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huggan/pix2pix-edge2shoes | c33cecfe57658244f753611943fdff043fd08e39 | 2022-04-15T04:28:29.000Z | [
"pytorch",
"dataset:huggan/edge2shoes",
"arxiv:1611.07004",
"huggan",
"gan",
"license:mit"
] | null | false | huggan | null | huggan/pix2pix-edge2shoes | 0 | null | null | ---
tags:
- huggan
- gan
datasets:
- huggan/edge2shoes
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
[Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
## Intended uses & limitations:
Used for reconstruction of images from edges
#### How to use
```python
from torchvision.transforms import Compose, Resize, ToTensor, Normalize
from PIL import Image
from torchvision.utils import save_image
import cv2
from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet
transform = Compose(
[
Resize((256, 256), Image.BICUBIC),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
model = GeneratorUNet.from_pretrained('huggan/pix2pix-edge2shoes)
def predict_fn(img):
inp = transform(img).unsqueeze(0)
out = model(inp)
save_image(out, 'out.png', normalize=True)
return 'out.png'
predict_fn(img)
```
#### Limitations and bias
* Gives unrealistic colors in the image
* Patterns in the edge drawing are not recognize properly
## Training data
* [edges2shoes](https://huggingface.co/datasets/huggan/edges2shoes)
## Training procedure
```
# clone the repository
git clone https://github.com/huggingface/community-events.git
pip install .
# change directory
cd community-events/huggan/pytorch/pix2pix/
# define config
accelerate config
# launch training with required parameters
accelerate launch train.py --checkpoint_interval 10 --dataset huggan/edges2shoes --push_to_hub --model_name pix2pix-edge2shoes --batch_size 128 --n_epochs 100
```
## Generated Images
Here,
* First Image Row: Input Sketch
* Second Image Row: Generated Image
* Third Image Row: Target Image
### BibTeX entry and citation info
```bibtex
@article{pix2pix2017,
title={Image-to-Image Translation with Conditional Adversarial Networks},
author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
journal={CVPR},
year={2017}
}
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timhbach/Team_Gryffindor_NER | 01e38cc5491a0a9c77c65ca6db7662aa38cc47d6 | 2022-04-29T21:13:30.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | timhbach | null | timhbach/Team_Gryffindor_NER | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Team_Gryffindor_NER
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. -->
# Team-Gryffindor-distilbert-base-finetuned-NER-creditcardcontract-100epoch
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Credit card agreement dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0470
- Precision: 0.7319
- Recall: 0.7064
- F1: 0.7190
- Accuracy: 0.9920
## 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: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0113 | 0.33 | 500 | 0.0443 | 0.6547 | 0.7028 | 0.6779 | 0.9908 |
| 0.0118 | 0.67 | 1000 | 0.0435 | 0.7207 | 0.6440 | 0.6802 | 0.9916 |
| 0.013 | 1.0 | 1500 | 0.0449 | 0.7113 | 0.6826 | 0.6966 | 0.9918 |
| 0.0113 | 1.34 | 2000 | 0.0434 | 0.7213 | 0.6697 | 0.6946 | 0.9915 |
| 0.0121 | 1.67 | 2500 | 0.0467 | 0.6955 | 0.6789 | 0.6871 | 0.9914 |
| 0.0125 | 2.01 | 3000 | 0.0417 | 0.7095 | 0.6991 | 0.7043 | 0.9920 |
| 0.0106 | 2.34 | 3500 | 0.0437 | 0.7191 | 0.6624 | 0.6896 | 0.9918 |
| 0.0114 | 2.68 | 4000 | 0.0468 | 0.7165 | 0.6679 | 0.6914 | 0.9920 |
| 0.0125 | 3.01 | 4500 | 0.0431 | 0.6888 | 0.6862 | 0.6875 | 0.9917 |
| 0.0107 | 3.35 | 5000 | 0.0446 | 0.7184 | 0.6459 | 0.6802 | 0.9913 |
| 0.0096 | 3.68 | 5500 | 0.0485 | 0.6926 | 0.6532 | 0.6723 | 0.9912 |
| 0.013 | 4.02 | 6000 | 0.0448 | 0.6134 | 0.6697 | 0.6404 | 0.9907 |
| 0.0102 | 4.35 | 6500 | 0.0497 | 0.6895 | 0.6642 | 0.6766 | 0.9913 |
| 0.0112 | 4.69 | 7000 | 0.0464 | 0.6759 | 0.6697 | 0.6728 | 0.9910 |
| 0.0117 | 5.02 | 7500 | 0.0484 | 0.7451 | 0.6275 | 0.6813 | 0.9916 |
| 0.0114 | 5.36 | 8000 | 0.0411 | 0.7086 | 0.6826 | 0.6953 | 0.9919 |
| 0.0108 | 5.69 | 8500 | 0.0443 | 0.7041 | 0.6679 | 0.6855 | 0.9916 |
| 0.0109 | 6.03 | 9000 | 0.0470 | 0.7228 | 0.6697 | 0.6952 | 0.9916 |
| 0.0099 | 6.36 | 9500 | 0.0471 | 0.7253 | 0.6881 | 0.7062 | 0.9913 |
| 0.0103 | 6.7 | 10000 | 0.0430 | 0.6986 | 0.7101 | 0.7043 | 0.9914 |
| 0.0117 | 7.03 | 10500 | 0.0462 | 0.7327 | 0.6991 | 0.7155 | 0.9918 |
| 0.0098 | 7.37 | 11000 | 0.0483 | 0.6910 | 0.6771 | 0.6840 | 0.9914 |
| 0.0107 | 7.7 | 11500 | 0.0468 | 0.7189 | 0.6899 | 0.7041 | 0.9916 |
| 0.0119 | 8.04 | 12000 | 0.0434 | 0.6970 | 0.6881 | 0.6925 | 0.9918 |
| 0.0112 | 8.37 | 12500 | 0.0469 | 0.7007 | 0.6917 | 0.6962 | 0.9918 |
| 0.011 | 8.71 | 13000 | 0.0469 | 0.6736 | 0.6514 | 0.6623 | 0.9914 |
| 0.0101 | 9.04 | 13500 | 0.0451 | 0.6691 | 0.6606 | 0.6648 | 0.9913 |
| 0.0099 | 9.38 | 14000 | 0.0462 | 0.7006 | 0.6826 | 0.6914 | 0.9918 |
| 0.0107 | 9.71 | 14500 | 0.0444 | 0.6840 | 0.6752 | 0.6796 | 0.9915 |
| 0.0118 | 10.05 | 15000 | 0.0457 | 0.7015 | 0.6771 | 0.6891 | 0.9918 |
| 0.0102 | 10.38 | 15500 | 0.0500 | 0.7413 | 0.6679 | 0.7027 | 0.9919 |
| 0.0107 | 10.72 | 16000 | 0.0470 | 0.7319 | 0.7064 | 0.7190 | 0.9920 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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huggan/fastgan-few-shot-shells | 698843e8a0c88fb7384898ca5f09f82b55b26a93 | 2022-05-06T22:32:48.000Z | [
"pytorch",
"dataset:huggan/few-shot-shells",
"arxiv:2101.04775",
"huggan",
"gan",
"unconditional-image-generation",
"license:mit"
] | unconditional-image-generation | false | huggan | null | huggan/fastgan-few-shot-shells | 0 | null | null | ---
tags:
- huggan
- gan
- unconditional-image-generation
datasets:
- huggan/few-shot-shells
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# Generate shell image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets.
This model was trained on a dataset of 64 high-quality images of Shells.
#### How to use
```python
# Clone this model
git clone https://huggingface.co/huggan/fastgan-few-shot-shells/
def load_generator(model_name_or_path):
generator = Generator(in_channels=256, out_channels=3)
generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3)
_ = generator.eval()
return generator
def _denormalize(input: torch.Tensor) -> torch.Tensor:
return (input * 127.5) + 127.5
# Load generator
generator = load_generator("huggan/fastgan-few-shot-shells")
# Generate a random noise image
noise = torch.zeros(1, 256, 1, 1, device=device).normal_(0.0, 1.0)
with torch.no_grad():
gan_images, _ = generator(noise)
gan_images = _denormalize(gan_images.detach())
save_image(gan_images, "sample.png", nrow=1, normalize=True)
```
#### Limitations and bias
* Converge faster and better with small datasets (less than 1000 samples)
## Training data
[fastgan-few-shot-shells](https://huggingface.co/datasets/huggan/few-shot-shells)
## Generated Images
### BibTeX entry and citation info
```bibtex
@article{FastGAN,
title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis},
author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal},
journal={ICLR},
year={2021}
}
``` | [
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yogi/autotrain-amazon_text_sum-730222226 | 6562d546d0ab9d7b5484e755a88b0bd2b7e78f22 | 2022-04-12T09:08:15.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:yogi/autotrain-data-amazon_text_sum",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | yogi | null | yogi/autotrain-amazon_text_sum-730222226 | 0 | null | transformers | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- yogi/autotrain-data-amazon_text_sum
co2_eq_emissions: 2986.6520132805163
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 730222226
- CO2 Emissions (in grams): 2986.6520132805163
## Validation Metrics
- Loss: 2.682709217071533
- Rouge1: 19.6069
- Rouge2: 7.3367
- RougeL: 19.2706
- RougeLsum: 19.286
- Gen Len: 5.5731
## 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/yogi/autotrain-amazon_text_sum-730222226
``` | [
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] |
maesneako/gpt2-en-maptask-finetuned | 90982e36897723928853d8b84b855bb00678b840 | 2022-04-11T16:24:18.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | maesneako | null | maesneako/gpt2-en-maptask-finetuned | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-en-maptask-finetuned
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. -->
# gpt2-en-maptask-finetuned
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None 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: 5e-05
- train_batch_size: 4
- 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: 100
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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] |
maveriq/mybert-base-32k | e32e57cb0b128c3576d97db67d3f8212adfee131 | 2022-04-11T17:35:29.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | maveriq | null | maveriq/mybert-base-32k | 0 | null | transformers | Entry not found | [
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tonyalves/ft-pt-br-local | 19ebdfb27cc4049ab271241aa46c3ad2ef8f5faf | 2022-04-11T20:39:07.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | tonyalves | null | tonyalves/ft-pt-br-local | 0 | null | transformers | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
model-index:
- name: ft-pt-br-local
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. -->
# ft-pt-br-local
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) on the None 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: 0.0003
- 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
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1+cu102
- Datasets 1.18.4
- Tokenizers 0.11.6
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maveriq/mybert-mini-60k | 4e2290e60b2b9d2e456f03213017739ed8af3702 | 2022-04-11T18:18:51.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | maveriq | null | maveriq/mybert-mini-60k | 0 | null | transformers | Entry not found | [
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maveriq/lingbert-mini-60k | 6fda7bd34a8e004d1d28fa6ba6152c88a4ef83c4 | 2022-04-11T18:21:22.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | maveriq | null | maveriq/lingbert-mini-60k | 0 | null | transformers | Entry not found | [
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maveriq/mybert-mini-172k | 452439b80ef78cea8f510d6626218073dd352994 | 2022-04-11T18:58:35.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | maveriq | null | maveriq/mybert-mini-172k | 0 | null | transformers | Entry not found | [
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irenelizihui/MarianMT_UFAL_en_es | 711f4c9569ed0f44ad622075f63cbcd79c799524 | 2022-04-11T23:04:22.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"license:wtfpl",
"autotrain_compatible"
] | text2text-generation | false | irenelizihui | null | irenelizihui/MarianMT_UFAL_en_es | 0 | 1 | transformers | ---
license: wtfpl
---
MarianMT trained on the UFAL dataset: English to Spanish Machine Translation model. | [
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irenelizihui/MarianMT_UFAL_en_ro | 57a6309a36d6f39f6341631c4d6f25e7c83e0e2d | 2022-04-11T23:03:23.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"license:wtfpl",
"autotrain_compatible"
] | text2text-generation | false | irenelizihui | null | irenelizihui/MarianMT_UFAL_en_ro | 0 | 1 | transformers | ---
license: wtfpl
---
UFAL English to Romainian Machine Translation Model based on MarianMT model. | [
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] |
huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings | 7894ac79f51f6fa3db4130c4954307ae0f593384 | 2022-04-11T22:44:40.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/angrymemorys-oldandtoothless-sadboi666_-witheredstrings/1649717075201/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/1506323689456947207/xBvvxyQr_400x400.jpg')">
</div>
<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/1511852580216967169/b1Aiv2t3_400x400.jpg')">
</div>
<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/378800000610482331/8808c2f408b97fe3646f2dca86441506_400x400.jpeg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">makeouthill & VacuumF & Jason Hendricks & Angry Memories</div>
<div style="text-align: center; font-size: 14px;">@angrymemorys-oldandtoothless-sadboi666_-witheredstrings</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 makeouthill & VacuumF & Jason Hendricks & Angry Memories.
| Data | makeouthill | VacuumF | Jason Hendricks | Angry Memories |
| --- | --- | --- | --- | --- |
| Tweets downloaded | 321 | 425 | 3250 | 3199 |
| Retweets | 34 | 0 | 0 | 941 |
| Short tweets | 49 | 31 | 0 | 1110 |
| Tweets kept | 238 | 394 | 3250 | 1148 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nh2rd94/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 @angrymemorys-oldandtoothless-sadboi666_-witheredstrings's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi/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/angrymemorys-oldandtoothless-sadboi666_-witheredstrings')
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)
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] |
nates/LER-roberta | bc3607faa3cf871f02f36f3d6fa65f2e326b3a58 | 2022-04-12T03:28:11.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | token-classification | false | nates | null | nates/LER-roberta | 0 | null | transformers | ---
license: cc-by-4.0
widget:
- text: This house was let out in tiny tenements and was inhabited by working people of all kinds--tailors, locksmiths, cooks, Germans ofsorts, girls picking up a living as best they could, petty clerks, etc.
example_title: "Crime and Punishment"
- text: Quixote having got on his back and the duke mounted a fine horse, they placed the duchess in the middle and set out for the castle.
example_title: "Don Quixote"
- text: The noble carriage of this gentleman, for whom he believed himself to be engaged, had won Planchet—that was the name of the Picard.
example_title: "The Three Musketeers"
---
### Description
A `roberta-base` model which has been fine tuned for token classification on the [LitBank](https://github.com/dbamman/litbank) dataset.
### Intended Use
This model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from [ACE 2005](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf)
- Person (PER)
- ORG
- GPE
- LOC
- VEH
- FAC
Due to the fine-tuning domain, it is expected to work best with literary sentences.
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junaidamk/mlner-mlwptok-muril | aed40ea46acad1f57295092187f1dc9810d64779 | 2022-04-12T08:57:48.000Z | [
"pytorch",
"bert",
"token-classification",
"dataset:mlner2021",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | junaidamk | null | junaidamk/mlner-mlwptok-muril | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mlner2021
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mlner-mlwptok-muril
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mlner2021
type: mlner2021
args: MLNER2021
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.8112759262826688
---
<!-- 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. -->
# mlner-mlwptok-muril
This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on the mlner2021 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8331
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8113
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 1.447 | 1.0 | 1389 | 0.9396 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.898 | 2.0 | 2778 | 0.8883 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.859 | 3.0 | 4167 | 0.8721 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.8302 | 4.0 | 5556 | 0.8666 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.8165 | 5.0 | 6945 | 0.8403 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.8143 | 6.0 | 8334 | 0.8376 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.8034 | 7.0 | 9723 | 0.8393 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.7766 | 8.0 | 11112 | 0.8383 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.794 | 9.0 | 12501 | 0.8346 | 0.0 | 0.0 | 0.0 | 0.8113 |
| 0.7858 | 10.0 | 13890 | 0.8331 | 0.0 | 0.0 | 0.0 | 0.8113 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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nnair25/Gram-Vaani-Harveen-Chadda-Fine-Tuning | e3fe71a121f576da1c402418e825d13403b5d330 | 2022-04-13T05:52:25.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | automatic-speech-recognition | false | nnair25 | null | nnair25/Gram-Vaani-Harveen-Chadda-Fine-Tuning | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
name: Gram-Vaani-Harveen-Chadda-Fine-Tuning
---
<!-- 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. -->
# Gram-Vaani-Harveen-Chadda-Fine-Tuning
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-hindi-him-4200) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8934
- Wer: 0.359
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 4.5558 | 21.05 | 400 | 0.8934 | 0.359 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
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johnowhitaker/mingpt-char-shakespeare | 0687519135b70307e60ed5369419e1894660c49c | 2022-04-12T07:56:59.000Z | [
"pytorch"
] | null | false | johnowhitaker | null | johnowhitaker/mingpt-char-shakespeare | 0 | null | null | Entry not found | [
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mimi/book_data | 9a4ec64cd99027097b9bac609664a53cdb72a5ac | 2022-05-12T01:50:23.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mimi | null | mimi/book_data | 0 | null | transformers | Entry not found | [
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Chris1/CycleGAN_punk2apes | 7db44664d29b484eff8f9dc1094b7e82e8132384 | 2022-04-12T08:10:37.000Z | [
"pytorch",
"huggan",
"gan",
"license:mit"
] | null | false | Chris1 | null | Chris1/CycleGAN_punk2apes | 0 | null | null | ---
tags:
- huggan
- gan
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
## Generated Images
You can embed local or remote images using ``
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
``` | [
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theojolliffe/opus-mt-en-ro-finetuned-en-to-cy | 2392a76de863b287ce99d91cac93c75b479d01e9 | 2022-04-13T11:10:10.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/opus-mt-en-ro-finetuned-en-to-cy | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: opus-mt-en-ro-finetuned-en-to-cy
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. -->
# opus-mt-en-ro-finetuned-en-to-cy
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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huggingtweets/nv1t | 1db943b3f55a42605435fed5f7e017ffc23039c1 | 2022-04-12T10:24:30.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/nv1t | 0 | null | transformers | ---
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/521651470832136193/8-XdhaC7_400x400.jpeg')">
</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">nuit</div>
<div style="text-align: center; font-size: 14px;">@nv1t</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 nuit.
| Data | nuit |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 520 |
| Short tweets | 127 |
| Tweets kept | 2597 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3aj0ls3h/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 @nv1t's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n9kpomf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n9kpomf/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/nv1t')
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)
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Chris1/ape2punk_epoch80 | f42c99e06db78f53a9460695268c1ad9ee52b137 | 2022-04-12T11:21:48.000Z | [
"pytorch",
"huggan",
"gan",
"license:mit"
] | null | false | Chris1 | null | Chris1/ape2punk_epoch80 | 0 | null | null | ---
tags:
- huggan
- gan
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
## Generated Images
You can embed local or remote images using ``
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
``` | [
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huggingnft/cryptopunks__2__bored-apes-yacht-club | aa592734173938947c65191aca14a719332ab0dd | 2022-04-25T16:05:53.000Z | [
"pytorch",
"arxiv:1703.10593",
"huggan",
"gan",
"image-to-image",
"huggingnft",
"nft",
"image",
"images",
"license:mit"
] | image-to-image | false | huggingnft | null | huggingnft/cryptopunks__2__bored-apes-yacht-club | 0 | 1 | null | ---
tags:
- huggan
- gan
- image-to-image
- huggingnft
- nft
- image
- images
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A generator B to A, named G_BA conditioned on an image from B
- A domain classifier D_A, associated with G_AB
- A domain classifier D_B, associated with G_BA
At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A.
