code snippet shows how to evaluate facebook/data2vec-audio-base-960h on LibriSpeech's "clean" and "other" test data.
*Result (WER)*:
|
[] |
[
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
[
83
] |
[
"passage: TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Data2Vec-Audio-Base
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.
[Paper](https://arxiv.org/abs/2202.03555)
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
**Abstract**
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec .
# Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
# Usage
See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model.
|
{"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]}
|
feature-extraction
|
facebook/data2vec-audio-base
|
[
"transformers",
"pytorch",
"data2vec-audio",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03555"
] |
[
"en"
] |
TAGS
#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Data2Vec-Audio-Base
Facebook's Data2Vec
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
Abstract
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under URL .
# Pre-Training method
!model image
For more information, please take a look at the official paper.
# Usage
See this notebook for more information on how to fine-tune the model.
|
[
"# Data2Vec-Audio-Base\n\nFacebook's Data2Vec\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .",
"# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.",
"# Usage\n\nSee this notebook for more information on how to fine-tune the model."
] |
[
"TAGS\n#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Data2Vec-Audio-Base\n\nFacebook's Data2Vec\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .",
"# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.",
"# Usage\n\nSee this notebook for more information on how to fine-tune the model."
] |
[
69,
372,
22,
18
] |
[
"passage: TAGS\n#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# Data2Vec-Audio-Base\n\nFacebook's Data2Vec\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli\n\nAbstract\n\nWhile the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.\n\nThe original model can be found under URL .# Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.# Usage\n\nSee this notebook for more information on how to fine-tune the model."
] |
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] |
null | null |
transformers
|
# Data2Vec-Text base model
Pretrained model on English language using the *data2vec* objective. It was introduced in
[this paper](https://arxiv.org/abs/2202.03555) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/main/examples/data2vec). This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing Data2Vec-Text did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
## Abstract
*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because
they were developed with a single modality in
mind. To get us closer to general self-supervised
learning, we present data2vec, a framework that
uses the same learning method for either speech,
NLP or computer vision. The core idea is to predict latent representations of the full input data
based on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific
targets such as words, visual tokens or units of
human speech which are local in nature, data2vec
predicts contextualized latent representations that
contain information from the entire input. Experiments on the major benchmarks of speech
recognition, image classification, and natural language understanding demonstrate a new state of
the art or competitive performance to predominant approaches.*
## Intended uses & limitations
The model is intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=data2vec-text) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
## Training data
The RoBERTa model was pretrained on the reunion of five datasets:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
- [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news
articles crawled between September 2016 and February 2019.
- [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to
train GPT-2,
- [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas.
Together theses datasets weight 160GB of text.
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2202.03555,
doi = {10.48550/ARXIV.2202.03555},
url = {https://arxiv.org/abs/2202.03555},
author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael},
keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
{"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]}
|
feature-extraction
|
facebook/data2vec-text-base
|
[
"transformers",
"pytorch",
"data2vec-text",
"feature-extraction",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2202.03555",
"arxiv:1806.02847",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03555",
"1806.02847"
] |
[
"en"
] |
TAGS
#transformers #pytorch #data2vec-text #feature-extraction #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2202.03555 #arxiv-1806.02847 #license-mit #endpoints_compatible #has_space #region-us
|
# Data2Vec-Text base model
Pretrained model on English language using the *data2vec* objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing Data2Vec-Text did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Pre-Training method
!model image
For more information, please take a look at the official paper.
## Abstract
*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because
they were developed with a single modality in
mind. To get us closer to general self-supervised
learning, we present data2vec, a framework that
uses the same learning method for either speech,
NLP or computer vision. The core idea is to predict latent representations of the full input data
based on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific
targets such as words, visual tokens or units of
human speech which are local in nature, data2vec
predicts contextualized latent representations that
contain information from the entire input. Experiments on the major benchmarks of speech
recognition, image classification, and natural language understanding demonstrate a new state of
the art or competitive performance to predominant approaches.*
## Intended uses & limitations
The model is intended to be fine-tuned on a downstream task.
See the model hub to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
## Training data
The RoBERTa model was pretrained on the reunion of five datasets:
- BookCorpus, a dataset consisting of 11,038 unpublished books;
- English Wikipedia (excluding lists, tables and headers) ;
- CC-News, a dataset containing 63 millions English news
articles crawled between September 2016 and February 2019.
- OpenWebText, an opensource recreation of the WebText dataset used to
train GPT-2,
- Stories a dataset containing a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas.
