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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| likes
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| library_name
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davanstrien/testwebook | davanstrien | 2023-01-31T15:21:51Z | 0 | 0 | null | [
"legal",
"license:afl-3.0",
"region:us"
]
| null | 2023-01-26T18:42:00Z | ---
license: afl-3.0
tags:
- legal
---
This isn't a real model! This repository is used to demo the use of Hub [webhooks](https://huggingface.co/docs/hub/webhooks) for building a metadata quality review app. |
Anjoe/poetry-gpt2-large-with-hoel | Anjoe | 2023-01-31T15:17:57Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-01-31T11:32:35Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: poetry-gpt2-large-with-hoel
results: []
---
<!-- 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. -->
# poetry-gpt2-large-with-hoel
This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5612
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.673 | 1.0 | 20539 | 3.6197 |
| 3.299 | 2.0 | 41078 | 3.5369 |
| 3.0433 | 3.0 | 61617 | 3.5612 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jumang4423/ninjumango-jumango-v1-0 | jumang4423 | 2023-01-31T15:04:05Z | 13 | 2 | diffusers | [
"diffusers",
"text-to-audio",
"audio-to-audio",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-audio | 2023-01-22T16:49:57Z | ---
license: creativeml-openrail-m
tags:
- text-to-audio
- audio-to-audio
---
gen samples or filter samples with jumango textures!
|
bnowak1831/dqn-SpaceInvadersNoFrameskip-v4 | bnowak1831 | 2023-01-31T15:00:18Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T14:59:40Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 633.00 +/- 142.74
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bnowak1831 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bnowak1831 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bnowak1831
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
bdrighes/ppo-LunarLender-v2 | bdrighes | 2023-01-31T14:52:43Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T14:52:19Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.12 +/- 12.91
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
james0248/q-FrozenLake-v1-4x4-noSlippery | james0248 | 2023-01-31T14:49:47Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T14:49:46Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="james0248/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
squirro/albert-base-v2-squad_v2 | squirro | 2023-01-31T14:37:20Z | 91 | 3 | transformers | [
"transformers",
"pytorch",
"tf",
"onnx",
"albert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-03-07T10:57:20Z | ---
license: apache-2.0
language: en
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: albert-base-v2-squad_v2
results:
- task:
name: Question Answering
type: question-answering
dataset:
type: squad_v2 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: The Stanford Question Answering Dataset
args: en
metrics:
- type: eval_exact
value: 78.8175
- type: eval_f1
value: 81.9984
- type: eval_HasAns_exact
value: 75.3374
- type: eval_HasAns_f1
value: 81.7083
- type: eval_NoAns_exact
value: 82.2876
- type: eval_NoAns_f1
value: 82.2876
---
# albert-base-v2-squad_v2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset.
## Model description
This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/).
For convenience this model is prepared to be used with the frameworks `PyTorch`, `Tensorflow` and `ONNX`.
## Intended uses & limitations
This model can handle mismatched question-context pairs. Make sure to specify `handle_impossible_answer=True` when using `QuestionAnsweringPipeline`.
__Example usage:__
```python
>>> from transformers import AutoModelForQuestionAnswering, AutoTokenizer, QuestionAnsweringPipeline
>>> model = AutoModelForQuestionAnswering.from_pretrained("squirro/albert-base-v2-squad_v2")
>>> tokenizer = AutoTokenizer.from_pretrained("squirro/albert-base-v2-squad_v2")
>>> qa_model = QuestionAnsweringPipeline(model, tokenizer)
>>> qa_model(
>>> question="What's your name?",
>>> context="My name is Clara and I live in Berkeley.",
>>> handle_impossible_answer=True # important!
>>> )
{'score': 0.9027367830276489, 'start': 11, 'end': 16, 'answer': 'Clara'}
```
## Training and evaluation data
Training and evaluation was done on [SQuAD2.0](https://huggingface.co/datasets/squad_v2).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| key | value |
|:-------------------------|--------------:|
| epoch | 3 |
| eval_HasAns_exact | 75.3374 |
| eval_HasAns_f1 | 81.7083 |
| eval_HasAns_total | 5928 |
| eval_NoAns_exact | 82.2876 |
| eval_NoAns_f1 | 82.2876 |
| eval_NoAns_total | 5945 |
| eval_best_exact | 78.8175 |
| eval_best_exact_thresh | 0 |
| eval_best_f1 | 81.9984 |
| eval_best_f1_thresh | 0 |
| eval_exact | 78.8175 |
| eval_f1 | 81.9984 |
| eval_samples | 12171 |
| eval_total | 11873 |
| train_loss | 0.775293 |
| train_runtime | 1402 |
| train_samples | 131958 |
| train_samples_per_second | 282.363 |
| train_steps_per_second | 1.104 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
---
# About Us
<img src="https://squirro.com/wp-content/themes/squirro/img/squirro_logo.svg" alt="Squirro Logo" width="250"/>
Squirro marries data from any source with your intent, and your context to intelligently augment decision-making - right when you need it!
An Insight Engine at its core, Squirro works with global organizations, primarily in financial services, public sector, professional services, and manufacturing, among others. Customers include Bank of England, European Central Bank (ECB), Deutsche Bundesbank, Standard Chartered, Henkel, Armacell, Candriam, and many other world-leading firms.
Founded in 2012, Squirro is currently present in Zürich, London, New York, and Singapore. Further information about AI-driven business insights can be found at http://squirro.com.
## Social media profiles:
- Redefining AI Podcast (Spotify): https://open.spotify.com/show/6NPLcv9EyaD2DcNT8v89Kb
- Redefining AI Podcast (Apple Podcasts): https://podcasts.apple.com/us/podcast/redefining-ai/id1613934397
- Squirro LinkedIn: https://www.linkedin.com/company/squirroag
- Squirro Academy LinkedIn: https://www.linkedin.com/showcase/the-squirro-academy
- Twitter: https://twitter.com/Squirro
- Facebook: https://www.facebook.com/squirro
- Instagram: https://www.instagram.com/squirro/ |
danilyef/ppo-LunarLander-v2 | danilyef | 2023-01-31T14:24:08Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T14:23:48Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.24 +/- 13.64
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sd-dreambooth-library/iman-maleki-morteza-koutzian | sd-dreambooth-library | 2023-01-31T14:18:37Z | 7 | 0 | diffusers | [
"diffusers",
"tensorboard",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-11-15T17:02:50Z | ---
license: mit
---
### iman_maleki_morteza_koutzian on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### model by apurik-parv
This your the Stable Diffusion model fine-tuned the artwork of iman_maleki and morteza_koutzian two iranian painters, concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: **imamk**
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb),
imamk
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|
akanametov/audio-diffusion-electronic | akanametov | 2023-01-31T14:14:30Z | 1 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-audio-generation",
"diffusion-models-class",
"license:mit",
"diffusers:AudioDiffusionPipeline",
"region:us"
]
| null | 2023-01-31T14:14:11Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-audio-generation
- diffusion-models-class
---
# Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional audio generation of music in the genre Electronic
## Usage
```python
from IPython.display import Audio
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("akanametov/audio-diffusion-electronic")
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```
|
molsen/beitv2_martin | molsen | 2023-01-31T14:02:36Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-01-30T16:37:52Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: beitv2_martin
results: []
---
<!-- 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. -->
# beitv2_martin
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown 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: 9.576953286904372e-06
- 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: cosine
- lr_scheduler_warmup_steps: 1002
- num_epochs: 35
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
PlanTL-GOB-ES/roberta-large-bne | PlanTL-GOB-ES | 2023-01-31T14:00:29Z | 991 | 19 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"national library of spain",
"spanish",
"bne",
"roberta-large-bne",
"es",
"dataset:bne",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:04Z | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "roberta-large-bne"
datasets:
- "bne"
metrics:
- "ppl"
widget:
- text: "Por la ventanilla del coche vi la Giralda y pensé que bonita que es la ciudad de <mask>."
- text: "Más vale <mask> que lamentar."
- text: "Caminante no hay camino, se hace camino al <mask>."
- text: "Tengo una pelota roja y otra amarilla. Si le doy la roja a Jose, sólo me queda la <mask>."
- text: "Tengo una pelota roja y otra amarilla. Si le doy la amarilla a Jose, sólo me queda la <mask>."
- text: "El <mask> es el pico más alto de España."
---
# RoBERTa large trained with data from the National Library of Spain (BNE)
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Overview](#overview)
- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Training data](#training-data)
- [Training procedure](#training-procedure)
- [Evaluation](#evaluation)
- [Additional information](#additional-information)
- [Author](#author)
- [Contact information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licensing-information)
- [Funding](#funding)
- [Citation Information](#citation-information)
- [Disclaimer](#disclaimer)
</details>
## Overview
- **Architecture:** roberta-large
- **Language:** Spanish
- **Task:** fill-mask
- **Data:** BNE
## Model description
The **roberta-large-bne** is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
## Intended uses and limitations
The **roberta-large-bne** model is ready-to-use only for masked language modeling to perform the Fill Mask task (try the inference API or read the next section).
However, it is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification, or Named Entity Recognition.
You can use the raw model for fill mask or fine-tune it to a downstream task.
## How to use
Here is how to use this model:
```python
>>> from transformers import pipeline
>>> from pprint import pprint
>>> unmasker = pipeline('fill-mask', model='PlanTL-GOB-ES/roberta-large-bne')
>>> pprint(unmasker("Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."))
[{'score': 0.0664491355419159,
'sequence': ' Gracias a los datos de la BNE se ha podido conocer este modelo del lenguaje.',
'token': 1910,
'token_str': ' conocer'},
{'score': 0.0492338091135025,
'sequence': ' Gracias a los datos de la BNE se ha podido realizar este modelo del lenguaje.',
'token': 2178,
'token_str': ' realizar'},
{'score': 0.03890657424926758,
'sequence': ' Gracias a los datos de la BNE se ha podido reconstruir este modelo del lenguaje.',
'token': 23368,
'token_str': ' reconstruir'},
{'score': 0.03662774711847305,
'sequence': ' Gracias a los datos de la BNE se ha podido desarrollar este modelo del lenguaje.',
'token': 3815,
'token_str': ' desarrollar'},
{'score': 0.030557377263903618,
'sequence': ' Gracias a los datos de la BNE se ha podido estudiar este modelo del lenguaje.',
'token': 6361,
'token_str': ' estudiar'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
>>> from transformers import RobertaTokenizer, RobertaModel
>>> tokenizer = RobertaTokenizer.from_pretrained('PlanTL-GOB-ES/roberta-large-bne')
>>> model = RobertaModel.from_pretrained('PlanTL-GOB-ES/roberta-large-bne')
>>> text = "Gracias a los datos de la BNE se ha podido desarrollar este modelo del lenguaje."
>>> encoded_input = tokenizer(text, return_tensors='pt')
>>> output = model(**encoded_input)
>>> print(output.last_hidden_state.shape)
torch.Size([1, 19, 1024])
```
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Training
### Training data
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among others, sentence splitting, language detection, filtering of bad-formed sentences, and deduplication of repetitive contents. During the process, document boundaries are kept. This resulted in 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting in 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
### Training procedure
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens.
The **roberta-large-bne** pre-training consists of a masked language model training, that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
## Evaluation
When fine-tuned on downstream tasks, this model achieves the following results:
| Dataset | Metric | [**RoBERTa-large**](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) |
|--------------|----------|------------|
| MLDoc | F1 | 0.9702 |
| CoNLL-NERC | F1 | 0.8823 |
| CAPITEL-NERC | F1 | 0.9051 |
| PAWS-X | F1 | 0.9150 |
| UD-POS | F1 | 0.9904 |
| CAPITEL-POS | F1 | 0.9856 |
| SQAC | F1 | 0.8202 |
| STS | Combined | 0.8411 |
| XNLI | Accuracy | 0.8263 |
For more evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish) or [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405).
## Additional information
### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
### Contact information
For further information, send an email to <[email protected]>
### Copyright
Copyright by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) (2022)
### Licensing information
This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) within the framework of the Plan-TL.
### Citation information
If you use this model, please cite our [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405):
```
@article{,
abstract = {We want to thank the National Library of Spain for such a large effort on the data gathering and the Future of Computing Center, a
Barcelona Supercomputing Center and IBM initiative (2020). This work was funded by the Spanish State Secretariat for Digitalization and Artificial
Intelligence (SEDIA) within the framework of the Plan-TL.},
author = {Asier Gutiérrez Fandiño and Jordi Armengol Estapé and Marc Pàmies and Joan Llop Palao and Joaquin Silveira Ocampo and Casimiro Pio Carrino and Carme Armentano Oller and Carlos Rodriguez Penagos and Aitor Gonzalez Agirre and Marta Villegas},
doi = {10.26342/2022-68-3},
issn = {1135-5948},
journal = {Procesamiento del Lenguaje Natural},
keywords = {Artificial intelligence,Benchmarking,Data processing.,MarIA,Natural language processing,Spanish language modelling,Spanish language resources,Tractament del llenguatge natural (Informàtica),Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural},
publisher = {Sociedad Española para el Procesamiento del Lenguaje Natural},
title = {MarIA: Spanish Language Models},
volume = {68},
url = {https://upcommons.upc.edu/handle/2117/367156#.YyMTB4X9A-0.mendeley},
year = {2022},
}
```
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
</details> |
kjmann/a2c-PandaReachDense-v2 | kjmann | 2023-01-31T13:55:19Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T13:05:13Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.60 +/- 0.16
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
huggingtweets/thetweetofgod | huggingtweets | 2023-01-31T13:27:05Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1513636967090917378/u3n2blUC_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">God (Not a Parody, Actually God)</div>
<div style="text-align: center; font-size: 14px;">@thetweetofgod</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from God (Not a Parody, Actually God).
| Data | God (Not a Parody, Actually God) |
| --- | --- |
| Tweets downloaded | 3194 |
| Retweets | 112 |
| Short tweets | 99 |
| Tweets kept | 2983 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ihyce239/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thetweetofgod's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ln886cfe) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ln886cfe/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/thetweetofgod')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/microsoft | huggingtweets | 2023-01-31T13:06:17Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: http://www.huggingtweets.com/microsoft/1675170372180/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1585617858184323072/Vy138ToA_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Microsoft</div>
<div style="text-align: center; font-size: 14px;">@microsoft</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Microsoft.