In the general setting, this technique provides style transfer functionalities between the selected image domains A and B.
This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA.
Under these framework, these aspects have been used to perform style transfer between NFT collections.
A collection is selected as domain A, another one as domain B and the CycleGAN provides forward and backward translation between A and B.
This has showed to allows high quality translation even in absence of paired sample-ground-truth data.
In particular, the model performs well with stationary backgrounds (no drastic texture changes in the appearance of backgrounds) as it is capable of recognizing the attributes of each of the elements of an NFT collections.
An attribute can be a variation in type of dressed fashion items such as sunglasses, earrings, clothes and also face or body attributes with respect to a common template model of the given NFT collection).
## Intended uses & limitations
#### How to use
```python
import torch
from PIL import Image
from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet
from torchvision import transforms as T
from torchvision.transforms import Compose, Resize, ToTensor, Normalize
from torchvision.utils import make_grid
from huggingface_hub import hf_hub_download, file_download
from accelerate import Accelerator
import json
def load_lightweight_model(model_name):
file_path = file_download.hf_hub_download(
repo_id=model_name,
filename="config.json"
)
config = json.loads(open(file_path).read())
organization_name, name = model_name.split("/")
model = Trainer(**config, organization_name=organization_name, name=name)
model.load(use_cpu=True)
model.accelerator = Accelerator()
return model
def get_concat_h(im1, im2):
dst = Image.new('RGB', (im1.width + im2.width, im1.height))
dst.paste(im1, (0, 0))
dst.paste(im2, (im1.width, 0))
return dst
n_channels = 3
image_size = 256
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
# load the translation model from source to target images: source will be generated by a separate Lightweight GAN, w
# while the target images are the result of the translation applied by the GeneratorResnet to the generated source images.
# Hence, given the source domain A and target domain B,
# B = Translator(GAN(A))
translator = GeneratorResNet.from_pretrained(f'huggingnft/{model_name}',
input_shape=(n_channels, image_size, image_size),
num_residual_blocks=9)
# sample noise that is used to generate source images by the
z = torch.randn(nrows, 100, 1, 1)
# load the GAN generator of source images that will be translated by the translation model
model = load_lightweight_model(f"huggingnft/{model_name.split('__2__')[0]}")
collectionA = model.generate_app(
num=timestamped_filename(),
nrow=nrows,
checkpoint=-1,
types="default"
)[1]
# resize to translator model input shape
resize = T.Resize((256, 256))
input = resize(collectionA)
# translate the resized collectionA to collectionB
collectionB = translator(input)
out_transform = T.ToPILImage()
results = []
for collA_image, collB_image in zip(input, collectionB):
results.append(
get_concat_h(out_transform(make_grid(collA_image, nrow=1, normalize=True)), out_transform(make_grid(collB_image, nrow=1, normalize=True)))
)
```
#### Limitations and bias
Translation between collections provides exceptional output images in the case of NFT collections that portray subjects in the same way.
If the backgrounds vary too much within either of the collections, performance degrades or many more training iterations re required to achieve acceptable results.
## Training data
The CycleGAN model is trained on an unpaired dataset of samples from two selected NFT collections: colle tionA and collectionB.
To this end, two collections are loaded by means of the function load_dataset in the huggingface library, as follows.
A list of all available collections is available at [huggingNFT](https://huggingface.co/huggingnft)
```python
from datasets import load_dataset
collectionA = load_dataset("huggingnft/COLLECTION_A")
collectionB = load_dataset("huggingnft/COLLECTION_B")
```
## Training procedure
#### Preprocessing
The following transformations are applied to each input sample of collectionA and collectionB.
The input size is fixed to RGB images of height, width = 256, 256
```python
n_channels = 3
image_size = 256
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
```
#### Hardware
The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs.
#### Hyperparameters
The following configuration has been kept fixed for all translation models:
- learning rate 0.0002
- number of epochs 200
- learning rate decay activation at epoch 80
- number of residual blocks of the cyclegan 9
- cycle loss weight 10.0
- identity loss weight 5.0
- optimizer ADAM with beta1 0.5 and beta2 0.999
- batch size 8
- NO mixed precision training
## Eval results
#### Training reports
[Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/CycleGAN-training-report--VmlldzoxODUxNzQz?accessToken=vueurpbhd2i8n347j880yakggs0sqdf7u0hpz3bpfsbrxcmk1jk4obg18f6wfk9w)
[Boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/CycleGAN-training-report--VmlldzoxODUxNzg4?accessToken=jpyviwn7kdf5216ycrthwp6l8t3heb0lt8djt7dz12guu64qnpdh3ekecfcnoahu)
#### Generated Images
In the provided images, row0 and row2 represent real images from the respective collections.
Row1 is the translation of the immediate above images in row0 by means of the G_AB translation model.
Row3 is the translation of the immediate above images in row2 by means of the G_BA translation model.
Visualization over the training iterations for [boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/Shared-panel-22-04-15-08-04-99--VmlldzoxODQ0MDI3?accessToken=45m3kxex5m3rpev3s6vmrv69k3u9p9uxcsp2k90wvbxwxzlqbqjqlnmgpl9265c0)
Visualization over the training iterations for [Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/Shared-panel-22-04-17-11-04-83--VmlldzoxODUxNjk5?accessToken=o25si6nflp2xst649vt6ayt56bnb95mxmngt1ieso091j2oazmqnwaf4h78vc2tu)
### References
```bibtex
@misc{https://doi.org/10.48550/arxiv.1703.10593,
doi = {10.48550/ARXIV.1703.10593},
url = {https://arxiv.org/abs/1703.10593},
author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### BibTeX entry and citation info
```bibtex
@InProceedings{huggingnft,
author={Aleksey Korshuk, Christian Cancedda}
year=2022
}
```
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] |
Chris1/real2sim | e14a0dac96ae97890973a5223a9d46ebe882e411 | 2022-04-12T11:33:32.000Z | [
"pytorch",
"huggan",
"gan",
"license:mit"
] | null | false | Chris1 | null | Chris1/real2sim | 0 | null | null | ---
tags:
- huggan
- gan
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
## Generated Images
You can embed local or remote images using ``
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
``` | [
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huggan/sim2real_cyclegan | 0a60c15105b38055c6897be6d91f142fd9a903e1 | 2022-05-05T10:55:19.000Z | [
"pytorch",
"arxiv:2104.13395",
"arxiv:1703.10593",
"conditional-image-generation",
"image-to-image",
"gan",
"cyclegan",
"license:mit"
] | image-to-image | false | huggan | null | huggan/sim2real_cyclegan | 0 | 1 | null | ---
tags:
- conditional-image-generation
- image-to-image
- gan
- cyclegan
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A generator B to A, named G_BA conditioned on an image from B
- A domain classifier D_A, associated with G_AB
- A domain classifier D_B, associated with G_BA
At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A.
In the general setting, this technique provides style transfer functionalities between the selected image domains A and B.
This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA.
Under these framework, these aspects have been used to perform style transfer between synthetic data obtained from a simulated driving dataset, GTA5, and the real driving data from Cityscapes.
This is of paramount importance to develop autonomous driving perception deep learning models, as this allows to generate synthetic data with automatic annotations which resembles real world images, without requiring the intervention of a human annotator.
This is fundamental because a manual annotator has been shown to require 1.5 to 3.3 hours to create semantic and instance segmentation masks for a single images.
These have been provided in the original [cityscapes paper (Cordts et al 2016)](https://arxiv.org/abs/2104.13395) and the [adverse condition dataset (Sakaridis et al. 2021)](https://arxiv.org/abs/2104.13395) paper.
Hence the CycleGAN provides forward and backward translation between synthetic and real world data.
This has showed to allows high quality translation even in absence of paired sample-ground-truth data.
The idea behind such model is that as the synthetic data distribution gets closer to the real world one, deep models do not suffer from degraded performance due to the domain shift issue.
A broad literature is available on the minimization of the domain shift, under the research branch of domain adaptation and transfer learning, of which image translation models provide an alternative approach
## Intended uses & limitations
#### How to use
```python
import os
from PIL import Image
from torchvision import transforms as T
from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip
from torchvision.utils import make_grid
from torch.utils.data import DataLoader
from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet
import torch.nn as nn
import torch
import gradio as gr
import glob
def pred_pipeline(img, transforms):
orig_shape = img.shape
input = transforms(img)
input = input.unsqueeze(0)
output = model(input)
out_img = make_grid(output,#.detach().cpu(),
nrow=1, normalize=True)
out_transform = Compose([
T.Resize(orig_shape[:2]),
T.ToPILImage()
])
return out_transform(out_img)
n_channels = 3
image_size = 512
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
model = GeneratorResNet.from_pretrained('Chris1/sim2real', input_shape=(n_channels, image_size, image_size),
num_residual_blocks=9)
real_images = model(synthetic_images)
```
#### Limitations and bias
Due to the absence of paired data, some background parts of the synthetic images are seldom wrongly translated, e.g. sky is translated to vegetation.
Additional pretext tasks in parallel to the discriminative classifier of fake and real samples could improve the result.
One easy improvement is the use of an additional parallel branch that performs semantic segmentation on the synthetic data, in order to learn features which are common to sky and vegetation, thus disentangling their representations as separate classes.
## Training data
The CycleGAN model is trained on an unpaired dataset of samples from synthetic and real driving data, respectively from the GTA5 and Cityscapes datasets.
To this end, the synthetic-to-real dataset can be loaded by means of the function load_dataset in the huggingface library, as follows.
```python
from datasets import load_dataset
unpaired_dataset = load_dataset("huggan/sim2real_gta5_to_cityscapes")
```
This dataset contains two columns, imageA and imageB representing respectively the GTA5 and Cityscapes data.
Due to the fact that the two columns have to be of the same length, GTA5 is subsampled in order to reach the same number of samples provided by the Cityscapes train split (2975)
## Training procedure
#### Preprocessing
The following transformations are applied to each input sample of synthetic and real data.
The input size is fixed to RGB images of height, width = 512, 512.
This choice has been made in order to limit the impact of upsampling the translated images to higher resolutions.
```python
n_channels = 3
image_size = 512
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
```
#### Hardware
The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs.
#### Hyperparameters
The following configuration has been kept fixed for all translation models:
- learning rate 0.0002
- number of epochs 200
- learning rate decay activation at epoch 100
- number of residual blocks of the cyclegan 9
- image size 512x512
- number of channels=3
- cycle loss weight 10.0
- identity loss weight 5.0
- optimizer ADAM with beta1 0.5 and beta2 0.999
- batch size 8
- NO mixed precision training
## Eval results
#### Generated Images
In the provided images, row0 and row2 represent the synthetic and real images from the respective datasets.
Row1 is the translation of the immediate above images in row0(synthetic) by means of the G_AB translation model, to the real world style.
Row3 is the translation of the immediate above images in row2(real) by means of the G_BA translation model, to the synthetic world style.
Visualization over the training iterations for [synthetic (GTA5) to real (Cityscapes) translation](https://wandb.ai/chris1nexus/experiments_cyclegan_s2r_hp_opt--10/reports/CycleGAN-sim2real-training-results--VmlldzoxODUyNTk4?accessToken=tow3v4vp02aurzodedrdht15ig1cx69v5mited4dm8bgnup0z192wri0xtftaeqj)
### References
```bibtex
@misc{https://doi.org/10.48550/arxiv.1703.10593,
doi = {10.48550/ARXIV.1703.10593},
url = {https://arxiv.org/abs/1703.10593},
author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
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sanchit-gandhi/tiny-random-bart | 31143b0f16b739cbadec03b5d2a0025fba90918c | 2022-04-12T13:32:12.000Z | [
"pytorch",
"jax",
"bart",
"feature-extraction",
"transformers"
] | feature-extraction | false | sanchit-gandhi | null | sanchit-gandhi/tiny-random-bart | 0 | null | transformers | Entry not found | [
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Guldeniz/pix2pix_maps | a8ef56fd47c2d8f9b245daa1bd71eceb4c7ce391 | 2022-04-12T17:44:34.000Z | [
"pytorch",
"huggan",
"gan",
"license:mit"
] | null | false | Guldeniz | null | Guldeniz/pix2pix_maps | 0 | null | null | ---
tags:
- huggan
- gan
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
## Generated Images
You can embed local or remote images using ``
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
``` | [
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huggan/pix2pix-maps-test | 39acb13bdb610983eeb8c10c5edbc8bc2d700a13 | 2022-04-12T15:57:14.000Z | [
"pytorch",
"huggan",
"gan",
"license:mit"
] | null | false | huggan | null | huggan/pix2pix-maps-test | 0 | null | null | ---
tags:
- huggan
- gan
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
## Generated Images
You can embed local or remote images using ``
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
``` | [
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nielsr/test | 7717b62c60f0e314a56fb96fe9d98975826176a5 | 2022-04-12T15:20:09.000Z | [
"pytorch"
] | null | false | nielsr | null | nielsr/test | 0 | null | null | Entry not found | [
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rajat99/SSL-Harveen-Chadda-Fine-Tuning | 37d5efecb32d113c24e8eb9b2263efda5b8aa737 | 2022-04-12T22:07:20.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | automatic-speech-recognition | false | rajat99 | null | rajat99/SSL-Harveen-Chadda-Fine-Tuning | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
name: SSL-Harveen-Chadda-Fine-Tuning
---
<!-- 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. -->
# SSL-Harveen-Chadda-Fine-Tuning
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-hindi-him-4200) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0032
- Wer: 0.1008
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3793 | 4.17 | 400 | 0.5347 | 0.2584 |
| 0.2137 | 8.33 | 800 | 0.8339 | 0.2664 |
| 0.1282 | 12.5 | 1200 | 0.1785 | 0.1414 |
| 0.0698 | 16.66 | 1600 | 0.0135 | 0.1066 |
| 0.0354 | 20.83 | 2000 | 0.0351 | 0.1179 |
| 0.0212 | 24.99 | 2400 | 0.0104 | 0.1035 |
| 0.0066 | 29.17 | 2800 | 0.0032 | 0.1008 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
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huggan/pix2pix-uavid-15 | bfa54b692d5d897bf7cb4181db1535e5ce303049 | 2022-04-15T13:45:13.000Z | [
"pytorch",
"dataset:arakesh/uavid-15-hq-mixedres",
"arxiv:1611.07004",
"huggan",
"gan",
"license:mit"
] | null | false | huggan | null | huggan/pix2pix-uavid-15 | 0 | null | null | ---
tags:
- huggan
- gan
datasets:
- arakesh/uavid-15-hq-mixedres
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
[Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
## Intended uses & limitations:
Used for reconstruction of images from edges
#### How to use
```python
from torchvision.transforms import Compose, Resize, ToTensor, Normalize
from PIL import Image
from torchvision.utils import save_image
import cv2
from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet
transform = Compose(
[
Resize((256, 256), Image.BICUBIC),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
model = GeneratorUNet.from_pretrained('huggan/pix2pix-uavid-15)
def predict_fn(img):
inp = transform(img).unsqueeze(0)
out = model(inp)
save_image(out, 'out.png', normalize=True)
return 'out.png'
predict_fn(img)
```
#### Limitations and bias
* Gives unrealistic colors in the image
## Training data
* [edges2shoes](https://huggingface.co/datasets/huggan/edges2shoes)
## Training procedure
```
# clone the repository
git clone https://github.com/huggingface/community-events.git
pip install .