Together theses datasets weight 160GB of text.
### BibTeX entry and citation info
|
[
"# Data2Vec-Text base model\n\nPretrained model on English language using the *data2vec* objective. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it\nmakes a difference between english and English.\n\nDisclaimer: The team releasing Data2Vec-Text did not write a model card for this model so this model card has been written by\nthe Hugging Face team.",
"## Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.",
"## Abstract\n\n*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because\nthey were developed with a single modality in\nmind. To get us closer to general self-supervised\nlearning, we present data2vec, a framework that\nuses the same learning method for either speech,\nNLP or computer vision. The core idea is to predict latent representations of the full input data\nbased on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific\ntargets such as words, visual tokens or units of\nhuman speech which are local in nature, data2vec\npredicts contextualized latent representations that\ncontain information from the entire input. Experiments on the major benchmarks of speech\nrecognition, image classification, and natural language understanding demonstrate a new state of\nthe art or competitive performance to predominant approaches.*",
"## Intended uses & limitations\n\nThe model is intended to be fine-tuned on a downstream task.\nSee the model hub to look for fine-tuned versions on a task that\ninterests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"## Training data\n\nThe RoBERTa model was pretrained on the reunion of five datasets:\n- BookCorpus, a dataset consisting of 11,038 unpublished books;\n- English Wikipedia (excluding lists, tables and headers) ;\n- CC-News, a dataset containing 63 millions English news\n articles crawled between September 2016 and February 2019.\n- OpenWebText, an opensource recreation of the WebText dataset used to\n train GPT-2,\n- Stories a dataset containing a subset of CommonCrawl data filtered to match the\n story-like style of Winograd schemas.\n\nTogether theses datasets weight 160GB of text.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #data2vec-text #feature-extraction #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2202.03555 #arxiv-1806.02847 #license-mit #endpoints_compatible #has_space #region-us \n",
"# Data2Vec-Text base model\n\nPretrained model on English language using the *data2vec* objective. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it\nmakes a difference between english and English.\n\nDisclaimer: The team releasing Data2Vec-Text did not write a model card for this model so this model card has been written by\nthe Hugging Face team.",
"## Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.",
"## Abstract\n\n*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because\nthey were developed with a single modality in\nmind. To get us closer to general self-supervised\nlearning, we present data2vec, a framework that\nuses the same learning method for either speech,\nNLP or computer vision. The core idea is to predict latent representations of the full input data\nbased on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific\ntargets such as words, visual tokens or units of\nhuman speech which are local in nature, data2vec\npredicts contextualized latent representations that\ncontain information from the entire input. Experiments on the major benchmarks of speech\nrecognition, image classification, and natural language understanding demonstrate a new state of\nthe art or competitive performance to predominant approaches.*",
"## Intended uses & limitations\n\nThe model is intended to be fine-tuned on a downstream task.\nSee the model hub to look for fine-tuned versions on a task that\ninterests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"## Training data\n\nThe RoBERTa model was pretrained on the reunion of five datasets:\n- BookCorpus, a dataset consisting of 11,038 unpublished books;\n- English Wikipedia (excluding lists, tables and headers) ;\n- CC-News, a dataset containing 63 millions English news\n articles crawled between September 2016 and February 2019.\n- OpenWebText, an opensource recreation of the WebText dataset used to\n train GPT-2,\n- Stories a dataset containing a subset of CommonCrawl data filtered to match the\n story-like style of Winograd schemas.\n\nTogether theses datasets weight 160GB of text.",
"### BibTeX entry and citation info"
] |
[
76,
94,
22,
209,
113,
151,
11
] |
[
"passage: TAGS\n#transformers #pytorch #data2vec-text #feature-extraction #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2202.03555 #arxiv-1806.02847 #license-mit #endpoints_compatible #has_space #region-us \n# Data2Vec-Text base model\n\nPretrained model on English language using the *data2vec* objective. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it\nmakes a difference between english and English.\n\nDisclaimer: The team releasing Data2Vec-Text did not write a model card for this model so this model card has been written by\nthe Hugging Face team.## Pre-Training method\n\n!model image\n\nFor more information, please take a look at the official paper.## Abstract\n\n*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because\nthey were developed with a single modality in\nmind. To get us closer to general self-supervised\nlearning, we present data2vec, a framework that\nuses the same learning method for either speech,\nNLP or computer vision. The core idea is to predict latent representations of the full input data\nbased on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific\ntargets such as words, visual tokens or units of\nhuman speech which are local in nature, data2vec\npredicts contextualized latent representations that\ncontain information from the entire input. Experiments on the major benchmarks of speech\nrecognition, image classification, and natural language understanding demonstrate a new state of\nthe art or competitive performance to predominant approaches.*"
] |
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null | null |
transformers
|
# Distilled Data-efficient Image Transformer (base-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, DeiTForImageClassificationWithTeacher
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-base-distilled-patch16-224')
model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
# forward pass
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
This model was pretrained and fine-tuned with distillation on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| **DeiT-base distilled** | **83.4** | **96.5** | **87M** | **https://huggingface.co/facebook/deit-base-distilled-patch16-224** |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]}
|
image-classification
|
facebook/deit-base-distilled-patch16-224
|
[
"transformers",
"pytorch",
"tf",
"deit",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.12877",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Distilled Data-efficient Image Transformer (base-sized model)
=============================================================
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
-----------------
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
Intended uses & limitations
---------------------------
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
Training data
-------------
This model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.