| Data | Microsoft |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 174 |
| Short tweets | 811 |
| Tweets kept | 2265 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wtsi24dv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @microsoft's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/o8j42bpn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/o8j42bpn/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/microsoft')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
nlp04/gpt_trinity_2_4_3e-5_lp5_nb5 | nlp04 | 2023-01-31T13:02:09Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-01-31T10:21:51Z | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: gpt_trinity_2_4_3e-5_lp5_nb5
results: []
---
<!-- 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. -->
# gpt_trinity_2_4_3e-5_lp5_nb5
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0291
## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.5765 | 0.05 | 1000 | 4.1247 |
| 3.19 | 0.09 | 2000 | 4.0578 |
| 3.1177 | 0.14 | 3000 | 4.0708 |
| 3.1116 | 0.19 | 4000 | 4.0654 |
| 3.0777 | 0.24 | 5000 | 4.0857 |
| 3.1105 | 0.28 | 6000 | 4.1127 |
| 3.1018 | 0.33 | 7000 | 4.1410 |
| 3.0728 | 0.38 | 8000 | 4.1834 |
| 3.1248 | 0.42 | 9000 | 4.2058 |
| 3.1035 | 0.47 | 10000 | 4.2048 |
| 3.0943 | 0.52 | 11000 | 4.1892 |
| 3.0724 | 0.57 | 12000 | 4.2063 |
| 3.0517 | 0.61 | 13000 | 4.1923 |
| 3.0372 | 0.66 | 14000 | 4.2112 |
| 3.0235 | 0.71 | 15000 | 4.2043 |
| 3.0329 | 0.76 | 16000 | 4.1630 |
| 3.0171 | 0.8 | 17000 | 4.1631 |
| 2.9997 | 0.85 | 18000 | 4.1563 |
| 2.9913 | 0.9 | 19000 | 4.1616 |
| 2.9579 | 0.94 | 20000 | 4.1494 |
| 2.9576 | 0.99 | 21000 | 4.1367 |
| 2.7461 | 1.04 | 22000 | 4.1593 |
| 2.7637 | 1.09 | 23000 | 4.1453 |
| 2.741 | 1.13 | 24000 | 4.1624 |
| 2.7514 | 1.18 | 25000 | 4.1357 |
| 2.755 | 1.23 | 26000 | 4.1524 |
| 2.7365 | 1.27 | 27000 | 4.1399 |
| 2.7356 | 1.32 | 28000 | 4.1285 |
| 2.7386 | 1.37 | 29000 | 4.1286 |
| 2.7489 | 1.42 | 30000 | 4.1231 |
| 2.7518 | 1.46 | 31000 | 4.1104 |
| 2.7317 | 1.51 | 32000 | 4.1202 |
| 2.7378 | 1.56 | 33000 | 4.1132 |
| 2.7309 | 1.6 | 34000 | 4.1047 |
| 2.7791 | 1.65 | 35000 | 4.0976 |
| 2.7427 | 1.7 | 36000 | 4.0874 |
| 2.7184 | 1.75 | 37000 | 4.0953 |
| 2.7107 | 1.79 | 38000 | 4.0963 |
| 2.7122 | 1.84 | 39000 | 4.0841 |
| 2.7172 | 1.89 | 40000 | 4.0852 |
| 2.7126 | 1.94 | 41000 | 4.0632 |
| 2.7063 | 1.98 | 42000 | 4.0643 |
| 2.5311 | 2.03 | 43000 | 4.0848 |
| 2.4496 | 2.08 | 44000 | 4.0943 |
| 2.4597 | 2.12 | 45000 | 4.0799 |
| 2.4472 | 2.17 | 46000 | 4.0802 |
| 2.4628 | 2.22 | 47000 | 4.0880 |
| 2.4508 | 2.27 | 48000 | 4.0791 |
| 2.4743 | 2.31 | 49000 | 4.0765 |
| 2.4692 | 2.36 | 50000 | 4.0739 |
| 2.4651 | 2.41 | 51000 | 4.0690 |
| 2.4885 | 2.45 | 52000 | 4.0723 |
| 2.5023 | 2.5 | 53000 | 4.0675 |
| 2.4651 | 2.55 | 54000 | 4.0649 |
| 2.4774 | 2.6 | 55000 | 4.0695 |
| 2.4717 | 2.64 | 56000 | 4.0559 |
| 2.4856 | 2.69 | 57000 | 4.0512 |
| 2.4572 | 2.74 | 58000 | 4.0473 |
| 2.486 | 2.79 | 59000 | 4.0438 |
| 2.449 | 2.83 | 60000 | 4.0385 |
| 2.456 | 2.88 | 61000 | 4.0355 |
| 2.4802 | 2.93 | 62000 | 4.0378 |
| 2.4635 | 2.97 | 63000 | 4.0308 |
| 2.3742 | 3.02 | 64000 | 4.0488 |
| 2.2371 | 3.07 | 65000 | 4.0579 |
| 2.2496 | 3.12 | 66000 | 4.0630 |
| 2.2758 | 3.16 | 67000 | 4.0516 |
| 2.2489 | 3.21 | 68000 | 4.0585 |
| 2.2374 | 3.26 | 69000 | 4.0715 |
| 2.2862 | 3.3 | 70000 | 4.0507 |
| 2.2502 | 3.35 | 71000 | 4.0512 |
| 2.238 | 3.4 | 72000 | 4.0545 |
| 2.2407 | 3.45 | 73000 | 4.0459 |
| 2.2529 | 3.49 | 74000 | 4.0452 |
| 2.2453 | 3.54 | 75000 | 4.0459 |
| 2.2314 | 3.59 | 76000 | 4.0416 |
| 2.2408 | 3.63 | 77000 | 4.0379 |
| 2.2497 | 3.68 | 78000 | 4.0348 |
| 2.2475 | 3.73 | 79000 | 4.0374 |
| 2.2376 | 3.78 | 80000 | 4.0319 |
| 2.244 | 3.82 | 81000 | 4.0331 |
| 2.2611 | 3.87 | 82000 | 4.0306 |
| 2.237 | 3.92 | 83000 | 4.0301 |
| 2.2337 | 3.97 | 84000 | 4.0291 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.9.0+cu102
- Datasets 2.8.0
- Tokenizers 0.13.2
|
windsound/NLP4WebTask6 | windsound | 2023-01-31T12:50:02Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-01-29T16:25:31Z | ---
tags:
- generated_from_trainer
model-index:
- name: result
results: []
---
<!-- 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. -->
# result
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on an unknown 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
96harsh56/roberta-base-finetuned-squad | 96harsh56 | 2023-01-31T12:49:33Z | 29 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-01-31T09:26:42Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-squad
results: []
---
<!-- 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. -->
# roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0867
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.5824 | 1.0 | 5485 | 4.4179 |
| 4.2525 | 2.0 | 10970 | 4.1442 |
| 4.1607 | 3.0 | 16455 | 4.0867 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
Ekkel-AI-Pvt-ltd/saqib-t1400-u2000-31Jan2023 | Ekkel-AI-Pvt-ltd | 2023-01-31T12:41:28Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-01-31T12:28:51Z | ---
license: creativeml-openrail-m
---
|
camenduru/tensorrt-test | camenduru | 2023-01-31T12:39:53Z | 0 | 0 | null | [
"TensorRT",
"Text2Image",
"Stable Diffusion",
"Image2Image",
"SDA",
"region:us"
]
| null | 2023-01-31T12:33:56Z |
---
tags:
- TensorRT
- Text2Image
- Stable Diffusion
- Image2Image
- SDA
---
# andite/anything-v4.0 converted into TensorRT
<a href="https://github.com/chavinlo/sda-node/"><img src="https://i.imgur.com/fQS926g.png"></a>
Model converted from diffusers into TensorRT for accelerated inference up to 4x faster.
For how to use the model check https://github.com/chavinlo/sda-node
This model was automatically converted by SDA-node
Compilation configuration:
```json
{
"_class_name": "StableDiffusionAccelerated_Base",
"_sda_version": "0.1.2",
"_trt_version": "8.5.1",
"_cuda_version": "11.6",
"_cudnn_version": "7.5",
"_onnx2trt_version": "8.5.1",
"UNET": {
"precision": "fp16",
"path": "engine/unet_fp16.plan"
},
"CLIP": {
"path": "engine/clip.plan"
},
"DE_VAE": {
"path": "engine/de_vae.plan"
}
}
``` |
abhijitt/bert_st_qa_allminiLML6V2 | abhijitt | 2023-01-31T12:36:58Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-01-31T12:36:00Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 685 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 68,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
kjmann/a2c-AntBulletEnv-v0 | kjmann | 2023-01-31T12:16:14Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T12:15:07Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 2266.52 +/- 41.96
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Elifr/clasificador-muchocine-distilbert | Elifr | 2023-01-31T12:14:24Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-01-31T12:13:43Z | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine-distilbert
results: []
---
<!-- 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. -->
# clasificador-muchocine-distilbert
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3952
- Accuracy: 0.4710
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.2939 | 0.4465 |
| 1.2888 | 2.0 | 776 | 1.2575 | 0.4684 |
| 0.8575 | 3.0 | 1164 | 1.3952 | 0.4710 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ykurilov/protogenphotoreal20 | ykurilov | 2023-01-31T12:10:12Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-01-31T12:10:12Z | ---
license: creativeml-openrail-m
---
|
amrisaurus/pretrained-m-bert-400 | amrisaurus | 2023-01-31T12:04:19Z | 1 | 0 | transformers | [
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
]
| null | 2023-01-31T09:22:48Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: pretrained-m-bert-400
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pretrained-m-bert-400
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Epoch: 399
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2737 | 10.9284 | 0 |
| 7.7909 | 10.9299 | 1 |
| 6.9555 | 11.4698 | 2 |
| 6.4835 | 11.5760 | 3 |
| 6.3613 | 11.4740 | 4 |
| 6.3206 | 12.1134 | 5 |
| 6.5777 | 11.8247 | 6 |
| 6.0680 | 12.1764 | 7 |
| 5.9353 | 12.1038 | 8 |
| 6.0701 | 12.0687 | 9 |
| 6.0047 | 12.4985 | 10 |
| 5.9272 | 12.8107 | 11 |
| 5.9446 | 12.5006 | 12 |
| 5.9716 | 12.8384 | 13 |
| 6.0548 | 12.3585 | 14 |
| 5.8666 | 12.5273 | 15 |
| 5.8951 | 12.8032 | 16 |
| 5.9528 | 13.0296 | 17 |
| 5.8313 | 12.9472 | 18 |
| 5.8324 | 13.0546 | 19 |
| 5.8763 | 13.1180 | 20 |
| 5.7623 | 13.2588 | 21 |
| 5.8636 | 13.2705 | 22 |
| 5.8864 | 13.1756 | 23 |
| 5.7413 | 13.0413 | 24 |
| 5.6825 | 13.2796 | 25 |
| 5.8705 | 13.5748 | 26 |
| 5.7772 | 14.0853 | 27 |
| 5.8029 | 13.5957 | 28 |
| 5.9590 | 13.0809 | 29 |
| 5.8240 | 13.0912 | 30 |
| 5.7687 | 13.5828 | 31 |
| 5.9836 | 13.5432 | 32 |
| 5.6915 | 13.7082 | 33 |
| 5.7853 | 13.6080 | 34 |
| 5.7760 | 13.3992 | 35 |
| 5.7134 | 13.6552 | 36 |
| 5.8415 | 13.5479 | 37 |
| 5.8087 | 13.6847 | 38 |
| 5.8611 | 13.6231 | 39 |
| 5.6492 | 13.8722 | 40 |
| 5.7846 | 13.7056 | 41 |
| 5.7455 | 14.1208 | 42 |
| 5.7129 | 13.7774 | 43 |
| 5.7185 | 14.0725 | 44 |
| 5.6655 | 14.0980 | 45 |
| 5.6734 | 14.0126 | 46 |
| 5.6658 | 14.1982 | 47 |
| 5.6720 | 14.0881 | 48 |
| 5.8220 | 14.0841 | 49 |
| 5.8567 | 14.0512 | 50 |
| 5.6811 | 14.4002 | 51 |
| 5.7271 | 14.1651 | 52 |
| 5.7329 | 13.8948 | 53 |
| 5.9306 | 14.1744 | 54 |
| 5.6757 | 13.6761 | 55 |
| 5.8785 | 14.1815 | 56 |
| 5.7267 | 14.4592 | 57 |
| 5.7313 | 14.1220 | 58 |
| 5.7537 | 14.3197 | 59 |
| 5.7907 | 13.9449 | 60 |
| 5.6611 | 14.0992 | 61 |
| 5.7009 | 14.4630 | 62 |
| 5.6760 | 14.3946 | 63 |
| 5.7096 | 14.2776 | 64 |
| 5.7778 | 14.3569 | 65 |
| 5.7223 | 14.5246 | 66 |
| 5.7954 | 14.2003 | 67 |
| 5.9369 | 14.3692 | 68 |
| 5.8792 | 14.5358 | 69 |
| 5.7172 | 14.3076 | 70 |
| 5.6608 | 14.1833 | 71 |
| 5.8710 | 14.1070 | 72 |
| 5.6896 | 13.9720 | 73 |
| 5.7404 | 14.8816 | 74 |
| 5.8649 | 14.1338 | 75 |
| 5.8223 | 14.4951 | 76 |
| 5.7701 | 14.4468 | 77 |
| 5.7852 | 14.3300 | 78 |
| 5.7038 | 14.0439 | 79 |
| 5.7386 | 14.3995 | 80 |
| 5.7594 | 14.1541 | 81 |
| 5.7688 | 14.3452 | 82 |
| 5.6329 | 14.9077 | 83 |
| 5.8224 | 14.5802 | 84 |
| 5.7969 | 14.8311 | 85 |
| 5.8297 | 14.5400 | 86 |
| 5.6488 | 15.0369 | 87 |
| 5.8020 | 14.7833 | 88 |
| 5.7345 | 14.3675 | 89 |
| 5.7530 | 14.1602 | 90 |
| 5.7599 | 15.2734 | 91 |
| 5.7256 | 14.9461 | 92 |
| 5.6803 | 14.8461 | 93 |
| 5.8602 | 14.5303 | 94 |
| 5.6319 | 14.8087 | 95 |
| 5.7581 | 14.7901 | 96 |
| 5.6319 | 14.8252 | 97 |
| 5.6869 | 14.8271 | 98 |
| 5.7664 | 15.2408 | 99 |
| 5.7731 | 15.0161 | 100 |
| 5.7304 | 15.4610 | 101 |
| 5.8412 | 14.8069 | 102 |
| 5.7723 | 14.1676 | 103 |
| 5.6118 | 15.4048 | 104 |
| 5.7734 | 15.1165 | 105 |
| 5.8253 | 14.9264 | 106 |
| 5.8027 | 14.7596 | 107 |
| 5.7457 | 15.1671 | 108 |
| 5.7381 | 15.2209 | 109 |
| 5.8087 | 14.7408 | 110 |
| 5.8030 | 15.2581 | 111 |
| 5.8320 | 14.9718 | 112 |
| 5.6863 | 15.2960 | 113 |
| 5.8710 | 14.7119 | 114 |
| 5.7232 | 15.0268 | 115 |
| 5.8191 | 14.8410 | 116 |
| 5.6752 | 15.1243 | 117 |
| 5.8462 | 15.0258 | 118 |
| 5.6872 | 15.1223 | 119 |
| 5.7252 | 14.9306 | 120 |
| 5.8288 | 15.0880 | 121 |
| 5.7154 | 15.1128 | 122 |
| 5.7731 | 15.4266 | 123 |
| 5.7265 | 14.9659 | 124 |
| 5.6335 | 15.1716 | 125 |
| 5.7175 | 15.6793 | 126 |
| 5.7104 | 15.4801 | 127 |
| 5.6567 | 15.0955 | 128 |
| 5.9180 | 15.0188 | 129 |
| 5.5288 | 15.3623 | 130 |
| 5.7597 | 15.4404 | 131 |
| 5.8730 | 15.1977 | 132 |
| 5.7699 | 15.5542 | 133 |
| 5.6693 | 15.1629 | 134 |
| 5.7615 | 15.1227 | 135 |
| 5.7240 | 15.4303 | 136 |
| 5.7303 | 15.1579 | 137 |
| 5.7373 | 15.2233 | 138 |
| 5.8327 | 14.7246 | 139 |
| 5.6575 | 15.5657 | 140 |
| 5.6499 | 15.1918 | 141 |