# change directory
cd community-events/huggan/pytorch/pix2pix/
# define config
accelerate config
# launch training with required parameters
accelerate launch train.py --checkpoint_interval 1 --dataset arakesh/uavid-15-hq-mixedres --push_to_hub --model_name pix2pix-uavid-15 --batch_size 2 --n_epochs 50 --image_size 1024 --sample_interval 500
```
## Generated Images
Here,
* First Image Row: Input Image
* Second Image Row: Generated Image
* Third Image Row: Target Image
### BibTeX entry and citation info
```bibtex
@article{pix2pix2017,
title={Image-to-Image Translation with Conditional Adversarial Networks},
author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
journal={CVPR},
year={2017}
}
``` | [
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lilitket/20220412-203254 | 7988a597cdefd4bdcf9dadaae38071a50cc43f42 | 2022-04-13T05:04:25.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220412-203254 | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: 20220412-203254
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. -->
# 20220412-203254
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0428
- Wer: 1.0019
## 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: 6e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 1200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:------:|:---------------:|:------:|
| 16.9455 | 1.5 | 200 | 16.4676 | 1.2534 |
| 15.444 | 3.01 | 400 | 14.1207 | 1.0 |
| 9.5452 | 4.51 | 600 | 8.4030 | 1.0 |
| 6.2565 | 6.02 | 800 | 6.5233 | 1.0 |
| 5.2827 | 7.52 | 1000 | 5.6058 | 1.0 |
| 4.7652 | 9.02 | 1200 | 4.9765 | 1.0 |
| 4.3803 | 10.53 | 1400 | 4.4565 | 1.0 |
| 4.0005 | 12.03 | 1600 | 4.0224 | 1.0 |
| 3.7041 | 13.53 | 1800 | 3.6903 | 1.0 |
| 3.4991 | 15.04 | 2000 | 3.4642 | 1.0 |
| 3.34 | 16.54 | 2200 | 3.3425 | 1.0 |
| 3.2352 | 18.05 | 2400 | 3.2617 | 1.0 |
| 3.1867 | 19.55 | 2600 | 3.2358 | 1.0 |
| 3.161 | 21.05 | 2800 | 3.2289 | 1.0 |
| 3.145 | 22.56 | 3000 | 3.2023 | 1.0 |
| 3.1203 | 24.06 | 3200 | 3.1964 | 1.0 |
| 3.1109 | 25.56 | 3400 | 3.1844 | 1.0 |
| 3.0958 | 27.07 | 3600 | 3.1839 | 1.0 |
| 3.0732 | 28.57 | 3800 | 3.2058 | 1.0 |
| 3.0535 | 30.08 | 4000 | 3.1843 | 1.0 |
| 3.0243 | 31.58 | 4200 | 3.1992 | 1.0 |
| 2.9829 | 33.08 | 4400 | 3.2019 | 1.0 |
| 2.9219 | 34.59 | 4600 | 3.2346 | 1.0 |
| 2.8313 | 36.09 | 4800 | 3.2781 | 1.0 |
| 2.7186 | 37.59 | 5000 | 3.3056 | 1.0 |
| 2.5745 | 39.1 | 5200 | 3.3554 | 1.0 |
| 2.4028 | 40.6 | 5400 | 3.4331 | 1.0 |
| 2.2645 | 42.11 | 5600 | 3.4418 | 1.0 |
| 2.1303 | 43.61 | 5800 | 3.5584 | 1.0 |
| 2.0257 | 45.11 | 6000 | 3.5943 | 1.0 |
| 1.9223 | 46.62 | 6200 | 3.6767 | 1.0 |
| 1.8344 | 48.12 | 6400 | 3.7363 | 1.0 |
| 1.7574 | 49.62 | 6600 | 3.8921 | 1.0 |
| 1.67 | 51.13 | 6800 | 3.9054 | 1.0 |
| 1.6118 | 52.63 | 7000 | 4.0352 | 1.0 |
| 1.5372 | 54.14 | 7200 | 3.9742 | 1.0 |
| 1.4846 | 55.64 | 7400 | 4.1078 | 1.0 |
| 1.4093 | 57.14 | 7600 | 4.1705 | 1.0 |
| 1.3379 | 58.65 | 7800 | 4.2737 | 1.0 |
| 1.28 | 60.15 | 8000 | 4.3662 | 1.0 |
| 1.2268 | 61.65 | 8200 | 4.4278 | 1.0 |
| 1.1641 | 63.16 | 8400 | 4.4831 | 1.0 |
| 1.1058 | 64.66 | 8600 | 4.5354 | 1.0 |
| 1.0596 | 66.17 | 8800 | 4.5983 | 1.0 |
| 0.9953 | 67.67 | 9000 | 4.7143 | 1.0 |
| 0.9406 | 69.17 | 9200 | 4.8536 | 1.0 |
| 0.9022 | 70.68 | 9400 | 4.7732 | 1.0 |
| 0.8551 | 72.18 | 9600 | 4.8929 | 1.0 |
| 0.8103 | 73.68 | 9800 | 4.9513 | 1.0 |
| 0.7665 | 75.19 | 10000 | 4.9530 | 1.0 |
| 0.7215 | 76.69 | 10200 | 5.1471 | 1.0 |
| 0.6906 | 78.2 | 10400 | 5.2295 | 1.0 |
| 0.6354 | 79.7 | 10600 | 5.1287 | 1.0 |
| 0.6196 | 81.2 | 10800 | 5.2081 | 1.0 |
| 0.6026 | 82.71 | 11000 | 5.4323 | 1.0 |
| 0.5726 | 84.21 | 11200 | 5.3907 | 1.0 |
| 0.5348 | 85.71 | 11400 | 5.5669 | 1.0 |
| 0.5344 | 87.22 | 11600 | 5.5685 | 1.0 |
| 0.4849 | 88.72 | 11800 | 5.5814 | 1.0 |
| 0.4689 | 90.23 | 12000 | 5.6186 | 1.0 |
| 0.4646 | 91.73 | 12200 | 5.4834 | 1.0 |
| 0.4266 | 93.23 | 12400 | 5.6463 | 1.0 |
| 0.4424 | 94.74 | 12600 | 5.6562 | 1.0 |
| 0.3865 | 96.24 | 12800 | 5.7463 | 1.0 |
| 0.3914 | 97.74 | 13000 | 5.7014 | 1.0 |
| 0.3661 | 99.25 | 13200 | 5.7543 | 1.0 |
| 0.3582 | 100.75 | 13400 | 5.9172 | 1.0 |
| 0.3571 | 102.26 | 13600 | 5.5968 | 1.0 |
| 0.3343 | 103.76 | 13800 | 5.3691 | 1.0 |
| 0.3123 | 105.26 | 14000 | 5.8917 | 1.0 |
| 0.3089 | 106.77 | 14200 | 5.8054 | 1.0 |
| 0.3078 | 108.27 | 14400 | 5.9066 | 1.0 |
| 0.3076 | 109.77 | 14600 | 5.7379 | 1.0 |
| 0.2924 | 111.28 | 14800 | 5.7931 | 1.0 |
| 0.2925 | 112.78 | 15000 | 5.9529 | 1.0 |
| 0.2839 | 114.29 | 15200 | 5.9881 | 1.0 |
| 0.2599 | 115.79 | 15400 | 6.0081 | 1.0 |
| 0.2685 | 117.29 | 15600 | 6.1049 | 1.0 |
| 0.2557 | 118.8 | 15800 | 6.1154 | 1.0 |
| 0.2688 | 120.3 | 16000 | 5.9336 | 1.0 |
| 0.2422 | 121.8 | 16200 | 6.0492 | 1.0 |
| 0.2408 | 123.31 | 16400 | 6.3155 | 1.0 |
| 0.2423 | 124.81 | 16600 | 6.3437 | 1.0 |
| 0.2421 | 126.32 | 16800 | 6.0979 | 1.0 |
| 0.2212 | 127.82 | 17000 | 5.5551 | 1.0 |
| 0.2239 | 129.32 | 17200 | 5.9007 | 1.0 |
| 0.2101 | 130.83 | 17400 | 6.0142 | 1.0 |
| 0.2097 | 132.33 | 17600 | 5.8984 | 1.0 |
| 0.2064 | 133.83 | 17800 | 5.9705 | 1.0 |
| 0.1898 | 135.34 | 18000 | 5.9915 | 1.0 |
| 0.2053 | 136.84 | 18200 | 6.1079 | 1.0 |
| 0.1822 | 138.35 | 18400 | 6.1324 | 1.0 |
| 0.1867 | 139.85 | 18600 | 6.1122 | 1.0 |
| 0.1831 | 141.35 | 18800 | 6.1476 | 1.0 |
| 0.1935 | 142.86 | 19000 | 5.7248 | 1.0 |
| 0.1983 | 144.36 | 19200 | 6.1466 | 1.0 |
| 0.176 | 145.86 | 19400 | 5.9555 | 1.0 |
| 0.1778 | 147.37 | 19600 | 6.1434 | 1.0 |
| 0.1758 | 148.87 | 19800 | 6.2104 | 1.0 |
| 0.1799 | 150.38 | 20000 | 6.0933 | 1.0 |
| 0.1674 | 151.88 | 20200 | 6.0476 | 1.0 |
| 0.1777 | 153.38 | 20400 | 5.8937 | 1.0 |
| 0.1616 | 154.89 | 20600 | 6.4417 | 1.0 |
| 0.1498 | 156.39 | 20800 | 6.3136 | 1.0 |
| 0.1607 | 157.89 | 21000 | 5.9295 | 1.0 |
| 0.1445 | 159.4 | 21200 | 6.2741 | 1.0 |
| 0.1636 | 160.9 | 21400 | 6.1931 | 1.0 |
| 0.1488 | 162.41 | 21600 | 6.0089 | 1.0 |
| 0.1549 | 163.91 | 21800 | 5.6184 | 1.0 |
| 0.1532 | 165.41 | 22000 | 6.1250 | 1.0 |
| 0.1581 | 166.92 | 22200 | 6.2635 | 1.0 |
| 0.146 | 168.42 | 22400 | 6.0498 | 1.0 |
| 0.148 | 169.92 | 22600 | 6.3486 | 1.0 |
| 0.1489 | 171.43 | 22800 | 6.1659 | 1.0 |
| 0.1464 | 172.93 | 23000 | 6.2259 | 1.0 |
| 0.139 | 174.44 | 23200 | 6.2796 | 1.0 |
| 0.1357 | 175.94 | 23400 | 6.2119 | 1.0 |
| 0.1435 | 177.44 | 23600 | 6.5722 | 1.0 |
| 0.1172 | 178.95 | 23800 | 6.4221 | 1.0 |
| 0.1539 | 180.45 | 24000 | 6.3963 | 1.0 |
| 0.1389 | 181.95 | 24200 | 6.2367 | 1.0 |
| 0.1274 | 183.46 | 24400 | 6.3693 | 1.0 |
| 0.1295 | 184.96 | 24600 | 6.0819 | 1.0 |
| 0.1337 | 186.47 | 24800 | 6.1525 | 1.0 |
| 0.1303 | 187.97 | 25000 | 6.2520 | 1.0 |
| 0.141 | 189.47 | 25200 | 6.5302 | 1.0 |
| 0.1322 | 190.98 | 25400 | 6.3731 | 1.0 |
| 0.1313 | 192.48 | 25600 | 6.3570 | 1.0 |
| 0.1178 | 193.98 | 25800 | 6.1667 | 1.0 |
| 0.1277 | 195.49 | 26000 | 6.1352 | 1.0 |
| 0.1169 | 196.99 | 26200 | 6.3132 | 1.0 |
| 0.1199 | 198.5 | 26400 | 6.6116 | 1.0 |
| 0.1138 | 200.0 | 26600 | 6.4862 | 1.0 |
| 0.1129 | 201.5 | 26800 | 6.3442 | 1.0 |
| 0.1142 | 203.01 | 27000 | 6.5077 | 1.0 |
| 0.1169 | 204.51 | 27200 | 6.5710 | 1.0 |
| 0.111 | 206.02 | 27400 | 6.0623 | 1.0 |
| 0.1198 | 207.52 | 27600 | 6.4331 | 1.0 |
| 0.1108 | 209.02 | 27800 | 5.9192 | 1.0 |
| 0.1121 | 210.53 | 28000 | 6.0724 | 1.0 |
| 0.1171 | 212.03 | 28200 | 6.3363 | 1.0 |
| 0.1188 | 213.53 | 28400 | 6.3704 | 1.0 |
| 0.104 | 215.04 | 28600 | 6.5802 | 1.0 |
| 0.1125 | 216.54 | 28800 | 5.4428 | 1.0 |
| 0.1115 | 218.05 | 29000 | 6.4286 | 1.0 |
| 0.1109 | 219.55 | 29200 | 6.6998 | 1.0 |
| 0.1061 | 221.05 | 29400 | 6.3761 | 1.0 |
| 0.1161 | 222.56 | 29600 | 5.8712 | 1.0 |
| 0.1091 | 224.06 | 29800 | 6.1844 | 1.0 |
| 0.0947 | 225.56 | 30000 | 6.5670 | 1.0 |
| 0.1004 | 227.07 | 30200 | 6.2302 | 1.0 |
| 0.1099 | 228.57 | 30400 | 6.4218 | 1.0 |
| 0.1154 | 230.08 | 30600 | 6.4911 | 1.0 |
| 0.0999 | 231.58 | 30800 | 6.4390 | 1.0 |
| 0.1068 | 233.08 | 31000 | 6.2367 | 1.0 |
| 0.1015 | 234.59 | 31200 | 6.0790 | 1.0 |
| 0.0958 | 236.09 | 31400 | 5.9926 | 1.0 |
| 0.1183 | 237.59 | 31600 | 6.3400 | 1.0 |
| 0.0833 | 239.1 | 31800 | 6.4481 | 1.0 |
| 0.0874 | 240.6 | 32000 | 6.4535 | 1.0 |
| 0.0958 | 242.11 | 32200 | 6.0597 | 1.0 |
| 0.1075 | 243.61 | 32400 | 6.3403 | 1.0 |
| 0.0909 | 245.11 | 32600 | 6.1297 | 1.0 |
| 0.1093 | 246.62 | 32800 | 6.2232 | 1.0 |
| 0.0995 | 248.12 | 33000 | 6.7110 | 1.0 |
| 0.1061 | 249.62 | 33200 | 5.8551 | 1.0 |
| 0.0872 | 251.13 | 33400 | 6.0338 | 1.0 |
| 0.109 | 252.63 | 33600 | 6.2880 | 1.0 |
| 0.0976 | 254.14 | 33800 | 5.9304 | 1.0 |
| 0.0977 | 255.64 | 34000 | 6.4527 | 1.0 |
| 0.0895 | 257.14 | 34200 | 6.3178 | 1.0 |
| 0.0951 | 258.65 | 34400 | 6.3646 | 1.0 |
| 0.0942 | 260.15 | 34600 | 6.4405 | 1.0 |
| 0.0876 | 261.65 | 34800 | 5.8373 | 1.0 |
| 0.0877 | 263.16 | 35000 | 6.5296 | 1.0 |
| 0.0896 | 264.66 | 35200 | 6.6644 | 1.0 |
| 0.0938 | 266.17 | 35400 | 6.4721 | 1.0 |
| 0.0864 | 267.67 | 35600 | 7.0132 | 1.0 |
| 0.0897 | 269.17 | 35800 | 6.3767 | 1.0 |
| 0.094 | 270.68 | 36000 | 6.1663 | 1.0 |
| 0.0782 | 272.18 | 36200 | 5.7325 | 1.0 |
| 0.0878 | 273.68 | 36400 | 6.0681 | 1.0 |
| 0.0877 | 275.19 | 36600 | 6.2621 | 1.0 |
| 0.0827 | 276.69 | 36800 | 5.9692 | 1.0 |
| 0.0929 | 278.2 | 37000 | 6.0207 | 1.0 |
| 0.0899 | 279.7 | 37200 | 5.4185 | 1.0 |
| 0.0841 | 281.2 | 37400 | 5.9206 | 1.0 |
| 0.0924 | 282.71 | 37600 | 6.1820 | 1.0 |
| 0.0844 | 284.21 | 37800 | 6.1505 | 1.0 |
| 0.0824 | 285.71 | 38000 | 6.1564 | 1.0 |
| 0.0842 | 287.22 | 38200 | 5.9483 | 1.0 |
| 0.0863 | 288.72 | 38400 | 5.9305 | 1.0 |
| 0.0851 | 290.23 | 38600 | 5.8416 | 1.0 |
| 0.079 | 291.73 | 38800 | 5.7345 | 1.0 |
| 0.081 | 293.23 | 39000 | 5.7323 | 1.0 |
| 0.0873 | 294.74 | 39200 | 5.9131 | 1.0 |
| 0.0836 | 296.24 | 39400 | 6.1722 | 1.0 |
| 0.0774 | 297.74 | 39600 | 5.9523 | 1.0 |
| 0.0902 | 299.25 | 39800 | 5.8769 | 1.0 |
| 0.0766 | 300.75 | 40000 | 6.2435 | 1.0 |
| 0.0766 | 302.26 | 40200 | 5.7556 | 1.0 |
| 0.0723 | 303.76 | 40400 | 5.4647 | 1.0 |
| 0.0724 | 305.26 | 40600 | 6.0184 | 1.0 |
| 0.0834 | 306.77 | 40800 | 5.8434 | 1.0 |
| 0.0846 | 308.27 | 41000 | 6.0586 | 1.0 |
| 0.0878 | 309.77 | 41200 | 5.7270 | 1.0 |
| 0.0761 | 311.28 | 41400 | 5.7259 | 1.0 |
| 0.0639 | 312.78 | 41600 | 6.0848 | 1.0 |
| 0.0733 | 314.29 | 41800 | 6.0474 | 1.0 |
| 0.0734 | 315.79 | 42000 | 5.9387 | 1.0 |
| 0.0779 | 317.29 | 42200 | 5.6040 | 1.0 |
| 0.0713 | 318.8 | 42400 | 6.3136 | 1.0 |
| 0.0756 | 320.3 | 42600 | 5.8936 | 1.0 |
| 0.0758 | 321.8 | 42800 | 6.3659 | 1.0 |
| 0.0662 | 323.31 | 43000 | 5.8035 | 1.0 |
| 0.0714 | 324.81 | 43200 | 5.3194 | 1.0 |
| 0.0782 | 326.32 | 43400 | 6.0054 | 1.0 |