Training procedure
------------------
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
Evaluation results
------------------
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
77,
189,
74,
94,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# Distilled Data-efficient Image Transformer (base-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained at resolution 224x224 and fine-tuned at resolution 384x384 on ImageNet-1k (1 million images, 1,000 classes). It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, DeiTForImageClassificationWithTeacher
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-base-distilled-patch16-384')
model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-384')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
This model was pretrained and fine-tuned with distillation on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|-------------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| **DeiT-base distilled 384 (1000 epochs)** | **85.2** | **97.2** | **88M** | **https://huggingface.co/facebook/deit-base-distilled-patch16-384** |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]}
|
image-classification
|
facebook/deit-base-distilled-patch16-384
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"deit",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.12877",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #safetensors #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Distilled Data-efficient Image Transformer (base-sized model)
=============================================================
Distilled data-efficient Image Transformer (DeiT) model pre-trained at resolution 224x224 and fine-tuned at resolution 384x384 on ImageNet-1k (1 million images, 1,000 classes). It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
-----------------
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
Intended uses & limitations
---------------------------
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
Training data
-------------
This model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.
Training procedure
------------------
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
At inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
Evaluation results
------------------
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
82,
189,
74,
96,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('facebook/deit-base-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| **DeiT-base** | **81.8** | **95.6** | **86M** | **https://huggingface.co/facebook/deit-base-patch16-224** |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet-1k"]}
|
image-classification
|
facebook/deit-base-patch16-224
|
[
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.12877",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Data-efficient Image Transformer (base-sized model)
===================================================
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
-----------------
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
Intended uses & limitations
---------------------------
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
Training data
-------------
The ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.
Training procedure
------------------
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
Evaluation results
------------------
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
76,
173,
74,
94,
11
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[
"passage: TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 384x384. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained at resolution 224 and fine-tuned at resolution 384 on a large collection of images in a supervised fashion, namely ImageNet-1k.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-base-patch16-384')
model = ViTForImageClassification.from_pretrained('facebook/deit-base-patch16-384')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| **DeiT-base 384** | **82.9** | **96.2** | **87M** | **https://huggingface.co/facebook/deit-base-patch16-384** |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet-1k"]}
|
image-classification
|
facebook/deit-base-patch16-384
|
[
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.12877",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Data-efficient Image Transformer (base-sized model)
===================================================
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 384x384. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
-----------------
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained at resolution 224 and fine-tuned at resolution 384 on a large collection of images in a supervised fashion, namely ImageNet-1k.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
Intended uses & limitations
---------------------------
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
Training data
-------------
The ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.
Training procedure
------------------
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
At inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
Evaluation results
------------------
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
72,
173,
74,
96,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# Distilled Data-efficient Image Transformer (small-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, DeiTForImageClassificationWithTeacher
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-small-distilled-patch16-224')
model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-small-distilled-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
This model was pretrained and fine-tuned with distillation on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| **DeiT-small distilled** | **81.2** | **95.4** | **22M** | **https://huggingface.co/facebook/deit-small-distilled-patch16-224** |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]}
|
image-classification
|
facebook/deit-small-distilled-patch16-224
|
[
"transformers",
"pytorch",
"tf",
"deit",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.12877",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Distilled Data-efficient Image Transformer (small-sized model)
==============================================================
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
-----------------
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
Intended uses & limitations
---------------------------
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
Training data
-------------
This model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.