| 5.7592 | 14.9380 | 142 |
| 5.7694 | 15.3017 | 143 |
| 5.7230 | 15.3452 | 144 |
| 5.8646 | 14.7439 | 145 |
| 5.7644 | 15.3110 | 146 |
| 5.7203 | 15.8015 | 147 |
| 5.6472 | 15.6682 | 148 |
| 5.8608 | 15.1659 | 149 |
| 5.7988 | 15.8322 | 150 |
| 5.8471 | 15.3636 | 151 |
| 5.7727 | 15.7957 | 152 |
| 5.6508 | 15.1050 | 153 |
| 5.7816 | 15.3157 | 154 |
| 5.8210 | 15.6138 | 155 |
| 5.7539 | 15.1308 | 156 |
| 5.7941 | 15.3775 | 157 |
| 5.6000 | 15.4838 | 158 |
| 5.7631 | 15.5708 | 159 |
| 5.7086 | 15.5326 | 160 |
| 5.7873 | 15.4520 | 161 |
| 5.7033 | 15.1639 | 162 |
| 5.8096 | 14.9752 | 163 |
| 5.7741 | 15.1489 | 164 |
| 5.6837 | 15.8508 | 165 |
| 5.6194 | 15.6911 | 166 |
| 5.8802 | 15.8204 | 167 |
| 5.7230 | 15.3510 | 168 |
| 5.7498 | 15.5641 | 169 |
| 5.6593 | 16.0866 | 170 |
| 5.7106 | 15.6393 | 171 |
| 5.7150 | 15.7347 | 172 |
| 5.8900 | 15.1096 | 173 |
| 5.6876 | 16.0257 | 174 |
| 5.7460 | 15.7940 | 175 |
| 5.6921 | 15.4479 | 176 |
| 5.7380 | 15.8687 | 177 |
| 5.6652 | 15.7234 | 178 |
| 5.7337 | 15.9722 | 179 |
| 5.7107 | 15.6004 | 180 |
| 5.6688 | 15.9564 | 181 |
| 5.7698 | 15.4953 | 182 |
| 5.6752 | 15.4695 | 183 |
| 5.7951 | 15.3870 | 184 |
| 5.6820 | 15.3892 | 185 |
| 5.7012 | 15.3069 | 186 |
| 5.8445 | 15.4246 | 187 |
| 5.7400 | 15.7290 | 188 |
| 5.7490 | 15.6652 | 189 |
| 5.7101 | 15.9525 | 190 |
| 5.8126 | 15.2409 | 191 |
| 5.7394 | 15.6082 | 192 |
| 5.6442 | 15.7932 | 193 |
| 5.7402 | 15.1385 | 194 |
| 5.7884 | 15.4226 | 195 |
| 5.5444 | 16.1383 | 196 |
| 5.7094 | 15.8144 | 197 |
| 5.6645 | 16.0524 | 198 |
| 5.6880 | 15.7864 | 199 |
| 5.7136 | 15.5176 | 200 |
| 5.6637 | 15.2914 | 201 |
| 5.6428 | 16.0657 | 202 |
| 5.7652 | 16.0983 | 203 |
| 5.7069 | 16.2021 | 204 |
| 5.7251 | 15.6593 | 205 |
| 5.7546 | 15.5634 | 206 |
| 5.6854 | 16.0332 | 207 |
| 5.7590 | 15.9248 | 208 |
| 5.7916 | 15.4682 | 209 |
| 5.7794 | 16.1797 | 210 |
| 5.6640 | 15.8133 | 211 |
| 5.5957 | 15.9253 | 212 |
| 5.8607 | 16.3813 | 213 |
| 5.7572 | 16.1253 | 214 |
| 5.7549 | 16.0874 | 215 |
| 5.6915 | 16.3464 | 216 |
| 5.6458 | 16.7042 | 217 |
| 5.6706 | 15.7191 | 218 |
| 5.7297 | 16.0799 | 219 |
| 5.6525 | 15.7766 | 220 |
| 5.6576 | 16.2920 | 221 |
| 5.6630 | 15.7460 | 222 |
| 5.7052 | 16.1921 | 223 |
| 5.7096 | 15.9475 | 224 |
| 5.8168 | 15.6872 | 225 |
| 5.7823 | 16.1230 | 226 |
| 5.7059 | 15.8028 | 227 |
| 5.7665 | 16.0063 | 228 |
| 5.7908 | 16.2456 | 229 |
| 5.7071 | 16.0749 | 230 |
| 5.7506 | 16.0597 | 231 |
| 5.6364 | 16.1518 | 232 |
| 5.7050 | 16.2710 | 233 |
| 5.7266 | 16.2172 | 234 |
| 5.7804 | 15.8192 | 235 |
| 5.7076 | 16.1186 | 236 |
| 5.6965 | 16.1123 | 237 |
| 5.7385 | 15.7495 | 238 |
| 5.7877 | 16.0528 | 239 |
| 5.5933 | 16.1774 | 240 |
| 5.6745 | 16.5711 | 241 |
| 5.6913 | 16.3114 | 242 |
| 5.7292 | 16.3525 | 243 |
| 5.7804 | 15.9284 | 244 |
| 5.6428 | 16.1581 | 245 |
| 5.6294 | 16.0005 | 246 |
| 5.7076 | 16.2986 | 247 |
| 5.7254 | 16.1134 | 248 |
| 5.7657 | 16.3133 | 249 |
| 5.7027 | 15.9364 | 250 |
| 5.6698 | 16.6521 | 251 |
| 5.6628 | 15.7597 | 252 |
| 5.6293 | 16.2164 | 253 |
| 5.6199 | 15.8001 | 254 |
| 5.6581 | 16.0590 | 255 |
| 5.6714 | 16.0894 | 256 |
| 5.7542 | 16.1121 | 257 |
| 5.7222 | 16.1358 | 258 |
| 5.8194 | 15.9213 | 259 |
| 5.6567 | 16.0372 | 260 |
| 5.6751 | 16.3755 | 261 |
| 5.6469 | 16.0893 | 262 |
| 5.6717 | 16.1122 | 263 |
| 5.7958 | 15.8767 | 264 |
| 5.8171 | 16.2429 | 265 |
| 5.7119 | 16.2250 | 266 |
| 5.7855 | 16.4065 | 267 |
| 5.7949 | 15.4456 | 268 |
| 5.7782 | 16.4003 | 269 |
| 5.7497 | 15.8927 | 270 |
| 5.6609 | 16.3852 | 271 |
| 5.8071 | 16.3739 | 272 |
| 5.7726 | 15.9497 | 273 |
| 5.6874 | 16.0825 | 274 |
| 5.7265 | 15.9471 | 275 |
| 5.8261 | 15.9685 | 276 |
| 5.6508 | 16.3370 | 277 |
| 5.6734 | 16.3040 | 278 |
| 5.6986 | 16.2803 | 279 |
| 5.7025 | 16.6162 | 280 |
| 5.7346 | 16.1483 | 281 |
| 5.6689 | 16.1718 | 282 |
| 5.6913 | 16.0822 | 283 |
| 5.7541 | 15.5025 | 284 |
| 5.7325 | 16.2702 | 285 |
| 5.8124 | 15.7343 | 286 |
| 5.6972 | 16.3263 | 287 |
| 5.7388 | 16.2631 | 288 |
| 5.7337 | 16.1185 | 289 |
| 5.5873 | 16.2938 | 290 |
| 5.6859 | 16.2026 | 291 |
| 5.8711 | 15.2906 | 292 |
| 5.6716 | 15.5945 | 293 |
| 5.7098 | 16.1930 | 294 |
| 5.6214 | 16.2915 | 295 |
| 5.7025 | 16.1317 | 296 |
| 5.6574 | 16.6203 | 297 |
| 5.7909 | 16.1098 | 298 |
| 5.8298 | 15.5068 | 299 |
| 5.7457 | 16.4390 | 300 |
| 5.6674 | 16.1994 | 301 |
| 5.6473 | 16.4981 | 302 |
| 5.7486 | 16.0311 | 303 |
| 5.5914 | 16.8805 | 304 |
| 5.7065 | 15.6899 | 305 |
| 5.7049 | 16.4121 | 306 |
| 5.7791 | 16.5573 | 307 |
| 5.7422 | 16.4121 | 308 |
| 5.6493 | 15.8135 | 309 |
| 5.7504 | 16.1579 | 310 |
| 5.7983 | 15.7995 | 311 |
| 5.6670 | 15.7541 | 312 |
| 5.6519 | 15.8321 | 313 |
| 5.7987 | 16.1384 | 314 |
| 5.8223 | 16.2025 | 315 |
| 5.7494 | 16.2737 | 316 |
| 5.7390 | 16.2242 | 317 |
| 5.7607 | 16.4318 | 318 |
| 5.6872 | 16.4799 | 319 |
| 5.7474 | 16.3387 | 320 |
| 5.8631 | 16.0204 | 321 |
| 5.7391 | 15.9252 | 322 |
| 5.6785 | 17.0023 | 323 |
| 5.6791 | 16.0897 | 324 |
| 5.7644 | 16.0529 | 325 |
| 5.7013 | 16.6206 | 326 |
| 5.8280 | 16.4234 | 327 |
| 5.6553 | 16.4436 | 328 |
| 5.6920 | nan | 329 |
| 5.7356 | 16.2001 | 330 |
| 5.7204 | 16.1120 | 331 |
| 5.5542 | 16.2315 | 332 |
| 5.7303 | 16.2902 | 333 |
| 5.6750 | 16.3221 | 334 |
| 5.6405 | 16.5904 | 335 |
| 5.8191 | 16.0871 | 336 |
| 5.7464 | 15.8915 | 337 |
| 5.7566 | 16.6865 | 338 |
| 5.7168 | 16.0537 | 339 |
| 5.6113 | 16.5037 | 340 |
| 5.6066 | 15.8096 | 341 |
| 5.6343 | 16.5955 | 342 |
| nan | nan | 343 |
| nan | nan | 344 |
| nan | nan | 345 |
| nan | nan | 346 |
| nan | nan | 347 |
| nan | nan | 348 |
| nan | nan | 349 |
| nan | nan | 350 |
| nan | nan | 351 |
| nan | nan | 352 |
| nan | nan | 353 |
| nan | nan | 354 |
| nan | nan | 355 |
| nan | nan | 356 |
| nan | nan | 357 |
| nan | nan | 358 |
| nan | nan | 359 |
| nan | nan | 360 |
| nan | nan | 361 |
| nan | nan | 362 |
| nan | nan | 363 |
| nan | nan | 364 |
| nan | nan | 365 |
| nan | nan | 366 |
| nan | nan | 367 |
| nan | nan | 368 |
| nan | nan | 369 |
| nan | nan | 370 |
| nan | nan | 371 |
| nan | nan | 372 |
| nan | nan | 373 |
| nan | nan | 374 |
| nan | nan | 375 |
| nan | nan | 376 |
| nan | nan | 377 |
| nan | nan | 378 |
| nan | nan | 379 |
| nan | nan | 380 |
| nan | nan | 381 |
| nan | nan | 382 |
| nan | nan | 383 |
| nan | nan | 384 |
| nan | nan | 385 |
| nan | nan | 386 |
| nan | nan | 387 |
| nan | nan | 388 |
| nan | nan | 389 |
| nan | nan | 390 |
| nan | nan | 391 |
| nan | nan | 392 |
| nan | nan | 393 |
| nan | nan | 394 |
| nan | nan | 395 |
| nan | nan | 396 |
| nan | nan | 397 |
| nan | nan | 398 |
| nan | nan | 399 |
### Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
EvaKlimentova/knots_distillprotbert_alphafold | EvaKlimentova | 2023-01-31T11:52:58Z | 17 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:EvaKlimentova/knots_AF",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-09T09:52:44Z | ---
datasets:
- EvaKlimentova/knots_AF
---
# M1 - finetuned DistillProtBert
The model is trained on [knots_AF dataset](https://huggingface.co/datasets/EvaKlimentova/knots_AF)
The accuracy on the test set is ~ 0.9787 |
iammartian0/ppo-Huggy | iammartian0 | 2023-01-31T11:47:49Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-01-31T11:47:26Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: iammartian0/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
pittawat/ppo-Huggy | pittawat | 2023-01-31T11:47:33Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-01-31T11:47:26Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: pittawat/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
biscuitboost/sd-1-5-robrew | biscuitboost | 2023-01-31T11:42:49Z | 1 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-01-31T11:27:19Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### SD-1-5-robrew Dreambooth model trained by biscuitboost with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
ddd24/ppo-LunarLander-v2 | ddd24 | 2023-01-31T11:30:49Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T10:14:50Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.91 +/- 23.07
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
laamaai/clasificador-muchocine | laamaai | 2023-01-31T10:22:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-01-31T10:20:57Z | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine
results: []
---
<!-- 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. -->
# clasificador-muchocine
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3877
- Accuracy: 0.4439
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.3596 | 0.3884 |
| 1.4301 | 2.0 | 776 | 1.2666 | 0.4323 |
| 1.0491 | 3.0 | 1164 | 1.3877 | 0.4439 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
liweiliu/Taxi-v3 | liweiliu | 2023-01-31T09:55:27Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T09:55:19Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="liweiliu/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
satoshiba/test-model | satoshiba | 2023-01-31T09:27:17Z | 3 | 0 | transformers | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-01-31T09:26:02Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: test-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# test-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Tokenizers 0.13.2
|
dogeplusplus/stable-sam | dogeplusplus | 2023-01-31T09:22:22Z | 4 | 0 | diffusers | [
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"sam-the-cat",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-01-09T18:49:12Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- sam-the-cat
widget:
- text: a photo of samruane cat
---
# sam
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('dogeplusplus/stable-sam')
image = pipeline().images[0]
image
```
|
erniechiew/sd-class-butterflies-32 | erniechiew | 2023-01-31T09:08:19Z | 0 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-01-31T09:08:09Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('erniechiew/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
jojoUla/bert-large-uncased-finetuned-DA-Zero-shot-20 | jojoUla | 2023-01-31T08:50:46Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-01-31T08:01:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-uncased-finetuned-DA-Zero-shot-20
results: []
---
<!-- 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-large-uncased-finetuned-DA-Zero-shot-20
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0118
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6214 | 1.0 | 435 | 1.1818 |
| 0.6285 | 2.0 | 870 | 1.2124 |
| 0.713 | 3.0 | 1305 | 1.1673 |
| 0.7902 | 4.0 | 1740 | 1.1342 |
| 0.8051 | 5.0 | 2175 | 1.1042 |
| 0.8167 | 6.0 | 2610 | 1.1086 |
| 0.8412 | 7.0 | 3045 | 1.0797 |
| 0.8885 | 8.0 | 3480 | 1.0575 |
| 0.918 | 9.0 | 3915 | 1.0749 |
| 0.9765 | 10.0 | 4350 | 1.0565 |
| 1.0009 | 11.0 | 4785 | 1.0509 |
| 0.986 | 12.0 | 5220 | 1.0564 |
| 0.9819 | 13.0 | 5655 | 1.0527 |
| 0.9786 | 14.0 | 6090 | 1.0064 |
| 0.9689 | 15.0 | 6525 | 1.0038 |
| 0.9481 | 16.0 | 6960 | 1.0186 |
| 0.955 | 17.0 | 7395 | 0.9860 |
| 0.9481 | 18.0 | 7830 | 0.9914 |
| 0.9452 | 19.0 | 8265 | 1.0173 |
| 0.9452 | 20.0 | 8700 | 1.0050 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
janko/grc_ner_joint_sm | janko | 2023-01-31T08:49:48Z | 4 | 0 | spacy | [
"spacy",
"token-classification",
"grc",
"model-index",
"region:us"
]
| token-classification | 2023-01-31T08:49:38Z | ---
tags:
- spacy
- token-classification
language:
- grc
model-index:
- name: grc_homercy_treebanks_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8635029354
- name: NER Recall
type: recall
value: 0.8847117794
- name: NER F Score
type: f_score
value: 0.8739787076