| 0.0775 | 327.82 | 43600 | 5.8471 | 1.0 |
| 0.0653 | 329.32 | 43800 | 5.4054 | 1.0 |
| 0.0739 | 330.83 | 44000 | 6.0978 | 1.0 |
| 0.0634 | 332.33 | 44200 | 6.0081 | 1.0 |
| 0.075 | 333.83 | 44400 | 6.0761 | 1.0 |
| 0.0609 | 335.34 | 44600 | 5.8444 | 1.0 |
| 0.0622 | 336.84 | 44800 | 6.2485 | 1.0 |
| 0.0757 | 338.35 | 45000 | 6.0131 | 1.0 |
| 0.0758 | 339.85 | 45200 | 5.9577 | 1.0 |
| 0.0617 | 341.35 | 45400 | 5.7657 | 1.0 |
| 0.0694 | 342.86 | 45600 | 5.7509 | 1.0 |
| 0.0646 | 344.36 | 45800 | 5.5593 | 1.0 |
| 0.0548 | 345.86 | 46000 | 5.9096 | 1.0 |
| 0.0604 | 347.37 | 46200 | 6.2313 | 1.0 |
| 0.0505 | 348.87 | 46400 | 5.4780 | 1.0 |
| 0.0631 | 350.38 | 46600 | 6.0868 | 1.0 |
| 0.0622 | 351.88 | 46800 | 5.8833 | 1.0 |
| 0.0605 | 353.38 | 47000 | 5.5888 | 1.0 |
| 0.0632 | 354.89 | 47200 | 5.7510 | 1.0 |
| 0.0658 | 356.39 | 47400 | 5.2321 | 1.0 |
| 0.0561 | 357.89 | 47600 | 5.6745 | 1.0 |
| 0.0737 | 359.4 | 47800 | 6.0472 | 1.0 |
| 0.0561 | 360.9 | 48000 | 6.2185 | 1.0 |
| 0.0564 | 362.41 | 48200 | 6.0749 | 1.0 |
| 0.0626 | 363.91 | 48400 | 5.6136 | 1.0 |
| 0.0725 | 365.41 | 48600 | 5.7983 | 1.0 |
| 0.0602 | 366.92 | 48800 | 5.5020 | 1.0 |
| 0.0599 | 368.42 | 49000 | 6.0626 | 1.0 |
| 0.0728 | 369.92 | 49200 | 6.3407 | 1.0 |
| 0.0561 | 371.43 | 49400 | 6.2899 | 1.0 |
| 0.0611 | 372.93 | 49600 | 6.5780 | 1.0 |
| 0.065 | 374.44 | 49800 | 6.4685 | 1.0 |
| 0.0561 | 375.94 | 50000 | 5.5252 | 1.0 |
| 0.0482 | 377.44 | 50200 | 5.3905 | 1.0 |
| 0.0575 | 378.95 | 50400 | 5.5660 | 1.0 |
| 0.0673 | 380.45 | 50600 | 6.3424 | 1.0 |
| 0.0588 | 381.95 | 50800 | 6.5294 | 1.0 |
| 0.0563 | 383.46 | 51000 | 5.2974 | 1.0 |
| 0.0702 | 384.96 | 51200 | 5.8705 | 1.0 |
| 0.0517 | 386.47 | 51400 | 5.7488 | 1.0 |
| 0.0629 | 387.97 | 51600 | 5.8414 | 1.0 |
| 0.0569 | 389.47 | 51800 | 5.3303 | 1.0 |
| 0.0586 | 390.98 | 52000 | 5.1755 | 1.0 |
| 0.0581 | 392.48 | 52200 | 6.0030 | 1.0 |
| 0.0673 | 393.98 | 52400 | 5.9972 | 1.0 |
| 0.0533 | 395.49 | 52600 | 6.1624 | 1.0 |
| 0.0597 | 396.99 | 52800 | 5.6803 | 1.0 |
| 0.0494 | 398.5 | 53000 | 5.4154 | 1.0 |
| 0.0526 | 400.0 | 53200 | 5.5855 | 1.0 |
| 0.0578 | 401.5 | 53400 | 5.9491 | 1.0 |
| 0.0546 | 403.01 | 53600 | 5.9381 | 1.0 |
| 0.0575 | 404.51 | 53800 | 5.9629 | 1.0 |
| 0.0592 | 406.02 | 54000 | 5.8617 | 1.0 |
| 0.0444 | 407.52 | 54200 | 5.5513 | 1.0 |
| 0.0467 | 409.02 | 54400 | 5.2998 | 1.0 |
| 0.0654 | 410.53 | 54600 | 5.3034 | 1.0 |
| 0.0546 | 412.03 | 54800 | 5.3077 | 1.0 |
| 0.0567 | 413.53 | 55000 | 5.0215 | 1.0 |
| 0.0564 | 415.04 | 55200 | 5.4569 | 1.0 |
| 0.0494 | 416.54 | 55400 | 5.7311 | 1.0 |
| 0.0448 | 418.05 | 55600 | 5.6774 | 1.0 |
| 0.0695 | 419.55 | 55800 | 5.5563 | 1.0 |
| 0.0451 | 421.05 | 56000 | 6.0087 | 1.0 |
| 0.0514 | 422.56 | 56200 | 5.4969 | 1.0 |
| 0.0504 | 424.06 | 56400 | 6.0321 | 1.0 |
| 0.0429 | 425.56 | 56600 | 5.6021 | 1.0 |
| 0.0503 | 427.07 | 56800 | 5.8039 | 1.0 |
| 0.0528 | 428.57 | 57000 | 5.9237 | 1.0 |
| 0.0508 | 430.08 | 57200 | 5.7653 | 1.0 |
| 0.0533 | 431.58 | 57400 | 6.2778 | 1.0 |
| 0.048 | 433.08 | 57600 | 6.0965 | 1.0 |
| 0.0492 | 434.59 | 57800 | 5.3128 | 1.0 |
| 0.0438 | 436.09 | 58000 | 5.7699 | 1.0 |
| 0.0525 | 437.59 | 58200 | 5.1163 | 1.0 |
| 0.0573 | 439.1 | 58400 | 5.4089 | 1.0 |
| 0.0587 | 440.6 | 58600 | 5.2019 | 1.0 |
| 0.049 | 442.11 | 58800 | 5.4515 | 1.0 |
| 0.0435 | 443.61 | 59000 | 5.5448 | 1.0 |
| 0.0378 | 445.11 | 59200 | 5.8339 | 1.0 |
| 0.0498 | 446.62 | 59400 | 5.4560 | 1.0 |
| 0.0361 | 448.12 | 59600 | 5.5045 | 1.0 |
| 0.0438 | 449.62 | 59800 | 4.8485 | 1.0 |
| 0.0512 | 451.13 | 60000 | 4.9816 | 1.0 |
| 0.0464 | 452.63 | 60200 | 5.2011 | 1.0 |
| 0.0437 | 454.14 | 60400 | 5.6679 | 1.0 |
| 0.0457 | 455.64 | 60600 | 5.7678 | 1.0 |
| 0.0397 | 457.14 | 60800 | 5.6997 | 1.0 |
| 0.0397 | 458.65 | 61000 | 5.2398 | 1.0 |
| 0.0412 | 460.15 | 61200 | 5.3039 | 1.0 |
| 0.0472 | 461.65 | 61400 | 5.9334 | 1.0 |
| 0.0389 | 463.16 | 61600 | 5.6996 | 1.0 |
| 0.043 | 464.66 | 61800 | 5.4419 | 1.0 |
| 0.0407 | 466.17 | 62000 | 5.2868 | 1.0 |
| 0.0511 | 467.67 | 62200 | 5.2794 | 1.0 |
| 0.0456 | 469.17 | 62400 | 5.6698 | 1.0 |
| 0.0462 | 470.68 | 62600 | 5.4807 | 1.0 |
| 0.0402 | 472.18 | 62800 | 4.9668 | 1.0 |
| 0.0459 | 473.68 | 63000 | 5.1433 | 1.0 |
| 0.0416 | 475.19 | 63200 | 5.1973 | 1.0 |
| 0.0468 | 476.69 | 63400 | 4.5775 | 1.0 |
| 0.0382 | 478.2 | 63600 | 5.4247 | 1.0 |
| 0.04 | 479.7 | 63800 | 5.3787 | 1.0 |
| 0.0446 | 481.2 | 64000 | 5.8649 | 1.0 |
| 0.0478 | 482.71 | 64200 | 5.5780 | 1.0 |
| 0.0468 | 484.21 | 64400 | 5.6468 | 1.0 |
| 0.0358 | 485.71 | 64600 | 5.5878 | 1.0 |
| 0.0375 | 487.22 | 64800 | 6.0500 | 1.0 |
| 0.0418 | 488.72 | 65000 | 5.6539 | 1.0 |
| 0.0419 | 490.23 | 65200 | 6.0848 | 1.0 |
| 0.0381 | 491.73 | 65400 | 5.7511 | 1.0 |
| 0.0476 | 493.23 | 65600 | 5.7423 | 1.0 |
| 0.0492 | 494.74 | 65800 | 5.3481 | 1.0 |
| 0.0373 | 496.24 | 66000 | 6.2950 | 1.0 |
| 0.0451 | 497.74 | 66200 | 5.4381 | 1.0 |
| 0.0457 | 499.25 | 66400 | 5.5962 | 1.0 |
| 0.0384 | 500.75 | 66600 | 5.6821 | 1.0 |
| 0.037 | 502.26 | 66800 | 5.8399 | 1.0 |
| 0.0378 | 503.76 | 67000 | 5.4415 | 1.0 |
| 0.038 | 505.26 | 67200 | 5.6578 | 1.0 |
| 0.0371 | 506.77 | 67400 | 5.6192 | 1.0 |
| 0.0314 | 508.27 | 67600 | 6.0258 | 1.0 |
| 0.0345 | 509.77 | 67800 | 6.4183 | 1.0 |
| 0.039 | 511.28 | 68000 | 5.1909 | 1.0 |
| 0.0337 | 512.78 | 68200 | 5.3650 | 1.0 |
| 0.0463 | 514.29 | 68400 | 5.5936 | 1.0 |
| 0.05 | 515.79 | 68600 | 5.6765 | 1.0 |
| 0.0374 | 517.29 | 68800 | 5.3339 | 1.0 |
| 0.0374 | 518.8 | 69000 | 5.7343 | 1.0 |
| 0.0406 | 520.3 | 69200 | 5.6398 | 1.0 |
| 0.0397 | 521.8 | 69400 | 5.4041 | 1.0 |
| 0.0391 | 523.31 | 69600 | 5.8327 | 1.0 |
| 0.0332 | 524.81 | 69800 | 5.6610 | 1.0 |
| 0.0325 | 526.32 | 70000 | 5.4675 | 1.0 |
| 0.0438 | 527.82 | 70200 | 5.4601 | 1.0 |
| 0.0281 | 529.32 | 70400 | 5.3118 | 1.0 |
| 0.0314 | 530.83 | 70600 | 5.6760 | 1.0 |
| 0.039 | 532.33 | 70800 | 5.2432 | 1.0 |
| 0.0293 | 533.83 | 71000 | 5.4250 | 1.0 |
| 0.0334 | 535.34 | 71200 | 4.9681 | 1.0 |
| 0.0341 | 536.84 | 71400 | 5.2753 | 1.0 |
| 0.0368 | 538.35 | 71600 | 4.7242 | 1.0 |
| 0.0339 | 539.85 | 71800 | 5.0283 | 1.0 |
| 0.0285 | 541.35 | 72000 | 4.9008 | 1.0 |
| 0.0323 | 542.86 | 72200 | 5.0817 | 1.0 |
| 0.0391 | 544.36 | 72400 | 5.2062 | 1.0 |
| 0.0337 | 545.86 | 72600 | 5.4544 | 1.0 |
| 0.0352 | 547.37 | 72800 | 5.0674 | 1.0 |
| 0.0419 | 548.87 | 73000 | 5.2037 | 1.0 |
| 0.0314 | 550.38 | 73200 | 4.8665 | 1.0 |
| 0.0324 | 551.88 | 73400 | 5.2775 | 1.0 |
| 0.0329 | 553.38 | 73600 | 4.7535 | 1.0 |
| 0.0304 | 554.89 | 73800 | 4.8733 | 0.9994 |
| 0.0388 | 556.39 | 74000 | 5.2259 | 1.0 |
| 0.0423 | 557.89 | 74200 | 5.4575 | 1.0 |
| 0.034 | 559.4 | 74400 | 4.9118 | 1.0 |
| 0.0289 | 560.9 | 74600 | 4.9018 | 1.0 |
| 0.0317 | 562.41 | 74800 | 5.0265 | 1.0 |
| 0.0346 | 563.91 | 75000 | 4.7586 | 1.0 |
| 0.0238 | 565.41 | 75200 | 5.0713 | 1.0 |
| 0.0275 | 566.92 | 75400 | 4.8972 | 1.0 |
| 0.0291 | 568.42 | 75600 | 4.8198 | 1.0 |
| 0.0371 | 569.92 | 75800 | 4.3204 | 1.0 |
| 0.033 | 571.43 | 76000 | 4.5274 | 1.0 |
| 0.0334 | 572.93 | 76200 | 4.8907 | 1.0 |
| 0.0375 | 574.44 | 76400 | 4.6204 | 1.0 |
| 0.0332 | 575.94 | 76600 | 4.4255 | 1.0 |
| 0.0359 | 577.44 | 76800 | 4.8451 | 1.0 |
| 0.0273 | 578.95 | 77000 | 5.8480 | 1.0 |
| 0.0273 | 580.45 | 77200 | 5.2345 | 1.0 |
| 0.0296 | 581.95 | 77400 | 5.3281 | 0.9994 |
| 0.0332 | 583.46 | 77600 | 5.6027 | 0.9994 |
| 0.0251 | 584.96 | 77800 | 5.3038 | 0.9994 |
| 0.0275 | 586.47 | 78000 | 5.0464 | 1.0 |
| 0.0193 | 587.97 | 78200 | 4.9974 | 0.9994 |
| 0.0326 | 589.47 | 78400 | 4.6220 | 1.0 |
| 0.033 | 590.98 | 78600 | 4.9772 | 1.0 |
| 0.0258 | 592.48 | 78800 | 4.6183 | 1.0 |
| 0.0326 | 593.98 | 79000 | 5.2420 | 1.0 |
| 0.0311 | 595.49 | 79200 | 4.6937 | 1.0 |
| 0.0257 | 596.99 | 79400 | 4.9505 | 1.0 |
| 0.0295 | 598.5 | 79600 | 5.0226 | 1.0 |
| 0.0309 | 600.0 | 79800 | 4.8904 | 1.0 |
| 0.0239 | 601.5 | 80000 | 5.4780 | 1.0 |
| 0.0238 | 603.01 | 80200 | 5.4525 | 1.0 |
| 0.0373 | 604.51 | 80400 | 4.8573 | 0.9994 |
| 0.0308 | 606.02 | 80600 | 5.1780 | 1.0 |
| 0.0262 | 607.52 | 80800 | 5.4216 | 1.0 |
| 0.0227 | 609.02 | 81000 | 4.9209 | 1.0 |
| 0.0222 | 610.53 | 81200 | 5.4042 | 1.0 |
| 0.0332 | 612.03 | 81400 | 5.2453 | 1.0 |
| 0.0261 | 613.53 | 81600 | 5.3043 | 1.0 |
| 0.0254 | 615.04 | 81800 | 5.7649 | 1.0 |
| 0.0276 | 616.54 | 82000 | 5.1942 | 1.0 |
| 0.0291 | 618.05 | 82200 | 4.9448 | 0.9994 |
| 0.0275 | 619.55 | 82400 | 5.0157 | 1.0006 |
| 0.0278 | 621.05 | 82600 | 4.7635 | 1.0006 |
| 0.0232 | 622.56 | 82800 | 5.4406 | 1.0 |
| 0.0311 | 624.06 | 83000 | 5.3966 | 1.0 |
| 0.0206 | 625.56 | 83200 | 4.8876 | 1.0013 |
| 0.0274 | 627.07 | 83400 | 5.4422 | 1.0 |
| 0.0199 | 628.57 | 83600 | 4.9966 | 1.0006 |
| 0.0253 | 630.08 | 83800 | 5.1257 | 1.0006 |
| 0.027 | 631.58 | 84000 | 5.1748 | 1.0 |
| 0.0312 | 633.08 | 84200 | 5.0923 | 1.0006 |
| 0.0288 | 634.59 | 84400 | 5.1963 | 1.0 |
| 0.0261 | 636.09 | 84600 | 4.8105 | 1.0013 |
| 0.0259 | 637.59 | 84800 | 4.9715 | 1.0 |
| 0.03 | 639.1 | 85000 | 5.2808 | 1.0 |
| 0.0215 | 640.6 | 85200 | 5.3155 | 1.0 |
| 0.028 | 642.11 | 85400 | 5.1311 | 1.0 |
| 0.0268 | 643.61 | 85600 | 5.1429 | 1.0 |
| 0.0238 | 645.11 | 85800 | 5.0456 | 1.0 |
| 0.0273 | 646.62 | 86000 | 5.2772 | 1.0 |
| 0.0196 | 648.12 | 86200 | 5.2186 | 1.0 |
| 0.0212 | 649.62 | 86400 | 4.8386 | 0.9994 |
| 0.0243 | 651.13 | 86600 | 5.1898 | 1.0 |
| 0.0255 | 652.63 | 86800 | 5.5885 | 1.0 |
| 0.0192 | 654.14 | 87000 | 5.7636 | 1.0 |
| 0.0272 | 655.64 | 87200 | 5.5897 | 1.0 |
| 0.0254 | 657.14 | 87400 | 5.3412 | 1.0 |
| 0.0389 | 658.65 | 87600 | 5.2754 | 1.0 |
| 0.025 | 660.15 | 87800 | 4.6929 | 1.0 |
| 0.0268 | 661.65 | 88000 | 4.7299 | 1.0 |
| 0.0303 | 663.16 | 88200 | 5.1038 | 1.0 |
| 0.019 | 664.66 | 88400 | 5.4165 | 1.0 |
| 0.0197 | 666.17 | 88600 | 5.3144 | 1.0 |
| 0.024 | 667.67 | 88800 | 4.7525 | 1.0 |
| 0.0272 | 669.17 | 89000 | 5.4154 | 1.0 |
| 0.0267 | 670.68 | 89200 | 5.1149 | 0.9994 |
| 0.0231 | 672.18 | 89400 | 5.3135 | 1.0 |
| 0.034 | 673.68 | 89600 | 4.8940 | 1.0 |
| 0.0205 | 675.19 | 89800 | 5.4456 | 1.0 |
| 0.0236 | 676.69 | 90000 | 5.1145 | 1.0 |
| 0.0276 | 678.2 | 90200 | 5.3905 | 1.0 |
| 0.0316 | 679.7 | 90400 | 5.4222 | 1.0 |
| 0.0253 | 681.2 | 90600 | 5.1456 | 1.0006 |
| 0.0255 | 682.71 | 90800 | 5.3852 | 1.0 |
| 0.0185 | 684.21 | 91000 | 4.9756 | 1.0 |
| 0.019 | 685.71 | 91200 | 5.9086 | 1.0 |
| 0.0232 | 687.22 | 91400 | 5.1278 | 1.0 |
| 0.0281 | 688.72 | 91600 | 4.9946 | 1.0 |
| 0.0188 | 690.23 | 91800 | 5.0910 | 1.0 |
| 0.0189 | 691.73 | 92000 | 4.8426 | 1.0 |
| 0.0276 | 693.23 | 92200 | 5.0411 | 1.0 |
| 0.021 | 694.74 | 92400 | 4.8377 | 1.0 |