Training procedure
------------------
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
Evaluation results
------------------
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
73,
189,
74,
94,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# Data-efficient Image Transformer (small-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-small-patch16-224')
model = ViTForImageClassification.from_pretrained('facebook/deit-small-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| **DeiT-small** | **79.9** | **95.0** | **22M** | **https://huggingface.co/facebook/deit-small-patch16-224** |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet-1k"]}
|
image-classification
|
facebook/deit-small-patch16-224
|
[
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.12877",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Data-efficient Image Transformer (small-sized model)
====================================================
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
-----------------
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
Intended uses & limitations
---------------------------
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
Training data
-------------
The ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.
Training procedure
------------------
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
Evaluation results
------------------
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
72,
173,
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94,
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"passage: TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.### BibTeX entry and citation info"
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] |
null | null |
transformers
|
# Distilled Data-efficient Image Transformer (tiny-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, DeiTForImageClassificationWithTeacher
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-tiny-distilled-patch16-224')
model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-tiny-distilled-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
This model was pretrained and fine-tuned with distillation on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| **DeiT-tiny distilled** | **74.5** | **91.9** | **6M** | **https://huggingface.co/facebook/deit-tiny-distilled-patch16-224** |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]}
|
image-classification
|
facebook/deit-tiny-distilled-patch16-224
|
[
"transformers",
"pytorch",
"tf",
"deit",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.12877",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Distilled Data-efficient Image Transformer (tiny-sized model)
=============================================================
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
-----------------
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
Intended uses & limitations
---------------------------
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
Training data
-------------
This model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.
Training procedure
------------------
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
Evaluation results
------------------
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
77,
189,
74,
94,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThis model was pretrained and fine-tuned with distillation on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# Data-efficient Image Transformer (tiny-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-tiny-patch16-224')
model = ViTForImageClassification.from_pretrained('facebook/deit-tiny-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| **DeiT-tiny** | **72.2** | **91.1** | **5M** | **https://huggingface.co/facebook/deit-tiny-patch16-224** |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]}
|
image-classification
|
facebook/deit-tiny-patch16-224
|
[
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.12877",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #vit #image-classification #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Data-efficient Image Transformer (tiny-sized model)
===================================================
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
-----------------
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
Intended uses & limitations
---------------------------
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
Training data
-------------
The ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.
Training procedure
------------------
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
Evaluation results
------------------
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.",
"### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
74,
173,
74,
94,
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[
"passage: TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.\n\n\nTraining data\n-------------\n\n\nThe ViT model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1k classes.\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe exact details of preprocessing of images during training/validation can be found here.\n\n\nAt inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.### Pretraining\n\n\nThe model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.\n\n\nEvaluation results\n------------------\n\n\n\nNote that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr).
Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
## Intended uses & limitations
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models.
### How to use
Here is how to use this model:
```python
from transformers import DetrFeatureExtractor, DetrForObjectDetection
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-101-dc5')
model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101-dc5')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
# model predicts bounding boxes and corresponding COCO classes
logits = outputs.logits
bboxes = outputs.pred_boxes
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### Training
The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).
## Evaluation results
This model achieves an AP (average precision) of **44.9** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2005-12872,
author = {Nicolas Carion and
Francisco Massa and
Gabriel Synnaeve and
Nicolas Usunier and
Alexander Kirillov and
Sergey Zagoruyko},
title = {End-to-End Object Detection with Transformers},
journal = {CoRR},
volume = {abs/2005.12872},
year = {2020},
url = {https://arxiv.org/abs/2005.12872},
archivePrefix = {arXiv},
eprint = {2005.12872},
timestamp = {Thu, 28 May 2020 17:38:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["object-detection"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "example_title": "Football Match"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg", "example_title": "Airport"}]}
|
object-detection
|
facebook/detr-resnet-101-dc5
|
[
"transformers",
"pytorch",
"safetensors",
"detr",
"object-detection",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2005.12872"
] |
[] |
TAGS
#transformers #pytorch #safetensors #detr #object-detection #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.
Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
## Intended uses & limitations
You can use the raw model for object detection. See the model hub to look for all available DETR models.
### How to use
Here is how to use this model:
Currently, both the feature extractor and model support PyTorch.
## Training data
The DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### Training
The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).
## Evaluation results
This model achieves an AP (average precision) of 44.9 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.