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.0
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.0
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.0
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.0
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.0
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.0
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.0
---
| Feature | Description |
| --- | --- |
| **Name** | `grc_homercy_treebanks_sm` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `frequency_lemmatizer`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `frequency_lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [marton & jan]() |
### Label Scheme
<details>
<summary>View label scheme (2302 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `---------`, `--p---fa-`, `--s---ma-`, `-3paia---`, `-3paim---`, `-3siia---`, `A-`, `C-`, `Df`, `Dq`, `Du`, `F-`, `G-`, `I-`, `Ma`, `Mo`, `Nb`, `Ne`, `Pc`, `Pd`, `Pi`, `Pk`, `Pp`, `Pr`, `Ps`, `Px`, `R-`, `S-`, `V-`, `a--------`, `a-------s`, `a-d---fa-`, `a-d---fd-`, `a-d---fg-`, `a-d---fn-`, `a-d---ma-`, `a-d---md-`, `a-d---mg-`, `a-d---mn-`, `a-d---mnc`, `a-d---mv-`, `a-d---na-`, `a-d---ng-`, `a-d---nn-`, `a-p----dc`, `a-p---fa-`, `a-p---fac`, `a-p---fas`, `a-p---fd-`, `a-p---fdc`, `a-p---fds`, `a-p---fg-`, `a-p---fgc`, `a-p---fn-`, `a-p---fnc`, `a-p---fns`, `a-p---fv-`, `a-p---m--`, `a-p---m-c`, `a-p---ma-`, `a-p---mac`, `a-p---mas`, `a-p---md-`, `a-p---mdc`, `a-p---mds`, `a-p---mg-`, `a-p---mgc`, `a-p---mgs`, `a-p---mn-`, `a-p---mnc`, `a-p---mns`, `a-p---mv-`, `a-p---mvs`, `a-p---na-`, `a-p---nac`, `a-p---nas`, `a-p---nd-`, `a-p---ndc`, `a-p---nds`, `a-p---ng-`, `a-p---ngs`, `a-p---nn-`, `a-p---nnc`, `a-p---nns`, `a-p---nv-`, `a-s----d-`, `a-s----dc`, `a-s----g-`, `a-s----gc`, `a-s---fa-`, `a-s---fac`, `a-s---fas`, `a-s---fd-`, `a-s---fds`, `a-s---fg-`, `a-s---fgc`, `a-s---fgs`, `a-s---fn-`, `a-s---fnc`, `a-s---fns`, `a-s---fv-`, `a-s---m--`, `a-s---ma-`, `a-s---mac`, `a-s---mas`, `a-s---md-`, `a-s---mdc`, `a-s---mds`, `a-s---mg-`, `a-s---mgc`, `a-s---mgs`, `a-s---mn-`, `a-s---mnc`, `a-s---mns`, `a-s---mv-`, `a-s---mvc`, `a-s---mvs`, `a-s---na-`, `a-s---nac`, `a-s---nas`, `a-s---nd-`, `a-s---ndc`, `a-s---nds`, `a-s---ng-`, `a-s---nn-`, `a-s---nnc`, `a-s---nns`, `a-s---nv-`, `a-s---nvs`, `c--------`, `d--------`, `d-------c`, `d-------s`, `g--------`, `i--------`, `l--------`, `l-d---fa-`, `l-d---fg-`, `l-d---mg-`, `l-d---mn-`, `l-d---na-`, `l-d---nn-`, `l-p---fa-`, `l-p---fd-`, `l-p---fg-`, `l-p---fn-`, `l-p---ma-`, `l-p---md-`, `l-p---mg-`, `l-p---mn-`, `l-p---na-`, `l-p---nd-`, `l-p---ng-`, `l-p---nn-`, `l-s---fa-`, `l-s---fd-`, `l-s---fg-`, `l-s---fn-`, `l-s---ma-`, `l-s---md-`, `l-s---mg-`, `l-s---mn-`, `l-s---na-`, `l-s---nd-`, `l-s---ng-`, `l-s---nn-`, `m--------`, `m-p---m--`, `m-p---md-`, `m-p---nn-`, `n-----fg-`, `n-----na-`, `n-----nn-`, `n-d----a-`, `n-d---fa-`, `n-d---fd-`, `n-d---fg-`, `n-d---fn-`, `n-d---ma-`, `n-d---md-`, `n-d---mg-`, `n-d---mn-`, `n-d---mv-`, `n-d---na-`, `n-d---nn-`, `n-p----d-`, `n-p----g-`, `n-p---fa-`, `n-p---fd-`, `n-p---fg-`, `n-p---fn-`, `n-p---fv-`, `n-p---ma-`, `n-p---md-`, `n-p---mg-`, `n-p---mn-`, `n-p---mv-`, `n-p---na-`, `n-p---nd-`, `n-p---ng-`, `n-p---nn-`, `n-p---nv-`, `n-s----d-`, `n-s----g-`, `n-s----n-`, `n-s----v-`, `n-s---fa-`, `n-s---fd-`, `n-s---fg-`, `n-s---fn-`, `n-s---fv-`, `n-s---m--`, `n-s---ma-`, `n-s---md-`, `n-s---mg-`, `n-s---mn-`, `n-s---mv-`, `n-s---na-`, `n-s---nd-`, `n-s---ng-`, `n-s---nn-`, `n-s---nv-`, `p--------`, `p-d----d-`, `p-d----n-`, `p-d---fa-`, `p-d---fd-`, `p-d---fg-`, `p-d---fn-`, `p-d---ma-`, `p-d---md-`, `p-d---mg-`, `p-d---mn-`, `p-d---mv-`, `p-p----a-`, `p-p----d-`, `p-p----g-`, `p-p----n-`, `p-p---fa-`, `p-p---fd-`, `p-p---fg-`, `p-p---fn-`, `p-p---ma-`, `p-p---md-`, `p-p---mg-`, `p-p---mn-`, `p-p---na-`, `p-p---nd-`, `p-p---ng-`, `p-p---nn-`, `p-s----a-`, `p-s----d-`, `p-s----g-`, `p-s----n-`, `p-s---fa-`, `p-s---fd-`, `p-s---fg-`, `p-s---fn-`, `p-s---ma-`, `p-s---md-`, `p-s---mg-`, `p-s---mn-`, `p-s---mv-`, `p-s---na-`, `p-s---nd-`, `p-s---ng-`, `p-s---nn-`, `p1p---fa-`, `p1p---ma-`, `p1p---md-`, `p1p---mg-`, `p1p---mn-`, `p1s---fa-`, `p1s---fd-`, `p1s---fg-`, `p1s---fn-`, `p1s---ma-`, `p1s---md-`, `p1s---mg-`, `p1s---mn-`, `p2p----a-`, `p2p----d-`, `p2p---ma-`, `p2p---mg-`, `p2p---mn-`, `p2s----a-`, `p2s----d-`, `p2s----g-`, `p2s----n-`, `p2s---ma-`, `p2s---md-`, `p2s---mg-`, `p3s---fa-`, `p3s---ma-`, `r--------`, `u--------`, `v---na---`, `v--amm---`, `v--an----`, `v--ana---`, `v--ane---`, `v--anm---`, `v--anp---`, `v--fna---`, `v--fne---`, `v--fnm---`, `v--fnp---`, `v--pna---`, `v--pnd---`, `v--pne---`, `v--pnp---`, `v--ppefa-`, `v--ppemn-`, `v--rn----`, `v--rna---`, `v--rne---`, `v--rnp---`, `v--tna---`, `v-dapafn-`, `v-dapama-`, `v-dapamg-`, `v-dapamn-`, `v-dapmfn-`, `v-dapmmn-`, `v-dappma-`, `v-dappmn-`, `v-dppafg-`, `v-dppama-`, `v-dppamn-`, `v-dppefn-`, `v-dppema-`, `v-dppemd-`, `v-dppemn-`, `v-dpppmn-`, `v-drpama-`, `v-drpamn-`, `v-drpefn-`, `v-drpemn-`, `v-p-pmma-`, `v-pap-mn-`, `v-papafa-`, `v-papafg-`, `v-papafn-`, `v-papama-`, `v-papamd-`, `v-papamg-`, `v-papamn-`, `v-papana-`, `v-papand-`, `v-papann-`, `v-papefn-`, `v-papema-`, `v-papemn-`, `v-papmfa-`, `v-papmfg-`, `v-papmfn-`, `v-papmma-`, `v-papmmd-`, `v-papmmg-`, `v-papmmn-`, `v-papmna-`, `v-papmng-`, `v-papmnn-`, `v-pappfd-`, `v-pappfg-`, `v-pappfn-`, `v-pappma-`, `v-pappmd-`, `v-pappmg-`, `v-pappmn-`, `v-pappna-`, `v-pappng-`, `v-pappnn-`, `v-pfpama-`, `v-pfpamg-`, `v-pfpamn-`, `v-pfpema-`, `v-pfpemn-`, `v-pfpmfa-`, `v-pfpmfn-`, `v-pfpmma-`, `v-pfpmmd-`, `v-pfpmmg-`, `v-pfpmmn-`, `v-pfpmnn-`, `v-pfppmn-`, `v-ppp-mn-`, `v-pppafa-`, `v-pppafd-`, `v-pppafg-`, `v-pppafn-`, `v-pppafv-`, `v-pppama-`, `v-pppamd-`, `v-pppamg-`, `v-pppamn-`, `v-pppamv-`, `v-pppana-`, `v-pppand-`, `v-pppang-`, `v-pppann-`, `v-pppefa-`, `v-pppefd-`, `v-pppefg-`, `v-pppefn-`, `v-pppefv-`, `v-pppema-`, `v-pppemd-`, `v-pppemg-`, `v-pppemn-`, `v-pppemv-`, `v-pppena-`, `v-pppend-`, `v-pppeng-`, `v-pppenn-`, `v-ppppma-`, `v-ppppmd-`, `v-ppppmn-`, `v-prp-mn-`, `v-prpafa-`, `v-prpafd-`, `v-prpafn-`, `v-prpama-`, `v-prpamd-`, `v-prpamg-`, `v-prpamn-`, `v-prpana-`, `v-prpang-`, `v-prpefa-`, `v-prpefd-`, `v-prpefg-`, `v-prpefn-`, `v-prpema-`, `v-prpemd-`, `v-prpemg-`, `v-prpemn-`, `v-prpena-`, `v-prpend-`, `v-prpeng-`, `v-prpenn-`, `v-prppfn-`, `v-prppmn-`, `v-sagamn-`, `v-saiamn-`, `v-samp---`, `v-sap-mg-`, `v-sap-mn-`, `v-sapafa-`, `v-sapafd-`, `v-sapafg-`, `v-sapafn-`, `v-sapama-`, `v-sapamd-`, `v-sapamg-`, `v-sapamn-`, `v-sapamv-`, `v-sapana-`, `v-sapang-`, `v-sapann-`, `v-sapanv-`, `v-sapema-`, `v-sapemn-`, `v-sapmfa-`, `v-sapmfd-`, `v-sapmfg-`, `v-sapmfn-`, `v-sapmma-`, `v-sapmmd-`, `v-sapmmg-`, `v-sapmmn-`, `v-sapmna-`, `v-sapmng-`, `v-sapmnn-`, `v-sappfa-`, `v-sappfd-`, `v-sappfg-`, `v-sappfn-`, `v-sappma-`, `v-sappmd-`, `v-sappmg-`, `v-sappmn-`, `v-sappna-`, `v-sappng-`, `v-sappnn-`, `v-sappnv-`, `v-sfpafa-`, `v-sfpafd-`, `v-sfpafn-`, `v-sfpama-`, `v-sfpamd-`, `v-sfpamg-`, `v-sfpamn-`, `v-sfpmfa-`, `v-sfpmfd-`, `v-sfpmfg-`, `v-sfpmfn-`, `v-sfpmma-`, `v-sfpmmg-`, `v-sfpmmn-`, `v-sfpmna-`, `v-sfppma-`, `v-spiamn-`, `v-spp-mn-`, `v-spp-nn-`, `v-sppa---`, `v-sppafa-`, `v-sppafd-`, `v-sppafg-`, `v-sppafn-`, `v-sppafv-`, `v-sppama-`, `v-sppamd-`, `v-sppamg-`, `v-sppamn-`, `v-sppamv-`, `v-sppana-`, `v-sppand-`, `v-sppang-`, `v-sppann-`, `v-sppanv-`, `v-sppefa-`, `v-sppefd-`, `v-sppefg-`, `v-sppefn-`, `v-sppema-`, `v-sppemd-`, `v-sppemg-`, `v-sppemn-`, `v-sppemv-`, `v-sppena-`, `v-sppend-`, `v-sppeng-`, `v-sppenn-`, `v-spppfa-`, `v-spppfd-`, `v-spppfg-`, `v-spppfn-`, `v-spppma-`, `v-spppmn-`, `v-srp-mn-`, `v-srpafa-`, `v-srpafd-`, `v-srpafg-`, `v-srpafn-`, `v-srpama-`, `v-srpamd-`, `v-srpamg-`, `v-srpamn-`, `v-srpamv-`, `v-srpana-`, `v-srpand-`, `v-srpang-`, `v-srpann-`, `v-srpefa-`, `v-srpefd-`, `v-srpefg-`, `v-srpefn-`, `v-srpema-`, `v-srpemd-`, `v-srpemg-`, `v-srpemn-`, `v-srpemv-`, `v-srpena-`, `v-srpend-`, `v-srpeng-`, `v-srpenn-`, `v-srppfn-`, `v-srppma-`, `v-srppmn-`, `v-srppmv-`, `v1paia---`, `v1paim---`, `v1paip---`, `v1paoa---`, `v1paom---`, `v1paop---`, `v1pasa---`, `v1pase---`, `v1pasm---`, `v1pasp---`, `v1pfia---`, `v1pfim---`, `v1pfom---`, `v1piia---`, `v1piie---`, `v1plia---`, `v1plie---`, `v1ppia---`, `v1ppie---`, `v1ppip---`, `v1ppoa---`, `v1ppoe---`, `v1ppsa---`, `v1ppse---`, `v1pria---`, `v1prie---`, `v1prsa---`, `v1prse---`, `v1ptie---`, `v1s-sa---`, `v1sa-a---`, `v1saia---`, `v1saie---`, `v1saim---`, `v1saip---`, `v1sao----`, `v1saoa---`, `v1saoe---`, `v1saom---`, `v1saop---`, `v1sasa---`, `v1sase---`, `v1sasm---`, `v1sasp---`, `v1sfi----`, `v1sfia---`, `v1sfie---`, `v1sfim---`, `v1sfip---`, `v1siia---`, `v1siie---`, `v1slia---`, `v1slie---`, `v1slim---`, `v1spia---`, `v1spie---`, `v1spoa---`, `v1spoe---`, `v1spsa---`, `v1spse---`, `v1sria---`, `v1srie---`, `v1sroa---`, `v1sroe---`, `v1srsa---`, `v1stie---`, `v1stim---`, `v2daia---`, `v2dama---`, `v2dasa---`, `v2dase---`, `v2dfia---`, `v2dfim---`, `v2diia---`, `v2diie---`, `v2dpia---`, `v2dpma---`, `v2dpme---`, `v2dria---`, `v2drma---`, `v2paia---`, `v2paim---`, `v2paip---`, `v2pama---`, `v2pame---`, `v2pamm---`, `v2paoa---`, `v2paom---`, `v2paop---`, `v2pasa---`, `v2pase---`, `v2pasm---`, `v2pasp---`, `v2pfia---`, `v2pfim---`, `v2piia---`, `v2piie---`, `v2ppia---`, `v2ppie---`, `v2ppma---`, `v2ppme---`, `v2ppoa---`, `v2ppoe---`, `v2ppsa---`, `v2pria---`, `v2prie---`, `v2prma---`, `v2prmp---`, `v2proa---`, `v2prsa---`, `v2saia---`, `v2saie---`, `v2saim---`, `v2saip---`, `v2sam----`, `v2sama---`, `v2same---`, `v2samm---`, `v2samp---`, `v2saoa---`, `v2saoe---`, `v2saom---`, `v2saop---`, `v2sasa---`, `v2sase---`, `v2sasm---`, `v2sasp---`, `v2sfi----`, `v2sfia---`, `v2sfie---`, `v2sfim---`, `v2sfip---`, `v2siia---`, `v2siie---`, `v2siip---`, `v2slia---`, `v2slie---`, `v2slim---`, `v2spia---`, `v2spie---`, `v2spma---`, `v2spme---`, `v2spoa---`, `v2spoe---`, `v2spsa---`, `v2spse---`, `v2sria---`, `v2srie---`, `v2srma---`, `v2srme---`, `v2sroa---`, `v2srsa---`, `v2stie---`, `v3-roe---`, `v3daia---`, `v3daim---`, `v3daip---`, `v3daoa---`, `v3dfia---`, `v3dfim---`, `v3diia---`, `v3diie---`, `v3dlia---`, `v3dlie---`, `v3dlim---`, `v3dpia---`, `v3dpie---`, `v3dpma---`, `v3dpme---`, `v3dpsa---`, `v3dria---`, `v3pai----`, `v3paia---`, `v3paie---`, `v3paim---`, `v3paip---`, `v3pamm---`, `v3paoa---`, `v3paoe---`, `v3paom---`, `v3paop---`, `v3pasa---`, `v3pase---`, `v3pasm---`, `v3pasp---`, `v3pfia---`, `v3pfie---`, `v3pfim---`, `v3piia---`, `v3piie---`, `v3piip---`, `v3plia---`, `v3plie---`, `v3plim---`, `v3plip---`, `v3ppia---`, `v3ppie---`, `v3ppip---`, `v3ppma---`, `v3ppme---`, `v3ppoa---`, `v3ppoe---`, `v3ppsa---`, `v3ppse---`, `v3pria---`, `v3prie---`, `v3prip---`, `v3sai----`, `v3saia---`, `v3saie---`, `v3saim---`, `v3saip---`, `v3sama---`, `v3samm---`, `v3samp---`, `v3sana---`, `v3sao----`, `v3saoa---`, `v3saoe---`, `v3saom---`, `v3saop---`, `v3sas----`, `v3sasa---`, `v3sase---`, `v3sasm---`, `v3sasp---`, `v3sfi----`, `v3sfia---`, `v3sfie---`, `v3sfim---`, `v3sfip---`, `v3sfoa---`, `v3sii----`, `v3siia---`, `v3siie---`, `v3siip---`, `v3sli----`, `v3slia---`, `v3slie---`, `v3slim---`, `v3slip---`, `v3spia---`, `v3spie---`, `v3spip---`, `v3spma---`, `v3spme---`, `v3spoa---`, `v3spoe---`, `v3spop---`, `v3spsa---`, `v3spse---`, `v3sria---`, `v3srie---`, `v3srip---`, `v3srma---`, `v3sroa---`, `v3srsa---`, `v3stie---`, `v3stim---`, `v3stip---`, `x--------`, `x-p----d-`, `x-p---nn-` |