| 0.0237 | 696.24 | 92600 | 4.9047 | 1.0 |
| 0.0209 | 697.74 | 92800 | 4.3727 | 1.0 |
| 0.0212 | 699.25 | 93000 | 4.4921 | 1.0006 |
| 0.0228 | 700.75 | 93200 | 4.7233 | 1.0006 |
| 0.0187 | 702.26 | 93400 | 4.7441 | 1.0006 |
| 0.0217 | 703.76 | 93600 | 4.7464 | 1.0 |
| 0.0254 | 705.26 | 93800 | 4.8955 | 0.9994 |
| 0.0268 | 706.77 | 94000 | 5.2548 | 1.0 |
| 0.0236 | 708.27 | 94200 | 5.2691 | 1.0 |
| 0.0174 | 709.77 | 94400 | 4.8898 | 1.0 |
| 0.0262 | 711.28 | 94600 | 4.7713 | 1.0 |
| 0.0226 | 712.78 | 94800 | 4.8464 | 1.0 |
| 0.0193 | 714.29 | 95000 | 5.0019 | 1.0 |
| 0.0229 | 715.79 | 95200 | 5.2043 | 1.0 |
| 0.014 | 717.29 | 95400 | 4.9124 | 0.9994 |
| 0.0274 | 718.8 | 95600 | 4.9160 | 1.0 |
| 0.0198 | 720.3 | 95800 | 5.1003 | 1.0 |
| 0.0222 | 721.8 | 96000 | 4.9865 | 1.0 |
| 0.0133 | 723.31 | 96200 | 5.0203 | 1.0 |
| 0.0162 | 724.81 | 96400 | 4.9140 | 1.0 |
| 0.024 | 726.32 | 96600 | 4.9667 | 1.0006 |
| 0.0197 | 727.82 | 96800 | 5.1605 | 1.0 |
| 0.0169 | 729.32 | 97000 | 5.4196 | 1.0 |
| 0.0238 | 730.83 | 97200 | 5.3665 | 1.0 |
| 0.0182 | 732.33 | 97400 | 5.0020 | 1.0013 |
| 0.0212 | 733.83 | 97600 | 5.1674 | 1.0006 |
| 0.0165 | 735.34 | 97800 | 4.9132 | 1.0013 |
| 0.0242 | 736.84 | 98000 | 4.7047 | 1.0013 |
| 0.022 | 738.35 | 98200 | 5.1006 | 1.0 |
| 0.022 | 739.85 | 98400 | 4.7582 | 0.9994 |
| 0.0178 | 741.35 | 98600 | 5.0911 | 1.0 |
| 0.0197 | 742.86 | 98800 | 4.9315 | 1.0 |
| 0.022 | 744.36 | 99000 | 4.4003 | 1.0019 |
| 0.0114 | 745.86 | 99200 | 4.9068 | 1.0 |
| 0.0184 | 747.37 | 99400 | 4.7211 | 1.0 |
| 0.0227 | 748.87 | 99600 | 4.8788 | 1.0 |
| 0.0127 | 750.38 | 99800 | 5.0337 | 1.0 |
| 0.0193 | 751.88 | 100000 | 5.0193 | 1.0 |
| 0.0167 | 753.38 | 100200 | 5.1066 | 1.0019 |
| 0.0149 | 754.89 | 100400 | 5.2865 | 1.0 |
| 0.0219 | 756.39 | 100600 | 5.2765 | 1.0 |
| 0.0185 | 757.89 | 100800 | 5.3224 | 1.0 |
| 0.0159 | 759.4 | 101000 | 5.1665 | 1.0 |
| 0.0178 | 760.9 | 101200 | 5.4050 | 0.9994 |
| 0.0119 | 762.41 | 101400 | 5.1887 | 0.9994 |
| 0.022 | 763.91 | 101600 | 4.7725 | 1.0 |
| 0.0214 | 765.41 | 101800 | 4.9794 | 1.0 |
| 0.0151 | 766.92 | 102000 | 4.7806 | 1.0013 |
| 0.0181 | 768.42 | 102200 | 4.3024 | 1.0006 |
| 0.0166 | 769.92 | 102400 | 4.8876 | 1.0 |
| 0.0211 | 771.43 | 102600 | 4.5744 | 1.0 |
| 0.0184 | 772.93 | 102800 | 4.8648 | 0.9994 |
| 0.02 | 774.44 | 103000 | 4.7831 | 0.9994 |
| 0.0188 | 775.94 | 103200 | 4.9471 | 0.9994 |
| 0.0201 | 777.44 | 103400 | 4.9495 | 1.0006 |
| 0.0169 | 778.95 | 103600 | 5.2604 | 1.0 |
| 0.0145 | 780.45 | 103800 | 5.5133 | 1.0 |
| 0.0176 | 781.95 | 104000 | 5.1844 | 0.9994 |
| 0.0149 | 783.46 | 104200 | 4.6289 | 1.0 |
| 0.0181 | 784.96 | 104400 | 5.2480 | 1.0 |
| 0.0132 | 786.47 | 104600 | 4.7374 | 0.9994 |
| 0.0115 | 787.97 | 104800 | 5.6450 | 1.0 |
| 0.014 | 789.47 | 105000 | 5.0672 | 1.0 |
| 0.0181 | 790.98 | 105200 | 5.1088 | 1.0 |
| 0.0181 | 792.48 | 105400 | 5.0798 | 1.0 |
| 0.0146 | 793.98 | 105600 | 5.1835 | 1.0 |
| 0.0219 | 795.49 | 105800 | 5.3850 | 1.0 |
| 0.0222 | 796.99 | 106000 | 4.9615 | 1.0 |
| 0.0176 | 798.5 | 106200 | 4.6814 | 1.0 |
| 0.017 | 800.0 | 106400 | 4.9264 | 1.0 |
| 0.0134 | 801.5 | 106600 | 5.0058 | 1.0 |
| 0.0139 | 803.01 | 106800 | 5.1207 | 1.0 |
| 0.0146 | 804.51 | 107000 | 4.8640 | 1.0006 |
| 0.018 | 806.02 | 107200 | 4.6173 | 1.0 |
| 0.0183 | 807.52 | 107400 | 4.8846 | 1.0 |
| 0.0169 | 809.02 | 107600 | 5.2157 | 1.0006 |
| 0.0128 | 810.53 | 107800 | 5.1951 | 1.0006 |
| 0.0181 | 812.03 | 108000 | 4.9453 | 1.0013 |
| 0.0107 | 813.53 | 108200 | 5.0670 | 1.0013 |
| 0.0171 | 815.04 | 108400 | 4.8881 | 1.0006 |
| 0.0144 | 816.54 | 108600 | 4.8071 | 1.0013 |
| 0.0133 | 818.05 | 108800 | 4.9399 | 1.0013 |
| 0.0138 | 819.55 | 109000 | 4.5266 | 1.0038 |
| 0.0159 | 821.05 | 109200 | 4.9887 | 1.0025 |
| 0.0157 | 822.56 | 109400 | 4.8344 | 1.0019 |
| 0.0134 | 824.06 | 109600 | 5.1275 | 1.0019 |
| 0.0142 | 825.56 | 109800 | 4.7311 | 1.0025 |
| 0.0207 | 827.07 | 110000 | 4.5203 | 1.0050 |
| 0.0125 | 828.57 | 110200 | 4.5815 | 1.0044 |
| 0.0161 | 830.08 | 110400 | 4.7650 | 1.0031 |
| 0.0091 | 831.58 | 110600 | 4.9839 | 1.0025 |
| 0.0154 | 833.08 | 110800 | 4.9781 | 1.0013 |
| 0.012 | 834.59 | 111000 | 5.0515 | 1.0025 |
| 0.0182 | 836.09 | 111200 | 5.1645 | 1.0025 |
| 0.0135 | 837.59 | 111400 | 5.1050 | 1.0013 |
| 0.0171 | 839.1 | 111600 | 4.8125 | 1.0025 |
| 0.0152 | 840.6 | 111800 | 5.4213 | 1.0038 |
| 0.0103 | 842.11 | 112000 | 5.0478 | 1.0031 |
| 0.0112 | 843.61 | 112200 | 4.9343 | 1.0019 |
| 0.0156 | 845.11 | 112400 | 5.2666 | 1.0019 |
| 0.0122 | 846.62 | 112600 | 5.0991 | 1.0031 |
| 0.0178 | 848.12 | 112800 | 5.1477 | 1.0006 |
| 0.0174 | 849.62 | 113000 | 5.1716 | 1.0031 |
| 0.0136 | 851.13 | 113200 | 5.3554 | 1.0013 |
| 0.0123 | 852.63 | 113400 | 4.8209 | 1.0019 |
| 0.0095 | 854.14 | 113600 | 4.9493 | 1.0019 |
| 0.0183 | 855.64 | 113800 | 4.8060 | 1.0038 |
| 0.0111 | 857.14 | 114000 | 4.8071 | 1.0006 |
| 0.0121 | 858.65 | 114200 | 4.7184 | 1.0006 |
| 0.0079 | 860.15 | 114400 | 4.7966 | 1.0019 |
| 0.0129 | 861.65 | 114600 | 4.8338 | 1.0013 |
| 0.0103 | 863.16 | 114800 | 4.5879 | 1.0019 |
| 0.0117 | 864.66 | 115000 | 4.7938 | 1.0025 |
| 0.018 | 866.17 | 115200 | 4.6562 | 1.0 |
| 0.0121 | 867.67 | 115400 | 4.6558 | 1.0 |
| 0.0184 | 869.17 | 115600 | 4.7076 | 1.0 |
| 0.0135 | 870.68 | 115800 | 5.1430 | 1.0 |
| 0.0163 | 872.18 | 116000 | 5.2987 | 1.0019 |
| 0.014 | 873.68 | 116200 | 5.0680 | 1.0019 |
| 0.0114 | 875.19 | 116400 | 4.9600 | 1.0013 |
| 0.0145 | 876.69 | 116600 | 5.0292 | 1.0025 |
| 0.0125 | 878.2 | 116800 | 4.6149 | 1.0038 |
| 0.008 | 879.7 | 117000 | 4.9819 | 1.0019 |
| 0.0122 | 881.2 | 117200 | 5.0868 | 1.0019 |
| 0.0157 | 882.71 | 117400 | 4.8522 | 1.0031 |
| 0.0146 | 884.21 | 117600 | 4.6680 | 1.0044 |
| 0.014 | 885.71 | 117800 | 4.4612 | 1.0038 |
| 0.0123 | 887.22 | 118000 | 4.4058 | 1.0044 |
| 0.0122 | 888.72 | 118200 | 4.6614 | 1.0038 |
| 0.012 | 890.23 | 118400 | 4.7230 | 1.0019 |
| 0.0159 | 891.73 | 118600 | 5.0094 | 1.0006 |
| 0.0087 | 893.23 | 118800 | 4.8522 | 1.0006 |
| 0.013 | 894.74 | 119000 | 4.9456 | 1.0013 |
| 0.0166 | 896.24 | 119200 | 4.9068 | 1.0019 |
| 0.0129 | 897.74 | 119400 | 4.9066 | 1.0019 |
| 0.0136 | 899.25 | 119600 | 4.6458 | 1.0019 |
| 0.0208 | 900.75 | 119800 | 4.6729 | 1.0013 |
| 0.0158 | 902.26 | 120000 | 4.6674 | 1.0019 |
| 0.0125 | 903.76 | 120200 | 4.5242 | 1.0025 |
| 0.0098 | 905.26 | 120400 | 4.7753 | 1.0025 |
| 0.0078 | 906.77 | 120600 | 4.8435 | 1.0013 |
| 0.0155 | 908.27 | 120800 | 4.6419 | 1.0006 |
| 0.0142 | 909.77 | 121000 | 4.7051 | 1.0013 |
| 0.0098 | 911.28 | 121200 | 5.0023 | 1.0 |
| 0.0097 | 912.78 | 121400 | 5.2775 | 1.0 |
| 0.0103 | 914.29 | 121600 | 5.1593 | 1.0 |
| 0.0115 | 915.79 | 121800 | 5.0693 | 1.0 |
| 0.0129 | 917.29 | 122000 | 5.4565 | 1.0 |
| 0.0123 | 918.8 | 122200 | 4.9698 | 1.0 |
| 0.0176 | 920.3 | 122400 | 4.8110 | 1.0019 |
| 0.0106 | 921.8 | 122600 | 4.7044 | 1.0031 |
| 0.0098 | 923.31 | 122800 | 4.7205 | 1.0031 |
| 0.0132 | 924.81 | 123000 | 4.5947 | 1.0013 |
| 0.0122 | 926.32 | 123200 | 4.9378 | 1.0 |
| 0.0101 | 927.82 | 123400 | 5.0470 | 1.0006 |
| 0.0151 | 929.32 | 123600 | 4.7670 | 1.0 |
| 0.0089 | 930.83 | 123800 | 4.9127 | 1.0019 |
| 0.0107 | 932.33 | 124000 | 4.9719 | 1.0031 |
| 0.0084 | 933.83 | 124200 | 5.0971 | 1.0025 |
| 0.0111 | 935.34 | 124400 | 4.6700 | 1.0031 |
| 0.0121 | 936.84 | 124600 | 4.7236 | 1.0031 |
| 0.0104 | 938.35 | 124800 | 4.7380 | 1.0019 |
| 0.0101 | 939.85 | 125000 | 4.8754 | 1.0025 |
| 0.0145 | 941.35 | 125200 | 5.0708 | 1.0031 |
| 0.0096 | 942.86 | 125400 | 4.8973 | 1.0031 |
| 0.0158 | 944.36 | 125600 | 5.1670 | 1.0019 |
| 0.0106 | 945.86 | 125800 | 5.0989 | 1.0044 |
| 0.0157 | 947.37 | 126000 | 5.0135 | 1.0044 |
| 0.0092 | 948.87 | 126200 | 4.7069 | 1.0025 |
| 0.0121 | 950.38 | 126400 | 4.6865 | 1.0031 |
| 0.0117 | 951.88 | 126600 | 4.6496 | 1.0050 |
| 0.0107 | 953.38 | 126800 | 5.0900 | 1.0025 |
| 0.0107 | 954.89 | 127000 | 4.8722 | 1.0025 |
| 0.0137 | 956.39 | 127200 | 4.7971 | 1.0013 |
| 0.0106 | 957.89 | 127400 | 5.0268 | 1.0025 |
| 0.0138 | 959.4 | 127600 | 5.1737 | 1.0019 |
| 0.01 | 960.9 | 127800 | 5.2657 | 1.0025 |
| 0.0101 | 962.41 | 128000 | 5.0265 | 1.0019 |
| 0.0092 | 963.91 | 128200 | 5.4657 | 1.0019 |
| 0.0109 | 965.41 | 128400 | 5.5858 | 1.0019 |
| 0.0078 | 966.92 | 128600 | 5.2281 | 1.0031 |
| 0.0111 | 968.42 | 128800 | 4.9028 | 1.0013 |
| 0.0068 | 969.92 | 129000 | 4.8840 | 1.0019 |
| 0.008 | 971.43 | 129200 | 5.3572 | 1.0013 |
| 0.0108 | 972.93 | 129400 | 5.0857 | 1.0025 |
| 0.0128 | 974.44 | 129600 | 5.1988 | 1.0019 |
| 0.0088 | 975.94 | 129800 | 4.9510 | 1.0025 |
| 0.013 | 977.44 | 130000 | 5.3014 | 1.0019 |
| 0.0108 | 978.95 | 130200 | 4.9356 | 1.0019 |
| 0.0112 | 980.45 | 130400 | 4.8090 | 1.0025 |
| 0.0115 | 981.95 | 130600 | 5.0231 | 1.0038 |
| 0.0166 | 983.46 | 130800 | 4.8263 | 1.0019 |
| 0.0061 | 984.96 | 131000 | 4.7797 | 1.0025 |
| 0.0097 | 986.47 | 131200 | 4.8421 | 1.0019 |
| 0.0115 | 987.97 | 131400 | 4.9492 | 1.0031 |
| 0.0058 | 989.47 | 131600 | 4.9278 | 1.0013 |
| 0.0089 | 990.98 | 131800 | 4.8451 | 1.0013 |
| 0.0133 | 992.48 | 132000 | 4.8694 | 1.0013 |
| 0.0074 | 993.98 | 132200 | 5.0419 | 1.0006 |
| 0.0088 | 995.49 | 132400 | 5.2701 | 1.0013 |
| 0.0116 | 996.99 | 132600 | 5.2893 | 1.0006 |
| 0.0068 | 998.5 | 132800 | 4.9237 | 1.0019 |
| 0.0096 | 1000.0 | 133000 | 5.0376 | 1.0013 |
| 0.0097 | 1001.5 | 133200 | 5.0571 | 1.0025 |
| 0.009 | 1003.01 | 133400 | 5.0585 | 1.0019 |
| 0.0077 | 1004.51 | 133600 | 4.9175 | 1.0013 |
| 0.0099 | 1006.02 | 133800 | 5.0497 | 1.0013 |
| 0.0042 | 1007.52 | 134000 | 4.9861 | 1.0013 |
| 0.0114 | 1009.02 | 134200 | 5.0604 | 1.0 |
| 0.0079 | 1010.53 | 134400 | 4.9738 | 1.0 |
| 0.0039 | 1012.03 | 134600 | 5.2350 | 1.0006 |
| 0.0081 | 1013.53 | 134800 | 4.8783 | 1.0006 |
| 0.0072 | 1015.04 | 135000 | 5.0616 | 1.0019 |
| 0.0059 | 1016.54 | 135200 | 4.9421 | 1.0006 |
| 0.0052 | 1018.05 | 135400 | 5.2076 | 1.0006 |
| 0.0072 | 1019.55 | 135600 | 4.7459 | 1.0019 |
| 0.0137 | 1021.05 | 135800 | 4.4156 | 1.0025 |
| 0.0086 | 1022.56 | 136000 | 4.7582 | 1.0038 |
| 0.006 | 1024.06 | 136200 | 4.6716 | 1.0013 |
| 0.0086 | 1025.56 | 136400 | 4.9067 | 1.0006 |
| 0.0074 | 1027.07 | 136600 | 5.1224 | 1.0 |
| 0.0099 | 1028.57 | 136800 | 4.8046 | 1.0006 |
| 0.0129 | 1030.08 | 137000 | 5.0482 | 1.0 |
| 0.011 | 1031.58 | 137200 | 4.9666 | 1.0019 |
| 0.0071 | 1033.08 | 137400 | 5.0899 | 1.0013 |
| 0.0067 | 1034.59 | 137600 | 5.0229 | 1.0006 |
| 0.0044 | 1036.09 | 137800 | 5.0371 | 1.0013 |
| 0.0101 | 1037.59 | 138000 | 4.9919 | 0.9994 |
| 0.006 | 1039.1 | 138200 | 5.1368 | 0.9994 |
| 0.0105 | 1040.6 | 138400 | 4.8836 | 0.9987 |