### BibTeX entry and citation info
|
[
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.",
"## Intended uses & limitations\n\nYou can use the raw model for object detection. See the model hub to look for all available DETR models.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch.",
"## Training data\n\nThe DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).",
"### Training\n\nThe model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).",
"## Evaluation results\n\nThis model achieves an AP (average precision) of 44.9 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #safetensors #detr #object-detection #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.",
"## Intended uses & limitations\n\nYou can use the raw model for object detection. See the model hub to look for all available DETR models.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch.",
"## Training data\n\nThe DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).",
"### Training\n\nThe model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).",
"## Evaluation results\n\nThis model achieves an AP (average precision) of 44.9 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.",
"### BibTeX entry and citation info"
] |
[
60,
127,
295,
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41,
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[
"passage: TAGS\n#transformers #pytorch #safetensors #detr #object-detection #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model."
] |
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] |
null | null |
transformers
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr).
Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
DETR can be naturally extended to perform panoptic segmentation, by adding a mask head on top of the decoder outputs.
## Intended uses & limitations
You can use the raw model for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models.
### How to use
Here is how to use this model:
```python
from transformers import DetrFeatureExtractor, DetrForSegmentation
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-101-panoptic')
model = DetrForSegmentation.from_pretrained('facebook/detr-resnet-101-panoptic')
# prepare inputs for the model
inputs = feature_extractor(images=image, return_tensors="pt")
# forward pass
outputs = model(**inputs)
# use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format
processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
# the segmentation is stored in a special-format png
panoptic_seg = Image.open(io.BytesIO(result["png_string"]))
panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8)
# retrieve the ids corresponding to each mask
panoptic_seg_id = rgb_to_id(panoptic_seg)
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The DETR model was trained on [COCO 2017 panoptic](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/detr/blob/master/datasets/coco_panoptic.py).
Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### Training
The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).
## Evaluation results
This model achieves the following results on COCO 2017 validation: a box AP (average precision) of **40.1**, a segmentation AP (average precision) of **33** and a PQ (panoptic quality) of **45.1**.
For more details regarding evaluation results, we refer to table 5 of the original paper.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2005-12872,
author = {Nicolas Carion and
Francisco Massa and
Gabriel Synnaeve and
Nicolas Usunier and
Alexander Kirillov and
Sergey Zagoruyko},
title = {End-to-End Object Detection with Transformers},
journal = {CoRR},
volume = {abs/2005.12872},
year = {2020},
url = {https://arxiv.org/abs/2005.12872},
archivePrefix = {arXiv},
eprint = {2005.12872},
timestamp = {Thu, 28 May 2020 17:38:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["image-segmentation", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg", "example_title": "Dog & Cat"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/construction-site.jpg", "example_title": "Construction Site"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/apple-orange.jpg", "example_title": "Apple & Orange"}]}
|
image-segmentation
|
facebook/detr-resnet-101-panoptic
|
[
"transformers",
"pytorch",
"safetensors",
"detr",
"image-segmentation",
"vision",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2005.12872"
] |
[] |
TAGS
#transformers #pytorch #safetensors #detr #image-segmentation #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.
Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
DETR can be naturally extended to perform panoptic segmentation, by adding a mask head on top of the decoder outputs.
## Intended uses & limitations
You can use the raw model for panoptic segmentation. See the model hub to look for all available DETR models.
### How to use
Here is how to use this model:
Currently, both the feature extractor and model support PyTorch.
## Training data
The DETR model was trained on COCO 2017 panoptic, a dataset consisting of 118k/5k annotated images for training/validation respectively.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### Training
The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).
## Evaluation results
This model achieves the following results on COCO 2017 validation: a box AP (average precision) of 40.1, a segmentation AP (average precision) of 33 and a PQ (panoptic quality) of 45.1.
For more details regarding evaluation results, we refer to table 5 of the original paper.