| **`morphologizer`** | `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET`, `POS=SCONJ`, `POS=CCONJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Mid,Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Opt\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Degree=Pos\|POS=ADV`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=INTJ`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Rel`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=DET`, `POS=ADV\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `POS=AUX\|Tense=Fut\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Cmp\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Gdv`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=VERB`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Gdv`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Gdv`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Voc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Opt\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Gdv`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Degree=Cmp\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Voc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=NUM`, `POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=X`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=DET`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Voc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|POS=AUX\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Nom\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Dat\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|POS=AUX\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `POS=PUNCT`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PART`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON`, `POS=DET`, `Case=Gen\|Number=Sing\|POS=PRON`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Number=Sing\|POS=PRON`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PRON`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Voc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=PRON`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=X`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Number=Sing\|POS=PRON`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=NOUN`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `POS=VERB\|Tense=Past\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Dual\|POS=PRON`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=X`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=DET`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Imp\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Number=Dual\|POS=PRON`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|POS=NOUN`, `Case=Acc\|Gender=Neut\|POS=NOUN`, `Case=Dat\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=DET`, `Case=Nom\|Gender=Neut\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Opt\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=PRON`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Voc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Number=Plur\|POS=PRON`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Degree=Sup\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Nom\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=X`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `POS=VERB\|Tense=Pres\|VerbForm=Inf`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Number=Dual\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=X`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`ner`** | `GROUP`, `PERSON`, `PLACE` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 0.00 |
| `POS_ACC` | 0.00 |
| `MORPH_ACC` | 0.00 |
| `MORPH_PER_FEAT` | 0.00 |
| `DEP_UAS` | 0.00 |
| `DEP_LAS` | 0.00 |
| `DEP_LAS_PER_TYPE` | 0.00 |
| `SENTS_P` | 0.00 |
| `SENTS_R` | 0.00 |
| `SENTS_F` | 0.00 |
| `LEMMA_ACC` | 0.00 |
| `ENTS_F` | 87.40 |
| `ENTS_P` | 86.35 |
| `ENTS_R` | 88.47 |
| `TOK2VEC_LOSS` | 42181.56 |
| `TAGGER_LOSS` | 0.00 |
| `MORPHOLOGIZER_LOSS` | 0.00 |
| `PARSER_LOSS` | 0.00 |
| `TRAINABLE_LEMMATIZER_LOSS` | 0.00 |
| `NER_LOSS` | 32227.48 | |
scy99/autotrain-hello_summarization-3171289572 | scy99 | 2023-01-31T08:45:52Z | 8 | 1 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"summarization",
"zh",
"dataset:scy99/autotrain-data-hello_summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| summarization | 2023-01-31T08:31:37Z | ---
tags:
- autotrain
- summarization
language:
- zh
widget:
- text: "I love AutoTrain 🤗"
datasets:
- scy99/autotrain-data-hello_summarization
co2_eq_emissions:
emissions: 25.535336151027007
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 3171289572
- CO2 Emissions (in grams): 25.5353
## Validation Metrics
- Loss: 3.536
- Rouge1: 38.529
- Rouge2: 6.769
- RougeL: 38.154
- RougeLsum: 37.958
- Gen Len: 18.864
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/scy99/autotrain-hello_summarization-3171289572
``` |
hxssgaa/dstc_setfit | hxssgaa | 2023-01-31T08:30:26Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-01-31T05:56:23Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7680 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 7680,
"warmup_steps": 768,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
guymorlan/English2Dialect | guymorlan | 2023-01-31T08:28:53Z | 29 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"Levantine Arabic",
"Shami",
"English",
"Egyptian",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-01-30T20:20:13Z | ---
tags:
- Levantine Arabic
- Shami
- English
- Egyptian
---
# English2Dialect
This model generates translations from English to colloquial Arabic, conditioning the translations on dialect. Supported dialects are: Palestinian/Jordanian, Syrian, Lebanese and Egyptian.
Dialect is specified via the first input token which should be either P/S/L/E.
For example, to translate to Palestinian/Jordanian, use the input:
`P What time is it now?`
And for Syrian:
`S What time is it now?`
- **Demo:** https://huggingface.co/spaces/guymorlan/English2Shami
- **Version w/o conditional generation (Levantine only):** https://huggingface.co/guymorlan/English2Shami
<!-- Provide a quick summary of what the model is/does. -->
## Training Data
The model was trained by fine-tuning the opus-mt-ar-en (MSA to English) model on ~85K parallel sentences in four dialects of colloquial Arabic.
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Guy Mor-Lan ([email protected])
- **Model type:** MarianMT Seq2Seq
- **License:** MIT
- **Finetuned from model:** Helsinki-NLP/opus-mt-ar-en[https://huggingface.co/Helsinki-NLP/opus-mt-ar-en]
<!--- **Repository:** [More Information Needed] --> |
kakaobrain/karlo-v1-alpha-image-variations | kakaobrain | 2023-01-31T08:27:48Z | 292 | 7 | diffusers | [
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"diffusers:UnCLIPImageVariationPipeline",
"region:us"
]
| text-to-image | 2023-01-30T19:46:46Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
# Karlo v1 alpha
Karlo is a text-conditional image generation model based on OpenAI's unCLIP architecture with the improvement over the standard super-resolution model from 64px to 256px, recovering high-frequency details only in the small number of denoising steps.
* [Original codebase](https://github.com/kakaobrain/karlo)
## Usage
Karlo is available in diffusers!
```python
pip install diffusers transformers accelerate safetensors
```
### Text to image
```python
from diffusers import UnCLIPPipeline
import torch
pipe = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16)
pipe = pipe.to('cuda')
prompt = "a high-resolution photograph of a big red frog on a green leaf."
image = pipe([prompt]).images[0]
image.save("./frog.png")
```
### Image variation
```python
from diffusers import UnCLIPImageVariationPipeline
import torch
from PIL import Image
pipe = UnCLIPImageVariationPipeline.from_pretrained("kakaobrain/karlo-v1-alpha-image-variations", torch_dtype=torch.float16)
pipe = pipe.to('cuda')
image = Image.open("./frog.png")
image = pipe(image).images[0]
image.save("./frog-variation.png")
```
## Model Architecture
### Overview
Karlo is a text-conditional diffusion model based on unCLIP, composed of prior, decoder, and super-resolution modules. In this repository, we include the improved version of the standard super-resolution module for upscaling 64px to 256px only in 7 reverse steps, as illustrated in the figure below:
<p float="left">
<img src="https://raw.githubusercontent.com/kakaobrain/karlo/main/assets/improved_sr_arch.jpg"/>
</p>
In specific, the standard SR module trained by DDPM objective upscales 64px to 256px in the first 6 denoising steps based on the respacing technique. Then, the additional fine-tuned SR module trained by [VQ-GAN](https://compvis.github.io/taming-transformers/)-style loss performs the final reverse step to recover high-frequency details. We observe that this approach is very effective to upscale the low-resolution in a small number of reverse steps.
### Details
We train all components from scratch on 115M image-text pairs including COYO-100M, CC3M, and CC12M. In the case of Prior and Decoder, we use ViT-L/14 provided by OpenAI’s [CLIP repository](https://github.com/openai/CLIP). Unlike the original implementation of unCLIP, we replace the trainable transformer in the decoder into the text encoder in ViT-L/14 for efficiency. In the case of the SR module, we first train the model using the DDPM objective in 1M steps, followed by additional 234K steps to fine-tune the additional component. The table below summarizes the important statistics of our components:
| | Prior | Decoder | SR |
|:------|----:|----:|----:|
| CLIP | ViT-L/14 | ViT-L/14 | - |
| #param | 1B | 900M | 700M + 700M |
| #optimization steps | 1M | 1M | 1M + 0.2M |
| #sampling steps | 25 | 50 (default), 25 (fast) | 7 |
|Checkpoint links| [ViT-L-14](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/096db1af569b284eb76b3881534822d9/ViT-L-14.pt), [ViT-L-14 stats](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th), [model](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt) | [model](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt) | [model](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt) |
In the checkpoint links, ViT-L-14 is equivalent to the original version, but we include it for convenience. We also remark that ViT-L-14-stats is required to normalize the outputs of the prior module.
### Evaluation
We quantitatively measure the performance of Karlo-v1.0.alpha in the validation split of CC3M and MS-COCO. The table below presents CLIP-score and FID. To measure FID, we resize the image of the shorter side to 256px, followed by cropping it at the center. We set classifier-free guidance scales for prior and decoder to 4 and 8 in all cases. We observe that our model achieves reasonable performance even with 25 sampling steps of decoder.
CC3M
| Sampling step | CLIP-s (ViT-B/16) | FID (13k from val)|
|:------|----:|----:|
| Prior (25) + Decoder (25) + SR (7) | 0.3081 | 14.37 |
| Prior (25) + Decoder (50) + SR (7) | 0.3086 | 13.95 |
MS-COCO
| Sampling step | CLIP-s (ViT-B/16) | FID (30k from val)|
|:------|----:|----:|
| Prior (25) + Decoder (25) + SR (7) | 0.3192 | 15.24 |
| Prior (25) + Decoder (50) + SR (7) | 0.3192 | 14.43 |
For more information, please refer to the upcoming technical report.
### Training Details
This alpha version of Karlo is trained on 115M image-text pairs,
including [COYO](https://github.com/kakaobrain/coyo-dataset)-100M high-quality subset, CC3M, and CC12M.
For those who are interested in a better version of Karlo trained on more large-scale high-quality datasets,
please visit the landing page of our application [B^DISCOVER](https://bdiscover.kakaobrain.com/).