| 0.0084 | 1042.11 | 138600 | 4.7600 | 1.0025 |
| 0.0089 | 1043.61 | 138800 | 4.9488 | 1.0019 |
| 0.0075 | 1045.11 | 139000 | 5.0581 | 1.0006 |
| 0.0109 | 1046.62 | 139200 | 5.1216 | 1.0006 |
| 0.0028 | 1048.12 | 139400 | 5.0811 | 0.9981 |
| 0.0044 | 1049.62 | 139600 | 4.8405 | 1.0013 |
| 0.0067 | 1051.13 | 139800 | 5.0062 | 1.0013 |
| 0.0057 | 1052.63 | 140000 | 5.0492 | 1.0006 |
| 0.0062 | 1054.14 | 140200 | 5.0783 | 1.0 |
| 0.0076 | 1055.64 | 140400 | 5.1404 | 1.0 |
| 0.0132 | 1057.14 | 140600 | 5.1925 | 1.0006 |
| 0.0043 | 1058.65 | 140800 | 5.0225 | 1.0019 |
| 0.0032 | 1060.15 | 141000 | 4.9694 | 1.0019 |
| 0.004 | 1061.65 | 141200 | 5.1353 | 1.0006 |
| 0.0092 | 1063.16 | 141400 | 5.2068 | 1.0013 |
| 0.0092 | 1064.66 | 141600 | 5.0825 | 1.0013 |
| 0.0057 | 1066.17 | 141800 | 5.0087 | 1.0013 |
| 0.0048 | 1067.67 | 142000 | 4.9626 | 1.0025 |
| 0.0052 | 1069.17 | 142200 | 5.0154 | 1.0006 |
| 0.0094 | 1070.68 | 142400 | 5.0147 | 1.0019 |
| 0.008 | 1072.18 | 142600 | 4.9181 | 1.0006 |
| 0.0073 | 1073.68 | 142800 | 4.9787 | 1.0019 |
| 0.0063 | 1075.19 | 143000 | 4.9458 | 1.0025 |
| 0.0069 | 1076.69 | 143200 | 5.0951 | 1.0019 |
| 0.005 | 1078.2 | 143400 | 4.8268 | 1.0019 |
| 0.008 | 1079.7 | 143600 | 4.8822 | 1.0019 |
| 0.0047 | 1081.2 | 143800 | 5.1148 | 1.0019 |
| 0.0138 | 1082.71 | 144000 | 4.9464 | 1.0 |
| 0.0054 | 1084.21 | 144200 | 4.9410 | 1.0013 |
| 0.0061 | 1085.71 | 144400 | 5.1612 | 1.0006 |
| 0.0095 | 1087.22 | 144600 | 5.0962 | 1.0006 |
| 0.0069 | 1088.72 | 144800 | 5.0092 | 1.0 |
| 0.0041 | 1090.23 | 145000 | 5.0815 | 1.0 |
| 0.0104 | 1091.73 | 145200 | 5.0631 | 1.0006 |
| 0.0025 | 1093.23 | 145400 | 5.1077 | 1.0006 |
| 0.0068 | 1094.74 | 145600 | 4.9715 | 1.0019 |
| 0.006 | 1096.24 | 145800 | 5.0608 | 1.0013 |
| 0.0081 | 1097.74 | 146000 | 5.1091 | 1.0013 |
| 0.0068 | 1099.25 | 146200 | 5.1470 | 1.0013 |
| 0.0047 | 1100.75 | 146400 | 5.1790 | 1.0006 |
| 0.0054 | 1102.26 | 146600 | 5.2646 | 1.0 |
| 0.0092 | 1103.76 | 146800 | 5.2853 | 1.0013 |
| 0.0066 | 1105.26 | 147000 | 5.0318 | 1.0013 |
| 0.008 | 1106.77 | 147200 | 5.0337 | 1.0013 |
| 0.0034 | 1108.27 | 147400 | 5.0388 | 1.0013 |
| 0.011 | 1109.77 | 147600 | 5.0256 | 1.0019 |
| 0.0072 | 1111.28 | 147800 | 5.0236 | 1.0019 |
| 0.0129 | 1112.78 | 148000 | 5.0792 | 1.0019 |
| 0.0063 | 1114.29 | 148200 | 5.1229 | 1.0 |
| 0.0045 | 1115.79 | 148400 | 5.1730 | 1.0013 |
| 0.0049 | 1117.29 | 148600 | 5.1491 | 1.0006 |
| 0.0039 | 1118.8 | 148800 | 5.1083 | 1.0013 |
| 0.008 | 1120.3 | 149000 | 5.0877 | 1.0019 |
| 0.0058 | 1121.8 | 149200 | 5.0018 | 1.0019 |
| 0.0046 | 1123.31 | 149400 | 5.0775 | 1.0013 |
| 0.0057 | 1124.81 | 149600 | 5.0081 | 1.0006 |
| 0.0048 | 1126.32 | 149800 | 5.0912 | 1.0013 |
| 0.0031 | 1127.82 | 150000 | 5.0987 | 1.0019 |
| 0.0059 | 1129.32 | 150200 | 5.0811 | 1.0013 |
| 0.0043 | 1130.83 | 150400 | 5.1520 | 1.0019 |
| 0.0071 | 1132.33 | 150600 | 5.2432 | 1.0019 |
| 0.0115 | 1133.83 | 150800 | 5.1358 | 1.0019 |
| 0.0061 | 1135.34 | 151000 | 5.0458 | 1.0019 |
| 0.0068 | 1136.84 | 151200 | 5.1654 | 1.0019 |
| 0.0107 | 1138.35 | 151400 | 5.0748 | 1.0019 |
| 0.0053 | 1139.85 | 151600 | 5.1501 | 1.0019 |
| 0.0058 | 1141.35 | 151800 | 5.2146 | 1.0019 |
| 0.0053 | 1142.86 | 152000 | 5.2234 | 1.0013 |
| 0.0034 | 1144.36 | 152200 | 5.2226 | 1.0019 |
| 0.0058 | 1145.86 | 152400 | 5.2033 | 1.0019 |
| 0.0039 | 1147.37 | 152600 | 5.2151 | 1.0019 |
| 0.0111 | 1148.87 | 152800 | 5.1126 | 1.0019 |
| 0.0054 | 1150.38 | 153000 | 5.2183 | 1.0019 |
| 0.0068 | 1151.88 | 153200 | 5.2718 | 1.0019 |
| 0.0074 | 1153.38 | 153400 | 5.1996 | 1.0019 |
| 0.0064 | 1154.89 | 153600 | 5.1910 | 1.0019 |
| 0.0058 | 1156.39 | 153800 | 5.1924 | 1.0019 |
| 0.004 | 1157.89 | 154000 | 5.2096 | 1.0019 |
| 0.0046 | 1159.4 | 154200 | 5.1493 | 1.0019 |
| 0.0066 | 1160.9 | 154400 | 5.1710 | 1.0019 |
| 0.0055 | 1162.41 | 154600 | 5.1339 | 1.0019 |
| 0.0076 | 1163.91 | 154800 | 5.1114 | 1.0019 |
| 0.0078 | 1165.41 | 155000 | 5.1069 | 1.0019 |
| 0.0039 | 1166.92 | 155200 | 5.1307 | 1.0019 |
| 0.0029 | 1168.42 | 155400 | 5.1418 | 1.0025 |
| 0.0033 | 1169.92 | 155600 | 5.0614 | 1.0025 |
| 0.005 | 1171.43 | 155800 | 5.0568 | 1.0025 |
| 0.0048 | 1172.93 | 156000 | 5.0617 | 1.0025 |
| 0.005 | 1174.44 | 156200 | 5.0957 | 1.0025 |
| 0.0072 | 1175.94 | 156400 | 5.0974 | 1.0025 |
| 0.0052 | 1177.44 | 156600 | 5.0740 | 1.0025 |
| 0.0042 | 1178.95 | 156800 | 5.0641 | 1.0019 |
| 0.0057 | 1180.45 | 157000 | 5.0398 | 1.0013 |
| 0.0063 | 1181.95 | 157200 | 4.9869 | 1.0019 |
| 0.0046 | 1183.46 | 157400 | 5.0102 | 1.0019 |
| 0.005 | 1184.96 | 157600 | 4.9957 | 1.0019 |
| 0.0044 | 1186.47 | 157800 | 5.0196 | 1.0019 |
| 0.0041 | 1187.97 | 158000 | 5.0451 | 1.0019 |
| 0.0053 | 1189.47 | 158200 | 5.0466 | 1.0019 |
| 0.0078 | 1190.98 | 158400 | 5.0443 | 1.0019 |
| 0.0061 | 1192.48 | 158600 | 5.0306 | 1.0019 |
| 0.0069 | 1193.98 | 158800 | 5.0399 | 1.0019 |
| 0.0114 | 1195.49 | 159000 | 5.0391 | 1.0019 |
| 0.0126 | 1196.99 | 159200 | 5.0403 | 1.0019 |
| 0.0067 | 1198.5 | 159400 | 5.0430 | 1.0019 |
| 0.0044 | 1200.0 | 159600 | 5.0428 | 1.0019 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.11.6
| [
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markn/adalm-bio-small | 22824f560e494e077e6e898a5c22497d3931e521 | 2022-04-12T21:12:35.000Z | [
"pytorch",
"bert",
"transformers",
"license:mit"
] | null | false | markn | null | markn/adalm-bio-small | 0 | null | transformers | ---
license: mit
---
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huggan/fastgan-few-shot-aurora | 8234db31eec47eb4720a814f42455ed30b89b6c0 | 2022-05-06T22:30:43.000Z | [
"pytorch",
"dataset:huggan/few-shot-aurora",
"arxiv:2101.04775",
"huggan",
"gan",
"unconditional-image-generation",
"license:mit"
] | unconditional-image-generation | false | huggan | null | huggan/fastgan-few-shot-aurora | 0 | null | null | ---
tags:
- huggan
- gan
- unconditional-image-generation
datasets:
- huggan/few-shot-aurora
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# Generate aurora image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets.
This model was trained on a dataset of 272 high-quality images of aurora.
#### How to use
```python
# Clone this model
git clone https://huggingface.co/huggan/fastgan-few-shot-aurora/
def load_generator(model_name_or_path):
generator = Generator(in_channels=256, out_channels=3)
generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3)
_ = generator.eval()
return generator
def _denormalize(input: torch.Tensor) -> torch.Tensor:
return (input * 127.5) + 127.5
# Load generator
generator = load_generator("huggan/fastgan-few-shot-aurora")
# Generate a random noise image
noise = torch.zeros(1, 256, 1, 1, device=device).normal_(0.0, 1.0)
with torch.no_grad():
gan_images, _ = generator(noise)
gan_images = _denormalize(gan_images.detach())
save_image(gan_images, "sample.png", nrow=1, normalize=True)
```
#### Limitations and bias
* Converge faster and better with small datasets (less than 1000 samples)
## Training data
[few-shot-aurora](https://huggingface.co/datasets/huggan/few-shot-aurora)
## Generated Images
### BibTeX entry and citation info
```bibtex
@article{FastGAN,
title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis},
author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal},
journal={ICLR},
year={2021}
}
``` | [
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mimicheng/codeparrot-ds-sample-1ep-12apr | 74d9d6d5c5bed469b64e79b2909849c6d58a4044 | 2022-04-13T07:16:11.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | mimicheng | null | mimicheng/codeparrot-ds-sample-1ep-12apr | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds-sample-1ep-12apr
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. -->
# codeparrot-ds-sample-1ep-12apr
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9947
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: tpu
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8723 | 0.37 | 1000 | 2.5340 |
| 2.1776 | 0.74 | 2000 | 1.9947 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
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huggingtweets/radfemman | 8e54c05acd96a0f147b5c263888801474421829f | 2022-04-13T06:22:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/radfemman | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/radfemman/1649830938917/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/1428572680882688005/rqGxWIRJ_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">Radfem Ally 🇺🇸</div>
<div style="text-align: center; font-size: 14px;">@radfemman</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 Radfem Ally 🇺🇸.
| Data | Radfem Ally 🇺🇸 |
| --- | --- |
| Tweets downloaded | 227 |
| Retweets | 33 |
| Short tweets | 14 |
| Tweets kept | 180 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29ku9tl5/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 @radfemman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33qza7xp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33qza7xp/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/radfemman')
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)
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huggan/pix2pix-maps | 29b247f1f7559f1667e3c516666c631ebfd785a1 | 2022-04-13T16:25:52.000Z | [
"pytorch",
"dataset:huggan/maps",
"arxiv:1611.07004",
"huggan",
"gan",
"license:mit"
] | null | false | huggan | null | huggan/pix2pix-maps | 0 | null | null | ---
tags:
- huggan
- gan
datasets:
- huggan/maps
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# Pix2Pix trained on the maps dataset
## Model description
This model is a [Pix2Pix](https://arxiv.org/abs/1611.07004) model trained on the [huggan/maps](https://huggingface.co/datasets/huggan/maps) dataset. The goal for the model is to turn a satellite map into a geographic map à la Google Maps, and the other way around.
The model was trained using the [example script](https://github.com/huggingface/community-events/tree/main/huggan/pytorch/pix2pix) provided by HuggingFace as part of the [HugGAN sprint](https://github.com/huggingface/community-events/tree/main/huggan).
## Intended uses & limitations
#### How to use
```python
from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet
from PIL import Image
from torchvision.utils import save_image
image = Image.open("...")
generator = GeneratorUNet.from_pretrained("huggan/pix2pix-maps")
pixel_values = transform(image).unsqueeze(0)
output = generator(pixel_values)
save_image(output, 'output.png', normalize=True)
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
The data used was huggan/maps.