### BibTeX entry and citation info
|
[
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.\n\nDETR can be naturally extended to perform panoptic segmentation, by adding a mask head on top of the decoder outputs.",
"## Intended uses & limitations\n\nYou can use the raw model for panoptic segmentation. See the model hub to look for all available DETR models.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch.",
"## Training data\n\nThe DETR model was trained on COCO 2017 panoptic, a dataset consisting of 118k/5k annotated images for training/validation respectively.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).",
"### Training\n\nThe model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).",
"## Evaluation results\n\nThis model achieves the following results on COCO 2017 validation: a box AP (average precision) of 40.1, a segmentation AP (average precision) of 33 and a PQ (panoptic quality) of 45.1.\n\nFor more details regarding evaluation results, we refer to table 5 of the original paper.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #safetensors #detr #image-segmentation #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.\n\nDETR can be naturally extended to perform panoptic segmentation, by adding a mask head on top of the decoder outputs.",
"## Intended uses & limitations\n\nYou can use the raw model for panoptic segmentation. See the model hub to look for all available DETR models.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch.",
"## Training data\n\nThe DETR model was trained on COCO 2017 panoptic, a dataset consisting of 118k/5k annotated images for training/validation respectively.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).",
"### Training\n\nThe model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).",
"## Evaluation results\n\nThis model achieves the following results on COCO 2017 validation: a box AP (average precision) of 40.1, a segmentation AP (average precision) of 33 and a PQ (panoptic quality) of 45.1.\n\nFor more details regarding evaluation results, we refer to table 5 of the original paper.",
"### BibTeX entry and citation info"
] |
[
63,
119,
325,
34,
28,
40,
3,
94,
42,
72,
11
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[
"passage: TAGS\n#transformers #pytorch #safetensors #detr #image-segmentation #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.\n\nDETR can be naturally extended to perform panoptic segmentation, by adding a mask head on top of the decoder outputs."
] |
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] |
null | null |
transformers
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr).
Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
## Intended uses & limitations
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models.
### How to use
Here is how to use this model:
```python
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# you can specify the revision tag if you don't want the timm dependency
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101", revision="no_timm")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
print(
f"Detected {model.config.id2label[label.item()]} with confidence "
f"{round(score.item(), 3)} at location {box}"
)
```
This should output (something along the lines of):
```
Detected cat with confidence 0.998 at location [344.06, 24.85, 640.34, 373.74]
Detected remote with confidence 0.997 at location [328.13, 75.93, 372.81, 187.66]
Detected remote with confidence 0.997 at location [39.34, 70.13, 175.56, 118.78]
Detected cat with confidence 0.998 at location [15.36, 51.75, 316.89, 471.16]
Detected couch with confidence 0.995 at location [-0.19, 0.71, 639.73, 474.17]
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### Training
The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).
## Evaluation results
This model achieves an AP (average precision) of **43.5** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2005-12872,
author = {Nicolas Carion and
Francisco Massa and
Gabriel Synnaeve and
Nicolas Usunier and
Alexander Kirillov and
Sergey Zagoruyko},
title = {End-to-End Object Detection with Transformers},
journal = {CoRR},
volume = {abs/2005.12872},
year = {2020},
url = {https://arxiv.org/abs/2005.12872},
archivePrefix = {arXiv},
eprint = {2005.12872},
timestamp = {Thu, 28 May 2020 17:38:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
{"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "example_title": "Football Match"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg", "example_title": "Airport"}]}
|
object-detection
|
facebook/detr-resnet-101
|
[
"transformers",
"pytorch",
"safetensors",
"detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2005.12872"
] |
[] |
TAGS
#transformers #pytorch #safetensors #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.
Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
!model image
## Intended uses & limitations
You can use the raw model for object detection. See the model hub to look for all available DETR models.
### How to use
Here is how to use this model:
This should output (something along the lines of):
Currently, both the feature extractor and model support PyTorch.
## Training data
The DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### Training
The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).
## Evaluation results
This model achieves an AP (average precision) of 43.5 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.
### BibTeX entry and citation info
|
[
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for object detection. See the model hub to look for all available DETR models.",
"### How to use\n\nHere is how to use this model:\n\n\nThis should output (something along the lines of):\n\n\nCurrently, both the feature extractor and model support PyTorch.",
"## Training data\n\nThe DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).",
"### Training\n\nThe model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).",
"## Evaluation results\n\nThis model achieves an AP (average precision) of 43.5 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #safetensors #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.\n\n!model image",
"## Intended uses & limitations\n\nYou can use the raw model for object detection. See the model hub to look for all available DETR models.",
"### How to use\n\nHere is how to use this model:\n\n\nThis should output (something along the lines of):\n\n\nCurrently, both the feature extractor and model support PyTorch.",
"## Training data\n\nThe DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).",
"### Training\n\nThe model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64).",
"## Evaluation results\n\nThis model achieves an AP (average precision) of 43.5 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.",
"### BibTeX entry and citation info"
] |
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[
"passage: TAGS\n#transformers #pytorch #safetensors #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description\n\nThe DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. \n\nThe model is trained using a \"bipartite matching loss\": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a \"no object\" as class and \"no bounding box\" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.\n\n!model image"
] |
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