## BibTex
If you find this repository useful in your research, please cite:
```
@misc{kakaobrain2022karlo-v1-alpha,
title = {Karlo-v1.0.alpha on COYO-100M and CC15M},
author = {Donghoon Lee, Jiseob Kim, Jisu Choi, Jongmin Kim, Minwoo Byeon, Woonhyuk Baek and Saehoon Kim},
year = {2022},
howpublished = {\url{https://github.com/kakaobrain/karlo}},
}
```
|
Sushovan/autotrain-test-text-classification-3175589570 | Sushovan | 2023-01-31T08:20:56Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:Sushovan/autotrain-data-test-text-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-01-31T08:18:55Z | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Sushovan/autotrain-data-test-text-classification
co2_eq_emissions:
emissions: 3.2260052742267447
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3175589570
- CO2 Emissions (in grams): 3.2260
## Validation Metrics
- Loss: 1.111
- Accuracy: 0.665
- Macro F1: 0.424
- Micro F1: 0.665
- Weighted F1: 0.638
- Macro Precision: 0.427
- Micro Precision: 0.665
- Weighted Precision: 0.622
- Macro Recall: 0.434
- Micro Recall: 0.665
- Weighted Recall: 0.665
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/Sushovan/autotrain-test-text-classification-3175589570
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Sushovan/autotrain-test-text-classification-3175589570", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Sushovan/autotrain-test-text-classification-3175589570", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
amrisaurus/pretrained-m-bert-200 | amrisaurus | 2023-01-31T08:05:40Z | 1 | 0 | transformers | [
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
]
| null | 2023-01-31T08:05:08Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: pretrained-m-bert-200
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pretrained-m-bert-200
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.6892
- Validation Loss: 15.9999
- Epoch: 199
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2629 | 10.9400 | 0 |
| 7.8719 | 10.8986 | 1 |
| 6.8337 | 11.4901 | 2 |
| 6.4663 | 11.6037 | 3 |
| 6.4171 | 11.5051 | 4 |
| 6.3166 | 12.1207 | 5 |
| 6.4304 | 11.7927 | 6 |
| 6.0435 | 12.1347 | 7 |
| 5.9134 | 12.1229 | 8 |
| 6.0124 | 12.0225 | 9 |
| 5.9096 | 12.4855 | 10 |
| 5.8829 | 12.7256 | 11 |
| 5.8533 | 12.3504 | 12 |
| 5.8075 | 12.7843 | 13 |
| 6.0418 | 12.6493 | 14 |
| 5.8611 | 12.4900 | 15 |
| 5.8863 | 12.7790 | 16 |
| 5.9484 | 13.0246 | 17 |
| 5.8226 | 12.9865 | 18 |
| 5.8262 | 13.1064 | 19 |
| 5.8687 | 13.1811 | 20 |
| 5.7531 | 13.2824 | 21 |
| 5.8473 | 13.2894 | 22 |
| 5.8762 | 13.1719 | 23 |
| 5.7386 | 13.0748 | 24 |
| 5.6647 | 13.3089 | 25 |
| 5.8553 | 13.5698 | 26 |
| 5.7698 | 14.1035 | 27 |
| 5.7972 | 13.6096 | 28 |
| 5.9381 | 13.1142 | 29 |
| 5.8173 | 13.1007 | 30 |
| 5.7676 | 13.6502 | 31 |
| 5.9740 | 13.5317 | 32 |
| 5.6842 | 13.7206 | 33 |
| 5.7764 | 13.5819 | 34 |
| 5.7659 | 13.4004 | 35 |
| 5.7104 | 13.6715 | 36 |
| 5.8345 | 13.5589 | 37 |
| 5.8067 | 13.6957 | 38 |
| 5.8537 | 13.6661 | 39 |
| 5.6418 | 13.8966 | 40 |
| 5.7818 | 13.7630 | 41 |
| 5.7406 | 14.1682 | 42 |
| 5.7053 | 13.8797 | 43 |
| 5.7151 | 14.1307 | 44 |
| 5.6621 | 14.1855 | 45 |
| 5.6716 | 14.1013 | 46 |
| 5.6596 | 14.2236 | 47 |
| 5.6680 | 14.0390 | 48 |
| 5.8122 | 14.0500 | 49 |
| 5.8497 | 14.0991 | 50 |
| 5.6758 | 14.5258 | 51 |
| 5.7158 | 14.2373 | 52 |
| 5.7288 | 13.9851 | 53 |
| 5.9239 | 14.2297 | 54 |
| 5.6722 | 13.6866 | 55 |
| 5.8708 | 14.2755 | 56 |
| 5.7190 | 14.4764 | 57 |
| 5.7218 | 14.1861 | 58 |
| 5.7478 | 14.3363 | 59 |
| 5.7843 | 13.9645 | 60 |
| 5.6555 | 14.1351 | 61 |
| 5.6951 | 14.5155 | 62 |
| 5.6711 | 14.4671 | 63 |
| 5.7068 | 14.4064 | 64 |
| 5.7773 | 14.5143 | 65 |
| 5.7188 | 14.6878 | 66 |
| 5.7912 | 14.3496 | 67 |
| 5.9308 | 14.4187 | 68 |
| 5.8765 | 14.6648 | 69 |
| 5.7103 | 14.3686 | 70 |
| 5.6585 | 14.3171 | 71 |
| 5.8697 | 14.2778 | 72 |
| 5.6874 | 14.1511 | 73 |
| 5.7367 | 15.0222 | 74 |
| 5.8603 | 14.2226 | 75 |
| 5.8183 | 14.6257 | 76 |
| 5.7646 | 14.5472 | 77 |
| 5.7813 | 14.4560 | 78 |
| 5.6991 | 14.1486 | 79 |
| 5.7365 | 14.5998 | 80 |
| 5.7602 | 14.3595 | 81 |
| 5.7646 | 14.4916 | 82 |
| 5.6289 | 15.1076 | 83 |
| 5.8171 | 14.7216 | 84 |
| 5.7939 | 14.9316 | 85 |
| 5.8249 | 14.6632 | 86 |
| 5.6479 | 15.2074 | 87 |
| 5.7985 | 14.9238 | 88 |
| 5.7332 | 14.4504 | 89 |
| 5.7495 | 14.2924 | 90 |
| 5.7579 | 15.3362 | 91 |
| 5.7217 | 15.0819 | 92 |
| 5.6750 | 14.9618 | 93 |
| 5.8607 | 14.6850 | 94 |
| 5.6310 | 14.9199 | 95 |
| 5.7532 | 14.8353 | 96 |
| 5.6318 | 14.9707 | 97 |
| 5.6861 | 14.8903 | 98 |
| 5.7634 | 15.3237 | 99 |
| 5.7703 | 15.0675 | 100 |
| 5.7290 | 15.5422 | 101 |
| 5.8383 | 14.9575 | 102 |
| 5.7694 | 14.2810 | 103 |
| 5.6092 | 15.5547 | 104 |
| 5.7699 | 15.2309 | 105 |
| 5.8225 | 15.0764 | 106 |
| 5.8007 | 14.8694 | 107 |
| 5.7435 | 15.2683 | 108 |
| 5.7358 | 15.3533 | 109 |
| 5.8024 | 14.8301 | 110 |
| 5.8027 | 15.3505 | 111 |
| 5.8282 | 15.1353 | 112 |
| 5.6818 | 15.3525 | 113 |
| 5.8653 | 14.7720 | 114 |
| 5.7234 | 15.2079 | 115 |
| 5.8179 | 14.9355 | 116 |
| 5.6718 | 15.2269 | 117 |
| 5.8428 | 15.1447 | 118 |
| 5.6875 | 15.2709 | 119 |
| 5.7212 | 15.1541 | 120 |
| 5.8223 | 15.2145 | 121 |
| 5.7125 | 15.2783 | 122 |
| 5.7707 | 15.6087 | 123 |
| 5.7251 | 15.1095 | 124 |
| 5.6308 | 15.2443 | 125 |
| 5.7163 | 15.7562 | 126 |
| 5.7097 | 15.5930 | 127 |
| 5.6560 | 15.1742 | 128 |
| 5.9121 | 15.0983 | 129 |
| 5.5284 | 15.4298 | 130 |
| 5.7584 | 15.5905 | 131 |
| 5.8737 | 15.3326 | 132 |
| 5.7731 | 15.6967 | 133 |
| 5.6686 | 15.2850 | 134 |
| 5.7585 | 15.2779 | 135 |
| 5.7239 | 15.6021 | 136 |
| 5.7295 | 15.3237 | 137 |
| 5.7358 | 15.3199 | 138 |
| 5.8334 | 14.8834 | 139 |
| 5.6537 | 15.6226 | 140 |
| 5.6501 | 15.2466 | 141 |
| 5.7591 | 14.9815 | 142 |
| 5.7694 | 15.3828 | 143 |
| 5.7239 | 15.4082 | 144 |
| 5.8641 | 14.8029 | 145 |
| 5.7668 | 15.4207 | 146 |
| 5.7180 | 15.8702 | 147 |
| 5.6461 | 15.7631 | 148 |
| 5.8629 | 15.2891 | 149 |
| 5.7973 | 15.9778 | 150 |
| 5.8458 | 15.4747 | 151 |
| 5.7720 | 15.9476 | 152 |
| 5.6491 | 15.2055 | 153 |
| 5.7801 | 15.3822 | 154 |
| 5.8175 | 15.7697 | 155 |
| 5.7536 | 15.2464 | 156 |
| 5.7925 | 15.4849 | 157 |
| 5.6012 | 15.5773 | 158 |
| 5.7623 | 15.7559 | 159 |
| 5.7078 | 15.7061 | 160 |
| 5.7834 | 15.5417 | 161 |
| 5.7058 | 15.3236 | 162 |
| 5.8079 | 15.1048 | 163 |
| 5.7757 | 15.2895 | 164 |
| 5.6822 | 15.9946 | 165 |
| 5.6205 | 15.8053 | 166 |
| 5.8778 | 15.9524 | 167 |
| 5.7211 | 15.5006 | 168 |
| 5.7499 | 15.7000 | 169 |
| 5.6561 | 16.1970 | 170 |
| 5.7077 | 15.7324 | 171 |
| 5.7177 | 15.8832 | 172 |
| 5.8901 | 15.2579 | 173 |
| 5.6842 | 16.1185 | 174 |
| 5.7424 | 15.8840 | 175 |
| 5.6889 | 15.5184 | 176 |
| 5.7339 | 15.9269 | 177 |
| 5.6635 | 15.8283 | 178 |
| 5.7331 | 16.0767 | 179 |
| 5.7096 | 15.7523 | 180 |
| 5.6715 | 16.0680 | 181 |
| 5.7703 | 15.6030 | 182 |
| 5.6772 | 15.6442 | 183 |
| 5.7933 | 15.6118 | 184 |
| 5.6788 | 15.5001 | 185 |
| 5.6985 | 15.4559 | 186 |
| 5.8450 | 15.5850 | 187 |
| 5.7437 | 15.9233 | 188 |
| 5.7502 | 15.8410 | 189 |
| 5.7081 | 16.0491 | 190 |
| 5.8119 | 15.3163 | 191 |
| 5.7426 | 15.7990 | 192 |
| 5.6422 | 15.9709 | 193 |
| 5.7431 | 15.3411 | 194 |
| 5.7894 | 15.5860 | 195 |
| 5.5432 | 16.2503 | 196 |
| 5.7073 | 16.0347 | 197 |
| 5.6637 | 16.2954 | 198 |
| 5.6892 | 15.9999 | 199 |
### Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
mxbonn/reinforce-pixelcopter | mxbonn | 2023-01-31T07:55:16Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T23:29:14Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 116.10 +/- 69.09
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
amrisaurus/pretrained-m-bert-100 | amrisaurus | 2023-01-31T07:42:18Z | 1 | 0 | transformers | [
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
]
| null | 2023-01-31T07:41:46Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: pretrained-m-bert-100
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pretrained-m-bert-100
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.7643
- Validation Loss: 15.3282
- Epoch: 99
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2677 | 10.9468 | 0 |
| 7.8146 | 10.9178 | 1 |
| 6.8830 | 11.4644 | 2 |
| 6.4854 | 11.6027 | 3 |
| 6.3696 | 11.5359 | 4 |
| 6.3373 | 12.1261 | 5 |
| 6.4347 | 11.7670 | 6 |
| 6.0652 | 12.2223 | 7 |
| 5.9448 | 12.1202 | 8 |
| 6.0746 | 12.0816 | 9 |
| 6.0138 | 12.4949 | 10 |
| 5.9344 | 12.8130 | 11 |
| 5.9458 | 12.4795 | 12 |
| 5.9723 | 12.8273 | 13 |
| 6.0556 | 12.3681 | 14 |
| 5.8662 | 12.5367 | 15 |
| 5.8969 | 12.8070 | 16 |
| 5.9584 | 13.0502 | 17 |
| 5.8317 | 12.9219 | 18 |
| 5.8259 | 13.0385 | 19 |
| 5.8747 | 13.0952 | 20 |
| 5.7600 | 13.2153 | 21 |
| 5.8675 | 13.2446 | 22 |
| 5.8878 | 13.1709 | 23 |
| 5.7433 | 13.0553 | 24 |
| 5.6823 | 13.2854 | 25 |
| 5.8674 | 13.5718 | 26 |
| 5.7787 | 14.0820 | 27 |
| 5.8037 | 13.5664 | 28 |
| 5.9530 | 13.0143 | 29 |
| 5.8236 | 13.0637 | 30 |
| 5.7696 | 13.5515 | 31 |
| 5.9817 | 13.4774 | 32 |
| 5.6877 | 13.6842 | 33 |
| 5.7816 | 13.5667 | 34 |
| 5.7775 | 13.3846 | 35 |
| 5.7104 | 13.6230 | 36 |
| 5.8429 | 13.5487 | 37 |
| 5.8082 | 13.6563 | 38 |
| 5.8588 | 13.6359 | 39 |
| 5.6482 | 13.8751 | 40 |
| 5.7874 | 13.6936 | 41 |
| 5.7451 | 14.1454 | 42 |
| 5.7165 | 13.8532 | 43 |
| 5.7180 | 14.0519 | 44 |
| 5.6640 | 14.0859 | 45 |
| 5.6735 | 14.0086 | 46 |
| 5.6666 | 14.1733 | 47 |
| 5.6681 | 13.9786 | 48 |
| 5.8221 | 14.0396 | 49 |
| 5.8544 | 14.0354 | 50 |
| 5.6817 | 14.4682 | 51 |
| 5.7215 | 14.2324 | 52 |
| 5.7315 | 13.9238 | 53 |
| 5.9291 | 14.2091 | 54 |
| 5.6790 | 13.6927 | 55 |
| 5.8746 | 14.1590 | 56 |
| 5.7267 | 14.4351 | 57 |
| 5.7268 | 14.0592 | 58 |
| 5.7535 | 14.2763 | 59 |
| 5.7884 | 13.8493 | 60 |
| 5.6596 | 14.0534 | 61 |
| 5.7041 | 14.4408 | 62 |
| 5.6752 | 14.4218 | 63 |
| 5.7102 | 14.3895 | 64 |
| 5.7761 | 14.3942 | 65 |
| 5.7248 | 14.5926 | 66 |
| 5.7945 | 14.2754 | 67 |
| 5.9298 | 14.3393 | 68 |
| 5.8765 | 14.5247 | 69 |
| 5.7173 | 14.3060 | 70 |
| 5.6568 | 14.1837 | 71 |
| 5.8706 | 14.0935 | 72 |
| 5.6913 | 14.0180 | 73 |
| 5.7403 | 14.9313 | 74 |
| 5.8633 | 14.1447 | 75 |
| 5.8216 | 14.5450 | 76 |
| 5.7655 | 14.4690 | 77 |
| 5.7860 | 14.4312 | 78 |
| 5.6992 | 14.1038 | 79 |
| 5.7390 | 14.5180 | 80 |
| 5.7588 | 14.2374 | 81 |
| 5.7709 | 14.4895 | 82 |
| 5.6294 | 14.9558 | 83 |
| 5.8151 | 14.5835 | 84 |
| 5.7965 | 14.8980 | 85 |
| 5.8296 | 14.5919 | 86 |
| 5.6494 | 15.2158 | 87 |
| 5.8014 | 14.9455 | 88 |
| 5.7313 | 14.4270 | 89 |
| 5.7492 | 14.2205 | 90 |
| 5.7618 | 15.2789 | 91 |
| 5.7249 | 15.0650 | 92 |
| 5.6761 | 14.8731 | 93 |
| 5.8601 | 14.6370 | 94 |
| 5.6296 | 14.8570 | 95 |
| 5.7572 | 14.7718 | 96 |
| 5.6341 | 14.9328 | 97 |
| 5.6881 | 14.9298 | 98 |
| 5.7643 | 15.3282 | 99 |
### Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
dhmeltzer/Reinforce-pixelcopter_v0 | dhmeltzer | 2023-01-31T07:22:43Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T07:22:40Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter_v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 37.40 +/- 26.73
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mechanicalsea/speecht5-sid | mechanicalsea | 2023-01-31T07:22:02Z | 0 | 5 | null | [
"tensorboard",
"speech",
"text",
"cross-modal",
"unified model",
"self-supervised learning",
"SpeechT5",
"Speaker Identification",
"Speaker Recognition",
"audio-classification",
"en",
"dataset:s3prl/mini_voxceleb1",
"license:mit",
"region:us"
]
| audio-classification | 2023-01-30T08:17:38Z | ---
license: mit
datasets:
- s3prl/mini_voxceleb1
language:
- en
metrics:
- accuracy
pipeline_tag: audio-classification
tags:
- speech
- text
- cross-modal
- unified model
- self-supervised learning
- SpeechT5
- Speaker Identification
- Speaker Recognition
---
## SpeechT5 SID
| [**Github**](https://github.com/microsoft/SpeechT5) | [**Huggingface**](https://huggingface.co/mechanicalsea/speecht5-sid) |
This manifest is an attempt to recreate the Speaker Identification recipe used for training [SpeechT5](https://aclanthology.org/2022.acl-long.393). This manifest was constructed using [VoxCeleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) containing over 100,000 utterances for 1,251 celebrities. The identification split are given as follows.
| | train | valid | test |
| ------------------- | ------: | ----: | ----: |
| **# of speakers** | 1,251 | 1,251 | 1,251 |
| **# of utterances** | 138,361 | 6,904 | 8,251 |
### Requirements
- [Fairseq](https://github.com/facebookresearch/fairseq)
### Tools
- `manifest/utils` is used to produce manifest as well as conduct training, validation, and evaluation.