## Training procedure
The following command was used:
```bash
accelerate launch train.py --dataset huggan/maps --push_to_hub --model_name pix2pix-maps --checkpoint_interval 1
```
## Eval results
## Generated Images
You can embed local or remote images using ``
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/IsolaZZE16,
author = {Phillip Isola and
Jun{-}Yan Zhu and
Tinghui Zhou and
Alexei A. Efros},
title = {Image-to-Image Translation with Conditional Adversarial Networks},
journal = {CoRR},
volume = {abs/1611.07004},
year = {2016},
url = {http://arxiv.org/abs/1611.07004},
eprinttype = {arXiv},
eprint = {1611.07004},
timestamp = {Mon, 13 Aug 2018 16:49:05 +0200},
biburl = {https://dblp.org/rec/journals/corr/IsolaZZE16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
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] |
obokkkk/kobert-finetuned-klue-v2 | ef5f1945d614548e889146ce106b4d581af5e79e | 2022-04-13T11:24:54.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | obokkkk | null | obokkkk/kobert-finetuned-klue-v2 | 0 | null | transformers | ---
tags:
- generated_from_trainer
model-index:
- name: kobert-finetuned-klue-v2
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. -->
# kobert-finetuned-klue-v2
This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2678
## 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.6289 | 0.54 | 500 | 5.3024 |
| 5.3083 | 1.08 | 1000 | 5.3707 |
| 5.3518 | 1.62 | 1500 | 5.3039 |
| 5.2912 | 2.16 | 2000 | 5.2800 |
| 5.2282 | 2.7 | 2500 | 5.2301 |
| 5.1498 | 3.24 | 3000 | 5.2435 |
| 5.079 | 3.78 | 3500 | 5.1997 |
| 4.8886 | 4.32 | 4000 | 5.1350 |
| 4.8166 | 4.86 | 4500 | 5.1441 |
| 4.5615 | 5.4 | 5000 | 5.1485 |
| 4.4183 | 5.94 | 5500 | 5.0775 |
| 4.1282 | 6.48 | 6000 | 5.0402 |
| 4.1214 | 7.02 | 6500 | 5.1331 |
| 3.7792 | 7.56 | 7000 | 5.1314 |
| 3.7455 | 8.1 | 7500 | 5.1936 |
| 3.5369 | 8.64 | 8000 | 5.1931 |
| 3.4832 | 9.18 | 8500 | 5.2678 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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vabadeh213/autotrain-wikihow-737822494 | c22a2d5b572f10ea0fd77e1a1cde9c2bf6bd8cf8 | 2022-04-13T13:07:34.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"ja",
"dataset:vabadeh213/autotrain-data-wikihow",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | vabadeh213 | null | vabadeh213/autotrain-wikihow-737822494 | 0 | null | transformers | ---
tags: autotrain
language: ja
widget:
- text: "脅威を感じた蛇は再び襲いかかります。したがって、噛まれた際は速やかに蛇の攻撃範囲から離れましょう。 少なくとも6mは間合いを取りましょう。できる限り速やかに医療処置を求めることが大切です。ほとんどの病院は、毒蛇用の抗毒素（血清）を用意しています。病院に到着する前の応急手当だけでは、あまり症状の改善にはつながりません。被害現場からすぐさま救急サービスに通報できれば不幸中の幸いです。救急車を呼べない場合は、何としても助けを求め、みなさんまたは被害者を最寄りの病院へ搬送しなければなりません。みなさんに噛みついた蛇がガラガラヘビかどうかが分からない場合でも、すぐに病院へ直行しましょう。実際に毒が体に回り、症状が出始めたとしても、病院にいれば安心できるでしょう。噛まれた箇所を心臓よりも上に置くと、毒を含んだ血液が猛スピードで心臓に流れ込みます。救助が来るまでの間、できれば被害者の体を静止させましょう。体を動かすと血流が増大し、あっという間に毒が回ります。したがって、毒蛇に噛まれた際は体の動きを最小限に抑えて安静にすることが大切です。もちろん、みなさんの周りに誰もいなければ、じっとしている場合ではありません。すぐに助けを求めましょう。"
- text: "噛み傷の周囲は大きく腫れ上がります。傷口の周りの衣類はすべて取り除きましょう。また、患部に付けているアクセサリー類も取り外しましょう。アクセサリーを付けたままにすると、患部が腫れた際に血管を締め付けてしまうため、場合によっては大切なアクセサリーを切断する羽目になります。30秒ほど噛み傷からそのまま出血させましょう。出血とともに、ある程度は毒が傷口から排出されるでしょう。毒を少しでも吸引できればそれに越したことはありません。ただし、必ず毒蛇用の吸引器を使いましょう。吸引ポンプには詳しい取扱説明書が付属しているはずですが、基本的にポンプを直接噛み傷に当てて毒を吸い出します。傷口を水で洗ってはいけません。皮膚を洗い流してしまうと、後で毒の種類を特定しにくくなります。医療者は皮膚に残った毒からガラガラヘビの種類を特定し、みなさんの症状に最適な治療法を決定します。添え木または三角巾で固定すれば、患部の血流を抑えることができます。できるだけ血流を抑えて毒の回りを遅らせましょう。腕を吊るす場合は、衣類を三角形に折り畳むか、または三角形に切りましょう。肘を中心にして腕を三角巾で包みます。みなさんまたは被害者の肘を布の形に合わせて曲げましょう。三角巾の端同士を肩口で結び合わせます。肘から先を三角形の底辺で固定したら、手を外に出します。 板切れや丸めた新聞紙を添え木にして腕や脚を支えましょう。それらが手元になければ、衣類を丸めて使いましょう。添え木を腕や脚の側面に当て、傷口の上下の関節を伸ばして固定します。ベルト、テープ、包帯といった手元にあるものを使って添え木をしっかりと縛り付けましょう。ただし、直接傷口の上から縛ってはいけません。傷口の上下いずれかの側で縛りましょう。患部の腫れが酷い場合は、添え木の圧迫を緩める必要があります。"
- text: "被害者に話しかけましょう。次々に質問を投げかけ、できる限り被害者の意識を噛み傷から逸らしましょう。 不安やパニックは心拍を上昇させ、毒の回りを速めます。みなさん自身が噛まれた場合は、とにかく落ち着きましょう。何度かゆっくりと深く息をして緊張をほぐしましょう。アメリカに滞在中のみなさんは、できれば救急車を待つ間、アメリカ中毒情報センター（1-800-222-1222）に連絡を取りましょう。噛まれた箇所が腫れ上がれば、一目でそれは毒蛇によるものと判断できます。それと同時に皮膚の変色が起こるでしょう。また、一連の細かい刺傷が残る無毒蛇の噛み傷とは違い、毒蛇の噛み傷は1カ所ないしは2カ所に目立った刺傷が残るのが特徴です。これは毒牙の発達に伴い、歯が退化しているためです。さらに、めまい、患部の激しい痛み、視覚障害、他の箇所のチクチクした痛み、そして極度の発汗といった症状も、毒蛇による噛み傷のサインです。皮膚が青ざめるのはショック症状の典型です。 他のサインとして、心拍の上昇、過呼吸、吐き気、めまいなどがあります。また、被害者の瞳孔が拡大していないかをチェックしましょう。被害者がショック症状を起こしつつある場合は、仰向けに寝かせて足を30cmほど浮かせましょう。さらに、毛布や上着などで体を包んで温めましょう。呼吸、咳、または体の動きといった生体反応が見られない場合は、直ちに心肺蘇生が必要です。これらの物質は毒の回りを加速させます。したがって、ガラガラヘビに噛まれた直後にアルコールまたはカフェイン飲料で水分補給をするのは禁物です。"
datasets:
- vabadeh213/autotrain-data-wikihow
co2_eq_emissions: 361.800665798794
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 737822494
- CO2 Emissions (in grams): 361.800665798794
## Validation Metrics
- Loss: 2.326287031173706
- Rouge1: 5.2053
- Rouge2: 1.8535
- RougeL: 5.2419
- RougeLsum: 5.228
- Gen Len: 18.3677
## 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/vabadeh213/autotrain-wikihow-737822494
``` | [
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melnikoff-oleg/bart-end-to-end | 55ab6562174cca944643de6a1e86875068b41031 | 2022-04-13T11:59:46.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | melnikoff-oleg | null | melnikoff-oleg/bart-end-to-end | 0 | null | transformers | Entry not found | [
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huggingtweets/elonmusk-jeffbezos-sweatystartup | 94780eada23add9a26e08095981a5bb9fea2170a | 2022-04-13T13:47:00.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/elonmusk-jeffbezos-sweatystartup | 0 | null | transformers | ---
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/1503591435324563456/foUrqiEw_400x400.jpg')">
</div>
<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/669103856106668033/UF3cgUk4_400x400.jpg')">
</div>
<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/1504792567031992320/7EflpzpQ_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Jeff Bezos & Nick Huber</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-jeffbezos-sweatystartup</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 Elon Musk & Jeff Bezos & Nick Huber.
| Data | Elon Musk | Jeff Bezos | Nick Huber |
| --- | --- | --- | --- |
| Tweets downloaded | 221 | 294 | 3250 |
| Retweets | 15 | 22 | 44 |
| Short tweets | 67 | 14 | 339 |
| Tweets kept | 139 | 258 | 2867 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hd9nbhx/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 @elonmusk-jeffbezos-sweatystartup's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2f15ydkr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2f15ydkr/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/elonmusk-jeffbezos-sweatystartup')
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)
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frozenwalker/SciFive_pubmedqa_question_generation | ea678c0cf37b4426457a003fcf0f1de018f991a6 | 2022-04-19T11:42:33.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | frozenwalker | null | frozenwalker/SciFive_pubmedqa_question_generation | 0 | null | transformers | Entry not found | [
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masakhane/afrimt5_bbj_fr_news | e924d50eb15911e45941520dcf7066ece7782f16 | 2022-04-13T18:28:32.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimt5_bbj_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/afrimbart_bbj_fr_news | d5ec07e79eeb4b46749070963f446a55fad66aef | 2022-04-13T18:28:39.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimbart_bbj_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/afribyt5_bbj_fr_news | 1ed1883a4e0e479b3e7a7b70b08c802a23f7b4ea | 2022-04-13T19:29:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afribyt5_bbj_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/byt5_fr_bbj_news | 5ba81fa10eeeb493e04df914bf6434e0858df3a2 | 2022-04-13T19:29:58.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/byt5_fr_bbj_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/byt5_bbj_fr_news | dc62009e7fd9f773157e06571fe054f931fda04d | 2022-04-13T19:30:01.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/byt5_bbj_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/mt5_bbj_fr_news | 2abaff5c158f0feacdd135f8e747e747a3e5c522 | 2022-04-13T20:41:14.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mt5_bbj_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/mt5_fr_bbj_news | 547173268338375fca6258a3200692b87225ee9e | 2022-04-13T20:41:07.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mt5_fr_bbj_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/mbart50_bbj_fr_news | e07fdf1eb0cc81155de359bc23c376512ebd9cc0 | 2022-04-13T20:41:18.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mbart50_bbj_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_fr_bbj_news | 46570681958c9b8e59ee1b8e0bc7f8842598decf | 2022-04-13T21:40:15.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_fr_bbj_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_fr_bbj_rel_news | 579c9a135b62a74aad340cf55af9caf01507c151 | 2022-04-13T21:40:19.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_fr_bbj_rel_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_bbj_fr_rel_news | 40949ff2d525d1a3985c783f637e1b836094b526 | 2022-04-13T21:40:24.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_bbj_fr_rel_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_fr_bbj_rel_news_ft | 0b00157684120bdf5d7a5ce07897443df73b0a42 | 2022-04-14T08:42:45.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_fr_bbj_rel_news_ft | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_fr_bbj_rel | 8635f78e4017ff014116d6f0b031a0b14c62109d | 2022-04-14T08:42:48.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_fr_bbj_rel | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_fr_bbj_rel_ft | 144e9b6b42351bb0ce3735abc4b7cdd1b3252b7d | 2022-04-14T10:00:19.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_fr_bbj_rel_ft | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_bbj_fr_rel_ft | 1f99875257572a5a807d696773a8e775d5bab245 | 2022-04-14T10:00:22.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_bbj_fr_rel_ft | 0 | null | transformers | ---
license: afl-3.0
---
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ntoldalagi/nick_asr_fresh | 136346b60c1781725fa77427cc32c17b1ee7d2f7 | 2022-04-15T01:04:08.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | ntoldalagi | null | ntoldalagi/nick_asr_fresh | 0 | null | transformers | Entry not found | [
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lilitket/20220413-210552 | bdac66b4f511f417c2380db3409241edf37a6baa | 2022-04-14T05:44:32.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220413-210552 | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: 20220413-210552
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. -->
# 20220413-210552
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0348
- Wer: 1.0006
## 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: 6e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 1200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:------:|:---------------:|:------:|
| 17.1111 | 1.5 | 200 | 16.6792 | 1.0 |
| 16.0992 | 3.01 | 400 | 15.3947 | 1.0 |
| 10.7668 | 4.51 | 600 | 10.3625 | 1.0 |
| 6.2652 | 6.02 | 800 | 7.6849 | 1.0 |
| 5.1417 | 7.52 | 1000 | 6.0307 | 1.0 |
| 4.6159 | 9.02 | 1200 | 5.0891 | 1.0 |
| 4.2444 | 10.53 | 1400 | 4.4120 | 1.0 |
| 3.8935 | 12.03 | 1600 | 3.9570 | 1.0 |
| 3.6292 | 13.53 | 1800 | 3.6405 | 1.0 |
| 3.4535 | 15.04 | 2000 | 3.4523 | 1.0 |
| 3.3175 | 16.54 | 2200 | 3.3589 | 1.0 |
| 3.2266 | 18.05 | 2400 | 3.2966 | 1.0 |
| 3.1825 | 19.55 | 2600 | 3.2658 | 1.0 |
| 3.1604 | 21.05 | 2800 | 3.2534 | 1.0 |
| 3.1438 | 22.56 | 3000 | 3.2437 | 1.0 |
| 3.1176 | 24.06 | 3200 | 3.2169 | 1.0 |
| 3.1088 | 25.56 | 3400 | 3.2102 | 1.0 |
| 3.0955 | 27.07 | 3600 | 3.1983 | 1.0 |
| 3.0763 | 28.57 | 3800 | 3.2092 | 1.0 |
| 3.0599 | 30.08 | 4000 | 3.2092 | 1.0 |
| 3.0385 | 31.58 | 4200 | 3.2154 | 1.0 |
| 2.9996 | 33.08 | 4400 | 3.2120 | 1.0 |
| 2.9207 | 34.59 | 4600 | 3.2146 | 1.0 |
| 2.8071 | 36.09 | 4800 | 3.2093 | 1.0 |
| 2.6412 | 37.59 | 5000 | 3.2282 | 1.0 |
| 2.4594 | 39.1 | 5200 | 3.2442 | 1.0 |
| 2.2708 | 40.6 | 5400 | 3.2944 | 1.0 |
| 2.1279 | 42.11 | 5600 | 3.3260 | 1.0 |
| 1.9985 | 43.61 | 5800 | 3.3586 | 1.0 |
| 1.8979 | 45.11 | 6000 | 3.3945 | 1.0 |
| 1.7838 | 46.62 | 6200 | 3.4761 | 1.0 |
| 1.6774 | 48.12 | 6400 | 3.4886 | 1.0 |
| 1.5958 | 49.62 | 6600 | 3.6208 | 1.0 |
| 1.4957 | 51.13 | 6800 | 3.6501 | 1.0 |
| 1.4202 | 52.63 | 7000 | 3.6492 | 1.0 |
| 1.3377 | 54.14 | 7200 | 3.7392 | 1.0 |
| 1.2872 | 55.64 | 7400 | 3.8624 | 1.0 |
| 1.1992 | 57.14 | 7600 | 3.8511 | 1.0 |
| 1.1238 | 58.65 | 7800 | 3.9662 | 1.0 |
| 1.0775 | 60.15 | 8000 | 3.9267 | 1.0 |
| 1.011 | 61.65 | 8200 | 4.0933 | 1.0 |
| 0.962 | 63.16 | 8400 | 4.0941 | 1.0 |
| 0.9041 | 64.66 | 8600 | 4.1163 | 1.0 |
| 0.8552 | 66.17 | 8800 | 4.1937 | 1.0 |
| 0.8054 | 67.67 | 9000 | 4.2277 | 1.0 |
| 0.7457 | 69.17 | 9200 | 4.3899 | 1.0 |
| 0.7292 | 70.68 | 9400 | 4.3621 | 1.0 |
| 0.6635 | 72.18 | 9600 | 4.4706 | 1.0 |
| 0.6333 | 73.68 | 9800 | 4.4571 | 1.0 |