- `mainfest/iden_split.txt` and `mainfest/vox1_meta.csv` are officially released files.
### Model and Results
- [`speecht5_sid.pt`](./speecht5_sid.pt) are reimplemented Speaker Identification fine-tuning on the released manifest **but with a smaller batch size** (Ensure the manifest is ok).
- `results` are reproduced by the released fine-tuned model and the accuracy is $96.194\%$.
- `log` is the tensorboard log of fine-tuning the released model.
### Reference
If you find our work is useful in your research, please cite the following paper:
```bibtex
@inproceedings{ao-etal-2022-speecht5,
title = {{S}peech{T}5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing},
author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {May},
year = {2022},
pages={5723--5738},
}
``` |
MrDivakaruni/ppo-SnowballTarget | MrDivakaruni | 2023-01-31T06:37:58Z | 13 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-01-31T06:37:53Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: MrDivakaruni/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
pittawat/ppo-LunarLander-v2-LunarLander-v2 | pittawat | 2023-01-31T06:31:37Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T06:31:16Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-MlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.95 +/- 17.14
name: mean_reward
verified: false
---
# **PPO-MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-MlpPolicy** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
MrDivakaruni/ppo-PyramidsTraining | MrDivakaruni | 2023-01-31T06:29:09Z | 6 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-01-31T06:28:18Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: MrDivakaruni/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cosmin/pokemon-lora | cosmin | 2023-01-31T06:04:44Z | 3 | 1 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-01-30T14:50:21Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/cosmin/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
devonho/detr-resnet-50_finetuned_cppe5 | devonho | 2023-01-31T06:02:36Z | 56 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cppe-5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| object-detection | 2023-01-29T06:54:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
results: []
---
<!-- 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. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
jwright94/ppo-PyramidsTraining | jwright94 | 2023-01-31T04:20:13Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-01-31T04:20:08Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: jwright94/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
wybxc/yuanhuo-v1-dreambooth | wybxc | 2023-01-31T04:13:31Z | 2 | 1 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"en",
"doi:10.57967/hf/0391",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-01-31T04:13:53Z | ---
license: cc-by-nc-sa-4.0
language:
- en
library_name: diffusers
tags:
- stable-diffusion
- text-to-image
widget:
- text: >-
(yanhuo with white hair and blue eyes and ahoge), (yanyuan with black hair and red eyes),
2girls, masterpiece, best quality, sisters
example_title: Yuan Huo
---
# YuanHuo-v1-dreambooth
## 下载
- [ckpt](./yanyuan_v1_dreambooth_clip2_5k_fp16.ckpt)
- [safetensors](./yanyuan_v1_dreambooth_clip2_5k_fp16.safetensors)
## 预览图
```text
(yanhuo with white hair and blue eyes and ahoge), (yanyuan with black hair and red eyes), 2girls, masterpiece, best quality, sisters, snowy street, dynamic angle, flat chest, [smile]
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10.5, Seed: 3993531120, Size: 512x384, Model hash: 15449d01d2, Denoising strength: 0.6, Clip skip: 2, ENSD: 31339, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent
```
```text
(yanhuo with white hair and blue eyes and ahoge), (yanyuan with black hair), 2girls, masterpiece, best quality, sisters, beach, sunset, dynamic angle, full body, flat chest, [smile], [red eyes], [blue eyes]
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10.5, Seed: 2708735031, Size: 512x384, Model hash: 15449d01d2, Denoising strength: 0.6, Clip skip: 2, ENSD: 31339, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent
```
## 推荐起手式
```text
(yanhuo with white hair and blue eyes and ahoge), (yanyuan with black hair and red eyes), 2girls, masterpiece, best quality, sisters
```
## 更多信息
见总仓库：[of_diffusion](https://huggingface.co/wybxc/of_diffusion)。 |
kyeongpil/Reinforce-Pixelcopter-PLE-v0 | kyeongpil | 2023-01-31T04:05:41Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T04:05:36Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 70.60 +/- 49.97
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
paigereeves/distilbert-base-uncased-finetuned-imdb | paigereeves | 2023-01-31T04:05:09Z | 4 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-01-30T10:19:38Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: paigereeves/distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# paigereeves/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.3278
- Validation Loss: 3.8448
- Epoch: 0
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.3278 | 3.8448 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ssube/stable-diffusion-x4-upscaler-onnx | ssube | 2023-01-31T03:45:15Z | 0 | 11 | null | [
"onnx",
"stable-diffusion",
"text-to-image",
"arxiv:2112.10752",
"license:openrail++",
"region:us"
]
| text-to-image | 2023-01-31T03:39:46Z | ---
license: openrail++
tags:
- stable-diffusion
- text-to-image
inference: false
---
# Stable Diffusion x4 ONNX
This model card focuses on the model associated with the Stable Diffusion Upscaler, available [here](https://github.com/Stability-AI/stablediffusion).
This model is trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
Converted to ONNX format for https://github.com/huggingface/diffusers/pull/2158
|
toshiouchiyama/whisper-small-ja | toshiouchiyama | 2023-01-31T03:44:49Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ja",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-01-02T19:39:54Z | ---
language:
- ja
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-ja
results: []
---
<!-- 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. -->
# whisper-small-ja
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3967
- Wer: 18.3755
## 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: 1e-05
- train_batch_size: 8
- 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.3 | 10 | 1.1627 | 26.0985 |
| No log | 0.61 | 20 | 0.7416 | 900.3995 |
| 1.2431 | 0.91 | 30 | 0.6344 | 60.3196 |
| 1.2431 | 1.21 | 40 | 0.5944 | 20.2397 |
| 0.5462 | 1.52 | 50 | 0.5341 | 19.3076 |
| 0.5462 | 1.82 | 60 | 0.4953 | 18.5087 |
| 0.5462 | 2.12 | 70 | 0.4715 | 19.9734 |
| 0.3259 | 2.42 | 80 | 0.4469 | 18.2423 |
| 0.3259 | 2.73 | 90 | 0.4246 | 19.7071 |
| 0.1986 | 3.03 | 100 | 0.4076 | 19.0413 |
| 0.1986 | 3.33 | 110 | 0.3949 | 17.7097 |
| 0.1986 | 3.64 | 120 | 0.4008 | 20.5060 |
| 0.1101 | 3.94 | 130 | 0.3892 | 18.3755 |
| 0.1101 | 4.24 | 140 | 0.3873 | 18.3755 |
| 0.0695 | 4.55 | 150 | 0.3930 | 19.7071 |
| 0.0695 | 4.85 | 160 | 0.3857 | 18.1092 |
| 0.0695 | 5.15 | 170 | 0.3861 | 19.0413 |
| 0.0467 | 5.45 | 180 | 0.3913 | 18.5087 |
| 0.0467 | 5.76 | 190 | 0.3963 | 18.7750 |
| 0.0346 | 6.06 | 200 | 0.3967 | 18.3755 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cpu
- Datasets 2.8.0
- Tokenizers 0.13.2
|
qbaro/wav2vec2-vi5 | qbaro | 2023-01-31T03:34:52Z | 17 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-01-03T03:33:32Z | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-vi5
results: []
---
<!-- 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. -->
# wav2vec2-vi5
This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-large-vi-vlsp2020](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi-vlsp2020) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1632
- Wer: 0.0714
## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1279 | 0.13 | 100 | 0.1495 | 0.0691 |
| 0.1067 | 0.27 | 200 | 0.1407 | 0.0656 |
| 0.1013 | 0.4 | 300 | 0.1352 | 0.0654 |
| 0.1282 | 0.53 | 400 | 0.1468 | 0.0705 |
| 0.1117 | 0.67 | 500 | 0.1658 | 0.0804 |
| 0.1173 | 0.8 | 600 | 0.1485 | 0.0753 |
| 0.1 | 0.94 | 700 | 0.1418 | 0.0699 |
| 0.1029 | 1.07 | 800 | 0.1523 | 0.0715 |
| 0.0823 | 1.2 | 900 | 0.1597 | 0.0731 |
| 0.0893 | 1.34 | 1000 | 0.1441 | 0.0688 |
| 0.0822 | 1.47 | 1100 | 0.1580 | 0.0711 |
| 0.1077 | 1.6 | 1200 | 0.1664 | 0.0832 |
| 0.0988 | 1.74 | 1300 | 0.1626 | 0.0744 |
| 0.0777 | 1.87 | 1400 | 0.1498 | 0.0705 |
| 0.0849 | 2.01 | 1500 | 0.1581 | 0.0711 |
| 0.0647 | 2.14 | 1600 | 0.1636 | 0.0729 |
| 0.0638 | 2.27 | 1700 | 0.1590 | 0.0750 |
| 0.0674 | 2.41 | 1800 | 0.1627 | 0.0755 |
| 0.0644 | 2.54 | 1900 | 0.1576 | 0.0746 |
| 0.0666 | 2.67 | 2000 | 0.1569 | 0.0704 |
| 0.0647 | 2.81 | 2100 | 0.1601 | 0.0693 |
| 0.0769 | 2.94 | 2200 | 0.1595 | 0.0680 |
| 0.0585 | 3.07 | 2300 | 0.1591 | 0.0718 |
| 0.0515 | 3.21 | 2400 | 0.1628 | 0.0714 |
| 0.068 | 3.34 | 2500 | 0.1549 | 0.0704 |
| 0.0473 | 3.48 | 2600 | 0.1618 | 0.0694 |
| 0.0545 | 3.61 | 2700 | 0.1539 | 0.0685 |
| 0.049 | 3.74 | 2800 | 0.1546 | 0.0698 |
| 0.0538 | 3.88 | 2900 | 0.1507 | 0.0698 |
| 0.0452 | 4.01 | 3000 | 0.1516 | 0.0702 |
| 0.0398 | 4.14 | 3100 | 0.1604 | 0.0723 |
| 0.0539 | 4.28 | 3200 | 0.1587 | 0.0713 |
| 0.0443 | 4.41 | 3300 | 0.1607 | 0.0718 |
| 0.0405 | 4.55 | 3400 | 0.1626 | 0.0709 |
| 0.038 | 4.68 | 3500 | 0.1623 | 0.0707 |
| 0.0388 | 4.81 | 3600 | 0.1621 | 0.0726 |
| 0.0418 | 4.95 | 3700 | 0.1632 | 0.0714 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
jwright94/ppo-SnowballTarget | jwright94 | 2023-01-31T03:32:55Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-01-31T03:32:49Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: jwright94/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dragonSwing/xlm-roberta-capu | dragonSwing | 2023-01-31T03:21:11Z | 2,214 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"capitalization",
"punctuation",
"token-classification",
"vi",
"dataset:oscar-corpus/OSCAR-2109",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-05-11T06:46:21Z | ---
language:
- vi
tags:
- capitalization
- punctuation
- token-classification
license: cc-by-sa-4.0
datasets:
- oscar-corpus/OSCAR-2109
metrics:
- accuracy
- precision
- recall
- f1
---
# ✨ xlm-roberta-capitalization-punctuation
This a [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model finetuned for Vietnamese punctuation restoration on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset.
The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation.
This model is intended for direct use as a punctuation restoration model for the general Vietnamese language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks.
Model restores the following punctuations -- **[. , : ? ]**
The model also restores the complex upper-casing of words like *YouTube*, *MobiFone*.
-----------------------------------------------
## 🚋 Usage
**Below is a quick way to get up and running with the model.**
1. Download files from hub
```python
import os
import shutil
import sys
from huggingface_hub import snapshot_download
cache_dir = "./capu"
def download_files(repo_id, cache_dir=None, ignore_regex=None):
download_dir = snapshot_download(repo_id=repo_id, cache_dir=cache_dir, ignore_regex=ignore_regex)
if cache_dir is None or download_dir == cache_dir:
return download_dir
file_names = os.listdir(download_dir)
for file_name in file_names:
shutil.move(os.path.join(download_dir, file_name), cache_dir)
os.rmdir(download_dir)
return cache_dir
cache_dir = download_files(repo_id="dragonSwing/xlm-roberta-capu", cache_dir=cache_dir, ignore_regex=["*.json", "*.bin"])
sys.path.append(cache_dir)
```
2. Sample python code
```python
import os
from gec_model import GecBERTModel
model = GecBERTModel(
vocab_path=os.path.join(cache_dir, "vocabulary"),
model_paths="dragonSwing/xlm-roberta-capu",
split_chunk=True
)
model("theo đó thủ tướng dự kiến tiếp bộ trưởng nông nghiệp mỹ tom wilsack bộ trưởng thương mại mỹ gina raimondo bộ trưởng tài chính janet yellen gặp gỡ thượng nghị sĩ patrick leahy và một số nghị sĩ mỹ khác")
# Always return list of outputs.
# ['Theo đó, Thủ tướng dự kiến tiếp Bộ trưởng Nông nghiệp Mỹ Tom Wilsack, Bộ trưởng Thương mại Mỹ Gina Raimondo, Bộ trưởng Tài chính Janet Yellen, gặp gỡ Thượng nghị sĩ Patrick Leahy và một số nghị sĩ Mỹ khác.']
model("những gói cước năm g mobifone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời so với mạng bốn g thì tốc độ truy cập mạng 5 g mobifone được nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần")
# ['Những gói cước 5G MobiFone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời. So với mạng 4G thì tốc độ truy cập mạng 5G MobiFone được Nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần.']
```
**This model can work on arbitrarily large text in Vietnamese language.**
-----------------------------------------------
## 📡 Training data
Here is the number of product reviews we used for fine-tuning the model:
| Language | Number of text samples |
| --- | --- |
| Vietnamese | 5,600,000 |
-----------------------------------------------
## 🎯 Accuracy
Below is a breakdown of the performance of the model by each label on 10,000 held-out text samples:
| label | precision | recall | f1-score | support |
| --- | --- | --- | --- | --- |
| **Upper** | 0.89 | 0.90 | 0.89 | 56497 |
| **Complex-Upper** | 0.93 | 0.83 | 0.88 | 480 |
| **.** | 0.81 | 0.84 | 0.82 | 18139 |
| **,** | 0.69 | 0.75 | 0.72 | 22961 |
| **:** | 0.76 | 0.60 | 0.67 | 1432 |
| **?** | 0.82 | 0.75 | 0.78 | 1730 |
| **none** | 0.99 | 0.99 | 0.99 |475611 |
-----------------------------------------------
|
mojoee/Reinforce-Cartpole-v1 | mojoee | 2023-01-31T03:19:25Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T03:19:13Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
jhn9803/distilbert-base-uncased-finetuned-clinc | jhn9803 | 2023-01-31T03:16:19Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-01-31T02:52:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9183870967741935
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2890 | 0.7432 |
| 2.6284 | 2.0 | 636 | 1.8756 | 0.8377 |
| 1.5483 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.015 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.7953 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
huggingtweets/90snormmcdonald | huggingtweets | 2023-01-31T03:03:17Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-01-31T03:01:50Z | ---
language: en
thumbnail: http://www.huggingtweets.com/90snormmcdonald/1675134192089/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1339391092/macdonald_400x400.gif')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">macdonald</div>
<div style="text-align: center; font-size: 14px;">@90snormmcdonald</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from macdonald.
| Data | macdonald |
| --- | --- |
| Tweets downloaded | 105 |
| Retweets | 0 |
| Short tweets | 4 |
| Tweets kept | 101 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rjng7zxe/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @90snormmcdonald's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rp8ijnsb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rp8ijnsb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/90snormmcdonald')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
dhmeltzer/spaceinvaders_noskip_v4_dqn | dhmeltzer | 2023-01-31T02:27:33Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-31T02:26:56Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 525.50 +/- 185.07
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dhmeltzer -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dhmeltzer -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dhmeltzer
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
sohm/ppo-PyramidsRND | sohm | 2023-01-31T02:20:24Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-01-31T02:19:26Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: sohm/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
MatAIart/kurzgesagt-style-v2-768 | MatAIart | 2023-01-31T02:02:19Z | 12 | 9 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-02T15:51:46Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### Kurzgesagt-style-v2-768 Dreambooth model trained on the v2-768 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
Kurzgesagt style (use that on your prompt)
|
Forkits/ppo-Huggy | Forkits | 2023-01-31T01:26:31Z | 10 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-01-31T01:26:24Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Forkits/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
tadisettiraju/raju_diffusion | tadisettiraju | 2023-01-31T00:42:15Z | 0 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-01-31T00:40:28Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generatioon.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('tadisettiraju/raju_diffusion')
image = pipeline().images[0]
image
```
|
aammari/setfit-zero-shot-classification-pbsp-p4-time | aammari | 2023-01-31T00:35:26Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-01-31T00:34:58Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 540 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 540,
"warmup_steps": 54,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
syjung/whisper-small-tuning3 | syjung | 2023-01-31T00:29:53Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"en",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-01-30T15:34:52Z | ---
language:
- en
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results: []
---
<!-- 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. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 6.8280
- eval_wer: 99.4048
- eval_runtime: 34.4895
- eval_samples_per_second: 1.131
- eval_steps_per_second: 1.131
- epoch: 0.0
- step: 20
## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 40
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
talitazahran/injunainjuna | talitazahran | 2023-01-30T23:48:44Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-01-30T23:48:44Z | ---
license: creativeml-openrail-m
---
|
PeterDerLustige/QTableTaxi | PeterDerLustige | 2023-01-30T23:41:51Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T23:41:49Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: QTableTaxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="PeterDerLustige/QTableTaxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AliBuildsAI/sd-class-butterflies-64 | AliBuildsAI | 2023-01-30T23:37:23Z | 3 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-01-30T23:36:33Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('AliBuildsAI/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
PeterDerLustige/q-FrozenLake-v1-4x4-noSlippery | PeterDerLustige | 2023-01-30T23:34:23Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T23:34:20Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="PeterDerLustige/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
andreids/en_textcat_sales | andreids | 2023-01-30T23:31:54Z | 5 | 0 | spacy | [
"spacy",
"text-classification",
"en",
"region:us"
]
| text-classification | 2023-01-30T23:31:39Z | ---
tags:
- spacy
- text-classification
language:
- en
model-index:
- name: en_textcat_sales
results: []
---
| Feature | Description |
| --- | --- |
| **Name** | `en_textcat_sales` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.4.3,<3.5.0` |
| **Default Pipeline** | `textcat` |
| **Components** | `textcat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (2 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`textcat`** | `OTHER`, `2100 - Sales` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `CATS_SCORE` | 83.00 |
| `CATS_MICRO_P` | 95.13 |
| `CATS_MICRO_R` | 95.13 |
| `CATS_MICRO_F` | 95.13 |
| `CATS_MACRO_P` | 94.91 |
| `CATS_MACRO_R` | 76.76 |
| `CATS_MACRO_F` | 83.00 |
| `CATS_MACRO_AUC` | 91.29 |
| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
| `TEXTCAT_LOSS` | 473.84 | |
Tiemi/FunnyShihTzu-dog | Tiemi | 2023-01-30T23:25:16Z | 4 | 9 | diffusers | [
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"animal",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-29T21:13:42Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a cartoon digital art of FunnyShihTzu dog smiling
---
# DreamBooth model for the FunnyShihTzu concept trained by Tiemi on the Tiemi/FunnyShihTzu dataset.
This is a Stable Diffusion model fine-tuned on photos of my dog with DreamBooth 🐕.
It can be used by modifying the `instance_prompt` and keeping the tag FunnyShihTzu.
**Examples of prompts:**
- a cartoon digital art of FunnyShihTzu dog smiling
- a photo of FunnyShihTzu dog laying in the couch
- a funko pop of FunnyShihTzu dog smiling
Each time you run the prompt you'll see a different image (even with the same text).
If you enjoy this model, please give it a like ❤️.
## Description
This is a Stable Diffusion model fine-tuned on `dog` images for the animal theme.
## Photo of my dog:
<img src="https://s3.amazonaws.com/moonup/production/uploads/1672671005943-6192492551e3de53a3628c6b.jpeg" alt="shih_tzu" width="200"/>
## Examples of generated images:
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Tiemi/FunnyShihTzu-dog')
image = pipeline().images[0]
image
```
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! |
talitazahran/jenjen | talitazahran | 2023-01-30T23:17:51Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-01-30T23:03:36Z | ---
license: creativeml-openrail-m
---
|
bjarlestam/dqn-SpaceInvadersNoFrameskip-v4-2 | bjarlestam | 2023-01-30T23:15:07Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T23:14:36Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 374.00 +/- 214.89
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bjarlestam -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bjarlestam -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bjarlestam
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 70000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.01),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Lakoc/ppo-Huggy | Lakoc | 2023-01-30T23:00:01Z | 4 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-01-30T22:59:53Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Lakoc/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
GBaker/biolinkbert-base-medqa-usmle-nocontext | GBaker | 2023-01-30T22:55:08Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:GBaker/MedQA-USMLE-4-options-hf",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| multiple-choice | 2023-01-28T19:09:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: biolinkbert-base-medqa-usmle-nocontext
results: []
datasets:
- GBaker/MedQA-USMLE-4-options-hf
---
<!-- 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. -->
# biolinkbert-base-medqa-usmle-nocontext
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5149
- Accuracy: 0.3943
## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.98 | 39 | 1.3339 | 0.3590 |
| No log | 1.98 | 78 | 1.3685 | 0.3794 |
| No log | 2.98 | 117 | 1.4162 | 0.3912 |
| No log | 3.98 | 156 | 1.4484 | 0.3888 |
| No log | 4.98 | 195 | 1.4869 | 0.3983 |
| No log | 5.98 | 234 | 1.5149 | 0.3943 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2 |
astein0/ppo-Huggy | astein0 | 2023-01-30T22:53:14Z | 10 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-01-30T22:53:07Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: astein0/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
selvino/dqn-SpaceInvadersNoFrameskip-v4 | selvino | 2023-01-30T22:37:00Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T22:36:24Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 614.00 +/- 99.64
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga selvino -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga selvino -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga selvino
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AliBuildsAI/sd-class-butterflies-32 | AliBuildsAI | 2023-01-30T22:25:04Z | 2 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-01-30T22:24:38Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('AliBuildsAI/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
inseq/wmt21-mlqe-ru-en | inseq | 2023-01-30T22:15:30Z | 57 | 1 | transformers | [
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"translation",
"wmt20",
"en",
"ru",
"multilingual",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-01-30T12:34:11Z | ---
language:
- en
- ru
- multilingual
license: cc-by-sa-4.0
tags:
- translation
- wmt20
widget:
- text: "Сахалинская кайнозойская складчатая область разделяется на Восточную и Западную зоны, разделённые Центрально-Сахалинским грабеном."
- text: "Существует несколько мнений о его точном месторасположении."
- text: "Крупный научно-образовательный центр, в котором обучается свыше ста тысяч студентов."
---
# Fairseq Ru-En NMT WMT20 MLQE
This repository contains the Russian-English model trained with the [fairseq toolkit](https://github.com/pytorch/fairseq) that was used to produce translations used in the WMT21 shared task on quality estimation (QE) on the [MLQE dataset](https://github.com/facebookresearch/mlqe).
The checkpoint was converted from the original fairseq checkpoint available [here](https://github.com/facebookresearch/mlqe/tree/master/nmt_models) using the `convert_fsmt_original_pytorch_checkpoint_to_pytorch.py` script from the 🤗 Transformers library (v4.26.0).
Please refer to the repositories linked above for additional information on usage, parameters and training data. |
matthh/a2c-PandaReachDense-v2 | matthh | 2023-01-30T22:08:48Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T22:06:30Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.86 +/- 0.28
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
odiaz1066/a2c-AntBulletEnv-v0 | odiaz1066 | 2023-01-30T22:06:36Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T22:05:34Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1190.35 +/- 89.58
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
giratikanon/rare-puppers-tg | giratikanon | 2023-01-30T21:44:32Z | 18 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-01-30T21:44:20Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers-tg
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9242424368858337
---
# rare-puppers-tg
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
 |
multimodalart/cat-toy-z | multimodalart | 2023-01-30T21:42:38Z | 3 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-01-30T21:35:02Z | ---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- text-to-image
- diffusers
- lora
widget:
- text: cttoyz
---
### cat-toy-z Dreambooth LoRA model trained by multimodalart with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
cttoyz (use that on your prompt)
|
manexpa/RLcourse-Taxi-v3 | manexpa | 2023-01-30T21:39:40Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T21:39:32Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: RLcourse-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="manexpa/RLcourse-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Lakoc/ppo-LunarLander-v2 | Lakoc | 2023-01-30T21:29:56Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T21:21:28Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 283.67 +/- 15.14
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Rolo/dqn-SpaceInvadersNoFrameskip-v4 | Rolo | 2023-01-30T21:26:27Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T21:21:36Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 618.00 +/- 149.44
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Rolo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Rolo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Rolo
```
## Hyperparameters
```python
OrderedDict([('batch_size', 4096),
('buffer_size', 2000000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 7e-05),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
inseq/wmt20-mlqe-ro-en | inseq | 2023-01-30T21:08:06Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"translation",
"wmt20",
"en",
"ro",
"multilingual",
"dataset:wmt/europarl",
"dataset:setimes",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-01-30T12:24:34Z | ---
language:
- en
- ro
- multilingual
license: cc-by-sa-4.0
tags:
- translation
- wmt20
datasets:
- wmt/europarl
- setimes
widget:
- text: "Limba română este o limbă indo-europeană din grupul italic și din subgrupul oriental al limbilor romanice. Printre limbile romanice, româna este a cincea după numărul de vorbitori, în urma spaniolei, portughezei, francezei și italienei."
- text: "Scalia s-a născut în Trenton, statul New Jersey. A studiat la liceul Xavier High School din Manhattan, și apoi la colegiul de arte liberale al Universității Georgetown din Washington, D.C."
- text: "Totuși, este amenințată de alterări ale habitatului său, iar în Ucraina și partea europeană din arealul său a Rusiei este în declin rapid"
---
# Fairseq Ro-En NMT WMT20 MLQE
This repository contains the Romanian-English model trained with the [fairseq toolkit](https://github.com/pytorch/fairseq) that was used to produce translations used in the WMT20 shared task on quality estimation (QE) on the [MLQE dataset](https://github.com/facebookresearch/mlqe).
The checkpoint was converted from the original fairseq checkpoint available [here](https://github.com/facebookresearch/mlqe/tree/master/nmt_models) using the `convert_fsmt_original_pytorch_checkpoint_to_pytorch.py` script from the 🤗 Transformers library (v4.26.0).
Please refer to the repositories linked above for additional information on usage, parameters and training data. |
inseq/wmt20-mlqe-en-zh | inseq | 2023-01-30T21:07:49Z | 6 | 7 | transformers | [
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"translation",
"wmt20",
"en",
"zh",
"multilingual",
"dataset:wmt/news-commentary",
"dataset:wmt/wikititles",
"dataset:wmt/uncorpus",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-01-30T12:22:22Z | ---
language:
- en
- zh
- multilingual
license: cc-by-sa-4.0
tags:
- translation
- wmt20
datasets:
- wmt/news-commentary
- wmt/wikititles
- wmt/uncorpus
widget:
- text: "It is a plump quail-shaped bird with white eyes and predominantly marbled black, rufous and pale brown plumage, marked prominently with white spots and stripes."
- text: "The 59th Primetime Creative Arts Emmy Awards honored the best in artistic and technical achievement in American prime time television programming from June 1, 2006, until May 31, 2007, as chosen by the Academy of Television Arts & Sciences."
- text: "While forests in temperate areas are readily categorised on the basis of tree canopy density, such schemes do not work well in tropical forests."
---
# Fairseq En-Zh NMT WMT20 MLQE
This repository contains the English-Chinese model trained with the [fairseq toolkit](https://github.com/pytorch/fairseq) that was used to produce translations used in the WMT20 shared task on quality estimation (QE) on the [MLQE dataset](https://github.com/facebookresearch/mlqe).
The checkpoint was converted from the original fairseq checkpoint available [here](https://github.com/facebookresearch/mlqe/tree/master/nmt_models) using the `convert_fsmt_original_pytorch_checkpoint_to_pytorch.py` script from the 🤗 Transformers library (v4.26.0).
Please refer to the repositories linked above for additional information on usage, parameters and training data |
bhalll/q-FrozenLake-v1-4x4-noSlippery | bhalll | 2023-01-30T21:04:03Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T21:04:01Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bhalll/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Celal11/resnet-50-finetuned-FER2013-0.003-CKPlus | Celal11 | 2023-01-30T20:54:32Z | 83 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-01-30T20:52:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned-FER2013-0.003-CKPlus
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9847715736040609
---
<!-- 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. -->
# resnet-50-finetuned-FER2013-0.003-CKPlus
This model is a fine-tuned version of [Celal11/resnet-50-finetuned-FER2013-0.003](https://huggingface.co/Celal11/resnet-50-finetuned-FER2013-0.003) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Accuracy: 0.9848
## 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: 0.003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6689 | 0.97 | 27 | 0.1123 | 0.9797 |
| 0.2929 | 1.97 | 54 | 0.0614 | 0.9848 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
MtCelesteMa/bert-base-uncased-finetuned-multiglue | MtCelesteMa | 2023-01-30T20:38:27Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:MtCelesteMa/multiglue",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-01-30T19:59:04Z | ---
license: apache-2.0
datasets:
- MtCelesteMa/multiglue
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model is bert-base-uncased finetuned on the MultiGLUE dataset.
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** Apache 2.0 (same as BERT)
- **Finetuned from model [optional]:** bert-base-uncased
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import numpy as np
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('bert-base-uncased')
model = transformers.AutoModelForSequenceClassification.from_pretrained('MtCelesteMa/bert-base-uncased-finetuned-multiglue')
task = 'cola'
sentence1 = 'Our friends won\'t buy this analysis, let alone the next one we propose.'
sentence2 = None
inputs = tokenizer(f'{task}:{sentence1}', f'{sentence2}', return_tensors='pt')
outputs = model(**inputs)
label = np.argmax(outputs.logits[0].detach().numpy())
print(label)
```
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** RTX A6000
- **Hours used:** 2
- **Cloud Provider:** [vast.ai](https://vast.ai)
- **Compute Region:** Sweden
- **Carbon Emitted:** 0.26 kg
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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generateai/my_awesome_model | generateai | 2023-01-30T20:28:33Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-01-30T20:03:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
---
<!-- 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. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4769
- Accuracy: 0.5
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 2.4849 | 0.5 |
| No log | 2.0 | 2 | 2.4769 | 0.5 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Metformin/finetuned_cxr_captioning_encdec | Metformin | 2023-01-30T20:25:54Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
]
| null | 2023-01-30T11:07:56Z | ---
library_name: keras
---
## 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:
| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision |
|----|-------------|-----|------|------|-------|-------|------------------|
|Adam|4.999999873689376e-05|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
iammartian0/MLPpolicy415-lunar | iammartian0 | 2023-01-30T20:12:54Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-30T20:01:25Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.84 +/- 39.29
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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