| 0.6109 | 75.19 | 10000 | 4.4594 | 1.0 |
| 0.5611 | 76.69 | 10200 | 4.5672 | 1.0 |
| 0.5286 | 78.2 | 10400 | 4.4957 | 1.0 |
| 0.4894 | 79.7 | 10600 | 4.5278 | 1.0 |
| 0.4831 | 81.2 | 10800 | 4.4604 | 1.0 |
| 0.4575 | 82.71 | 11000 | 4.7439 | 1.0 |
| 0.4418 | 84.21 | 11200 | 4.6511 | 1.0 |
| 0.4085 | 85.71 | 11400 | 4.5008 | 1.0 |
| 0.4011 | 87.22 | 11600 | 4.7690 | 1.0 |
| 0.3791 | 88.72 | 11800 | 4.8675 | 1.0 |
| 0.3487 | 90.23 | 12000 | 5.0327 | 1.0 |
| 0.3661 | 91.73 | 12200 | 4.8084 | 1.0 |
| 0.3306 | 93.23 | 12400 | 4.9095 | 1.0 |
| 0.3449 | 94.74 | 12600 | 4.8223 | 1.0 |
| 0.2949 | 96.24 | 12800 | 4.8245 | 1.0 |
| 0.2987 | 97.74 | 13000 | 5.0803 | 1.0 |
| 0.2896 | 99.25 | 13200 | 5.2074 | 1.0 |
| 0.2731 | 100.75 | 13400 | 5.1951 | 1.0 |
| 0.2749 | 102.26 | 13600 | 5.2071 | 1.0 |
| 0.2554 | 103.76 | 13800 | 5.0861 | 1.0 |
| 0.2436 | 105.26 | 14000 | 5.0851 | 1.0 |
| 0.2494 | 106.77 | 14200 | 4.8623 | 1.0 |
| 0.23 | 108.27 | 14400 | 5.0466 | 1.0 |
| 0.2345 | 109.77 | 14600 | 5.2474 | 1.0 |
| 0.2233 | 111.28 | 14800 | 4.9394 | 1.0 |
| 0.2231 | 112.78 | 15000 | 4.9572 | 1.0 |
| 0.213 | 114.29 | 15200 | 5.3215 | 1.0 |
| 0.2002 | 115.79 | 15400 | 5.3042 | 1.0 |
| 0.2023 | 117.29 | 15600 | 5.0279 | 1.0 |
| 0.2074 | 118.8 | 15800 | 4.9727 | 1.0 |
| 0.2071 | 120.3 | 16000 | 4.6775 | 1.0 |
| 0.1915 | 121.8 | 16200 | 4.8386 | 1.0 |
| 0.1899 | 123.31 | 16400 | 4.7898 | 1.0 |
| 0.1821 | 124.81 | 16600 | 5.3147 | 1.0 |
| 0.1908 | 126.32 | 16800 | 5.6218 | 1.0 |
| 0.1712 | 127.82 | 17000 | 4.6083 | 1.0 |
| 0.1705 | 129.32 | 17200 | 5.2468 | 1.0 |
| 0.1664 | 130.83 | 17400 | 5.0412 | 1.0 |
| 0.167 | 132.33 | 17600 | 5.0116 | 1.0 |
| 0.162 | 133.83 | 17800 | 5.2799 | 1.0 |
| 0.1561 | 135.34 | 18000 | 5.2485 | 1.0 |
| 0.1501 | 136.84 | 18200 | 5.1109 | 1.0 |
| 0.14 | 138.35 | 18400 | 5.2310 | 1.0 |
| 0.1576 | 139.85 | 18600 | 5.1631 | 1.0 |
| 0.1433 | 141.35 | 18800 | 5.3497 | 1.0 |
| 0.148 | 142.86 | 19000 | 4.8892 | 1.0 |
| 0.1525 | 144.36 | 19200 | 4.8522 | 1.0 |
| 0.1517 | 145.86 | 19400 | 4.7830 | 1.0 |
| 0.139 | 147.37 | 19600 | 5.2041 | 1.0 |
| 0.1392 | 148.87 | 19800 | 4.7968 | 1.0 |
| 0.1351 | 150.38 | 20000 | 5.0326 | 1.0 |
| 0.1355 | 151.88 | 20200 | 5.0474 | 1.0 |
| 0.138 | 153.38 | 20400 | 4.7491 | 1.0006 |
| 0.1332 | 154.89 | 20600 | 5.3905 | 1.0 |
| 0.1252 | 156.39 | 20800 | 4.9815 | 1.0 |
| 0.1179 | 157.89 | 21000 | 5.3281 | 1.0 |
| 0.1228 | 159.4 | 21200 | 5.1108 | 1.0006 |
| 0.1311 | 160.9 | 21400 | 4.8016 | 1.0 |
| 0.1278 | 162.41 | 21600 | 4.8306 | 1.0 |
| 0.1209 | 163.91 | 21800 | 4.6413 | 1.0 |
| 0.1199 | 165.41 | 22000 | 4.6375 | 1.0 |
| 0.1172 | 166.92 | 22200 | 4.9108 | 1.0 |
| 0.1247 | 168.42 | 22400 | 4.6139 | 1.0006 |
| 0.1121 | 169.92 | 22600 | 4.4765 | 1.0006 |
| 0.125 | 171.43 | 22800 | 4.6819 | 1.0006 |
| 0.1259 | 172.93 | 23000 | 4.9577 | 1.0 |
| 0.1044 | 174.44 | 23200 | 5.2993 | 1.0006 |
| 0.1107 | 175.94 | 23400 | 4.3140 | 1.0 |
| 0.1142 | 177.44 | 23600 | 4.5850 | 1.0 |
| 0.0971 | 178.95 | 23800 | 4.8177 | 1.0006 |
| 0.1186 | 180.45 | 24000 | 4.9972 | 1.0 |
| 0.1164 | 181.95 | 24200 | 4.5840 | 1.0 |
| 0.1014 | 183.46 | 24400 | 4.9117 | 0.9994 |
| 0.1087 | 184.96 | 24600 | 4.5646 | 1.0006 |
| 0.1075 | 186.47 | 24800 | 4.6995 | 1.0 |
| 0.1111 | 187.97 | 25000 | 4.7877 | 1.0 |
| 0.1079 | 189.47 | 25200 | 4.8420 | 1.0 |
| 0.1053 | 190.98 | 25400 | 5.1083 | 1.0 |
| 0.1048 | 192.48 | 25600 | 4.2876 | 1.0 |
| 0.0974 | 193.98 | 25800 | 4.6699 | 1.0006 |
| 0.0983 | 195.49 | 26000 | 4.6522 | 1.0 |
| 0.0935 | 196.99 | 26200 | 4.9879 | 1.0 |
| 0.0948 | 198.5 | 26400 | 4.4146 | 1.0 |
| 0.0867 | 200.0 | 26600 | 5.1909 | 1.0 |
| 0.0932 | 201.5 | 26800 | 5.2019 | 1.0 |
| 0.0951 | 203.01 | 27000 | 3.6893 | 1.0 |
| 0.085 | 204.51 | 27200 | 4.3071 | 1.0006 |
| 0.0912 | 206.02 | 27400 | 4.4651 | 1.0 |
| 0.092 | 207.52 | 27600 | 4.4218 | 1.0 |
| 0.0936 | 209.02 | 27800 | 5.1391 | 1.0 |
| 0.0989 | 210.53 | 28000 | 4.8787 | 1.0006 |
| 0.0898 | 212.03 | 28200 | 4.1418 | 1.0013 |
| 0.0943 | 213.53 | 28400 | 4.1857 | 1.0 |
| 0.0834 | 215.04 | 28600 | 4.3519 | 1.0 |
| 0.0851 | 216.54 | 28800 | 4.3612 | 1.0006 |
| 0.0932 | 218.05 | 29000 | 4.2200 | 1.0006 |
| 0.0848 | 219.55 | 29200 | 4.2054 | 1.0 |
| 0.0873 | 221.05 | 29400 | 4.4815 | 1.0 |
| 0.0949 | 222.56 | 29600 | 3.9426 | 1.0 |
| 0.0856 | 224.06 | 29800 | 3.7650 | 1.0 |
| 0.0768 | 225.56 | 30000 | 3.9774 | 1.0 |
| 0.0823 | 227.07 | 30200 | 4.3728 | 1.0 |
| 0.0913 | 228.57 | 30400 | 3.7813 | 1.0 |
| 0.0951 | 230.08 | 30600 | 4.1581 | 1.0 |
| 0.0843 | 231.58 | 30800 | 4.6891 | 1.0 |
| 0.0879 | 233.08 | 31000 | 4.2984 | 1.0 |
| 0.0807 | 234.59 | 31200 | 3.9511 | 1.0 |
| 0.0765 | 236.09 | 31400 | 3.8094 | 1.0 |
| 0.0861 | 237.59 | 31600 | 4.3118 | 1.0 |
| 0.0596 | 239.1 | 31800 | 4.0774 | 1.0006 |
| 0.0752 | 240.6 | 32000 | 3.6005 | 1.0 |
| 0.0729 | 242.11 | 32200 | 4.8616 | 1.0 |
| 0.0783 | 243.61 | 32400 | 3.9858 | 1.0 |
| 0.0759 | 245.11 | 32600 | 4.1231 | 1.0 |
| 0.08 | 246.62 | 32800 | 4.5182 | 1.0 |
| 0.0782 | 248.12 | 33000 | 3.7721 | 1.0 |
| 0.0914 | 249.62 | 33200 | 3.5902 | 1.0 |
| 0.0668 | 251.13 | 33400 | 3.9673 | 1.0 |
| 0.0798 | 252.63 | 33600 | 3.8693 | 1.0 |
| 0.0814 | 254.14 | 33800 | 3.9804 | 1.0006 |
| 0.0775 | 255.64 | 34000 | 3.9483 | 1.0 |
| 0.0721 | 257.14 | 34200 | 4.6892 | 1.0 |
| 0.0722 | 258.65 | 34400 | 4.1972 | 1.0 |
| 0.0755 | 260.15 | 34600 | 4.4523 | 1.0 |
| 0.0683 | 261.65 | 34800 | 4.1090 | 1.0 |
| 0.0698 | 263.16 | 35000 | 4.0634 | 1.0 |
| 0.0712 | 264.66 | 35200 | 4.0469 | 1.0006 |
| 0.0754 | 266.17 | 35400 | 4.0113 | 1.0006 |
| 0.0709 | 267.67 | 35600 | 4.0592 | 1.0 |
| 0.0637 | 269.17 | 35800 | 3.7540 | 1.0 |
| 0.0688 | 270.68 | 36000 | 3.9645 | 1.0 |
| 0.0592 | 272.18 | 36200 | 3.7443 | 1.0 |
| 0.0585 | 273.68 | 36400 | 3.8287 | 1.0 |
| 0.0734 | 275.19 | 36600 | 3.6780 | 1.0 |
| 0.058 | 276.69 | 36800 | 4.0194 | 1.0 |
| 0.0707 | 278.2 | 37000 | 3.6663 | 1.0006 |
| 0.0728 | 279.7 | 37200 | 3.8640 | 1.0 |
| 0.064 | 281.2 | 37400 | 4.5473 | 1.0 |
| 0.0583 | 282.71 | 37600 | 4.1813 | 1.0 |
| 0.0634 | 284.21 | 37800 | 3.8821 | 1.0 |
| 0.0565 | 285.71 | 38000 | 3.9566 | 1.0006 |
| 0.0735 | 287.22 | 38200 | 4.5317 | 1.0 |
| 0.0797 | 288.72 | 38400 | 3.8040 | 1.0 |
| 0.0601 | 290.23 | 38600 | 4.0956 | 1.0 |
| 0.0599 | 291.73 | 38800 | 4.0592 | 1.0 |
| 0.0517 | 293.23 | 39000 | 3.5204 | 1.0006 |
| 0.0622 | 294.74 | 39200 | 4.1739 | 1.0 |
| 0.0705 | 296.24 | 39400 | 4.0262 | 1.0 |
| 0.0589 | 297.74 | 39600 | 4.2476 | 1.0 |
| 0.0606 | 299.25 | 39800 | 3.7931 | 1.0 |
| 0.0603 | 300.75 | 40000 | 4.0540 | 0.9994 |
| 0.0568 | 302.26 | 40200 | 3.5900 | 1.0 |
| 0.0583 | 303.76 | 40400 | 3.8095 | 1.0 |
| 0.0513 | 305.26 | 40600 | 3.8949 | 1.0 |
| 0.0637 | 306.77 | 40800 | 3.8085 | 1.0 |
| 0.0659 | 308.27 | 41000 | 4.2311 | 1.0 |
| 0.068 | 309.77 | 41200 | 3.4876 | 1.0006 |
| 0.0616 | 311.28 | 41400 | 3.7634 | 1.0 |
| 0.0515 | 312.78 | 41600 | 3.8762 | 1.0 |
| 0.0584 | 314.29 | 41800 | 4.2070 | 1.0 |
| 0.054 | 315.79 | 42000 | 3.9088 | 1.0 |
| 0.0571 | 317.29 | 42200 | 3.9679 | 1.0006 |
| 0.0497 | 318.8 | 42400 | 3.8443 | 1.0 |
| 0.0507 | 320.3 | 42600 | 4.2397 | 1.0 |
| 0.0612 | 321.8 | 42800 | 4.2228 | 1.0 |
| 0.0467 | 323.31 | 43000 | 3.6684 | 1.0 |
| 0.0586 | 324.81 | 43200 | 3.8685 | 1.0013 |
| 0.0557 | 326.32 | 43400 | 4.3341 | 1.0006 |
| 0.0584 | 327.82 | 43600 | 3.6683 | 1.0 |
| 0.0575 | 329.32 | 43800 | 3.9005 | 1.0 |
| 0.0571 | 330.83 | 44000 | 3.8594 | 1.0 |
| 0.0471 | 332.33 | 44200 | 3.6871 | 1.0 |
| 0.055 | 333.83 | 44400 | 4.0402 | 1.0 |
| 0.0422 | 335.34 | 44600 | 4.0226 | 1.0 |
| 0.0422 | 336.84 | 44800 | 3.5907 | 1.0 |
| 0.0513 | 338.35 | 45000 | 3.7380 | 1.0 |
| 0.0593 | 339.85 | 45200 | 3.8530 | 1.0 |
| 0.0446 | 341.35 | 45400 | 4.0879 | 1.0 |
| 0.0492 | 342.86 | 45600 | 3.8984 | 1.0 |
| 0.0422 | 344.36 | 45800 | 4.2423 | 1.0 |
| 0.0478 | 345.86 | 46000 | 3.6391 | 1.0 |
| 0.0425 | 347.37 | 46200 | 4.2352 | 1.0 |
| 0.0426 | 348.87 | 46400 | 4.0004 | 1.0 |
| 0.0566 | 350.38 | 46600 | 4.0957 | 1.0006 |
| 0.0522 | 351.88 | 46800 | 3.9436 | 0.9994 |
| 0.0503 | 353.38 | 47000 | 4.3325 | 1.0 |
| 0.0513 | 354.89 | 47200 | 3.5738 | 0.9994 |
| 0.0428 | 356.39 | 47400 | 3.8233 | 0.9994 |
| 0.0402 | 357.89 | 47600 | 3.6210 | 1.0006 |
| 0.0575 | 359.4 | 47800 | 3.6991 | 1.0 |
| 0.0459 | 360.9 | 48000 | 3.8384 | 1.0 |
| 0.0423 | 362.41 | 48200 | 4.2164 | 1.0 |
| 0.0401 | 363.91 | 48400 | 4.0001 | 1.0 |
| 0.0612 | 365.41 | 48600 | 4.1363 | 0.9994 |
| 0.0492 | 366.92 | 48800 | 4.0748 | 0.9994 |
| 0.0467 | 368.42 | 49000 | 3.6856 | 0.9994 |
| 0.0565 | 369.92 | 49200 | 4.1829 | 1.0 |
| 0.0351 | 371.43 | 49400 | 3.9579 | 1.0006 |
| 0.0499 | 372.93 | 49600 | 3.8893 | 1.0 |
| 0.0477 | 374.44 | 49800 | 3.5199 | 1.0 |
| 0.0471 | 375.94 | 50000 | 4.0405 | 1.0013 |
| 0.037 | 377.44 | 50200 | 3.8785 | 1.0006 |
| 0.0382 | 378.95 | 50400 | 4.2958 | 1.0013 |
| 0.0553 | 380.45 | 50600 | 4.3845 | 1.0006 |
| 0.0389 | 381.95 | 50800 | 3.7282 | 1.0013 |
| 0.0373 | 383.46 | 51000 | 4.0840 | 1.0006 |
| 0.0597 | 384.96 | 51200 | 4.0250 | 1.0006 |
| 0.0404 | 386.47 | 51400 | 3.6077 | 0.9994 |
| 0.0501 | 387.97 | 51600 | 3.5198 | 0.9994 |
| 0.0432 | 389.47 | 51800 | 3.7678 | 1.0 |
| 0.0462 | 390.98 | 52000 | 3.6467 | 1.0 |
| 0.0444 | 392.48 | 52200 | 3.8941 | 1.0006 |
| 0.0494 | 393.98 | 52400 | 3.6969 | 1.0 |
| 0.0402 | 395.49 | 52600 | 3.5324 | 1.0006 |
| 0.0402 | 396.99 | 52800 | 3.8874 | 1.0006 |
| 0.0395 | 398.5 | 53000 | 3.3793 | 1.0006 |
| 0.0367 | 400.0 | 53200 | 3.6539 | 1.0013 |
| 0.0374 | 401.5 | 53400 | 3.4230 | 1.0 |
| 0.0407 | 403.01 | 53600 | 3.6068 | 1.0 |
| 0.0378 | 404.51 | 53800 | 3.5291 | 1.0013 |
| 0.0471 | 406.02 | 54000 | 3.3563 | 1.0006 |
| 0.0336 | 407.52 | 54200 | 2.9932 | 1.0044 |
| 0.032 | 409.02 | 54400 | 3.3549 | 1.0006 |
| 0.0474 | 410.53 | 54600 | 3.1140 | 1.0013 |
| 0.0402 | 412.03 | 54800 | 2.8515 | 1.0075 |
| 0.0366 | 413.53 | 55000 | 3.1142 | 1.0050 |
| 0.041 | 415.04 | 55200 | 3.0917 | 1.0056 |
| 0.0358 | 416.54 | 55400 | 3.3401 | 1.0006 |
| 0.0299 | 418.05 | 55600 | 3.5304 | 1.0 |
| 0.0512 | 419.55 | 55800 | 3.4768 | 1.0 |
| 0.0374 | 421.05 | 56000 | 3.2792 | 0.9994 |
| 0.037 | 422.56 | 56200 | 3.0088 | 1.0013 |
| 0.0395 | 424.06 | 56400 | 3.0185 | 0.9994 |
| 0.039 | 425.56 | 56600 | 3.0249 | 1.0 |
| 0.0345 | 427.07 | 56800 | 3.3437 | 1.0 |
| 0.0438 | 428.57 | 57000 | 3.0905 | 1.0 |
| 0.0373 | 430.08 | 57200 | 3.4256 | 1.0019 |
| 0.0362 | 431.58 | 57400 | 3.4747 | 1.0006 |
| 0.0353 | 433.08 | 57600 | 3.3952 | 1.0019 |
| 0.0363 | 434.59 | 57800 | 3.3967 | 1.0006 |
| 0.0234 | 436.09 | 58000 | 3.5076 | 0.9994 |
| 0.0347 | 437.59 | 58200 | 3.4128 | 0.9987 |
| 0.039 | 439.1 | 58400 | 3.5784 | 1.0013 |
| 0.041 | 440.6 | 58600 | 3.6292 | 1.0013 |
| 0.0348 | 442.11 | 58800 | 3.3197 | 0.9981 |
| 0.0285 | 443.61 | 59000 | 3.0545 | 0.9981 |
| 0.0293 | 445.11 | 59200 | 3.1784 | 1.0013 |
| 0.0394 | 446.62 | 59400 | 2.8197 | 0.9975 |
| 0.0282 | 448.12 | 59600 | 3.1450 | 1.0 |
| 0.0387 | 449.62 | 59800 | 2.8260 | 0.9987 |
| 0.0348 | 451.13 | 60000 | 3.0389 | 0.9987 |
| 0.03 | 452.63 | 60200 | 3.1857 | 0.9981 |
| 0.029 | 454.14 | 60400 | 3.1893 | 1.0013 |
| 0.0333 | 455.64 | 60600 | 3.1428 | 0.9981 |
| 0.027 | 457.14 | 60800 | 3.5792 | 0.9994 |
| 0.037 | 458.65 | 61000 | 3.4338 | 1.0 |
| 0.0327 | 460.15 | 61200 | 2.9592 | 1.0056 |
| 0.0283 | 461.65 | 61400 | 3.3657 | 1.0031 |
| 0.0337 | 463.16 | 61600 | 3.1392 | 1.0025 |
| 0.0275 | 464.66 | 61800 | 3.2706 | 1.0019 |
| 0.0304 | 466.17 | 62000 | 3.5289 | 1.0019 |
| 0.042 | 467.67 | 62200 | 3.2222 | 1.0019 |
| 0.035 | 469.17 | 62400 | 3.3533 | 1.0038 |
| 0.038 | 470.68 | 62600 | 3.7162 | 1.0006 |
| 0.0297 | 472.18 | 62800 | 3.2382 | 1.0025 |
| 0.0314 | 473.68 | 63000 | 3.2555 | 1.0006 |
| 0.0288 | 475.19 | 63200 | 3.8791 | 1.0013 |
| 0.0336 | 476.69 | 63400 | 3.1988 | 1.0006 |
| 0.0371 | 478.2 | 63600 | 3.1025 | 1.0006 |
| 0.0288 | 479.7 | 63800 | 3.2712 | 1.0006 |
| 0.0374 | 481.2 | 64000 | 3.4452 | 0.9987 |
| 0.0329 | 482.71 | 64200 | 3.3956 | 1.0 |
| 0.036 | 484.21 | 64400 | 3.4541 | 1.0006 |
| 0.03 | 485.71 | 64600 | 3.2153 | 1.0 |
| 0.0319 | 487.22 | 64800 | 2.9109 | 0.9962 |
| 0.0276 | 488.72 | 65000 | 3.0832 | 0.9975 |
| 0.0315 | 490.23 | 65200 | 3.4669 | 1.0 |
| 0.0244 | 491.73 | 65400 | 3.4678 | 0.9994 |
| 0.0306 | 493.23 | 65600 | 3.6775 | 0.9987 |
| 0.0266 | 494.74 | 65800 | 3.3509 | 1.0 |
| 0.0247 | 496.24 | 66000 | 3.8882 | 1.0006 |
| 0.0322 | 497.74 | 66200 | 3.5801 | 0.9994 |
| 0.04 | 499.25 | 66400 | 3.6454 | 1.0006 |
| 0.0313 | 500.75 | 66600 | 3.9949 | 1.0013 |
| 0.0222 | 502.26 | 66800 | 3.5645 | 0.9987 |
| 0.0257 | 503.76 | 67000 | 3.7539 | 0.9994 |
| 0.028 | 505.26 | 67200 | 3.6815 | 1.0006 |
| 0.0256 | 506.77 | 67400 | 3.3799 | 1.0013 |
| 0.0257 | 508.27 | 67600 | 3.6969 | 1.0013 |
| 0.0271 | 509.77 | 67800 | 3.7083 | 0.9994 |
| 0.0265 | 511.28 | 68000 | 3.3015 | 0.9994 |
| 0.0289 | 512.78 | 68200 | 3.7103 | 1.0006 |
| 0.0337 | 514.29 | 68400 | 3.1877 | 1.0006 |
| 0.0396 | 515.79 | 68600 | 3.7914 | 1.0 |
| 0.0316 | 517.29 | 68800 | 3.4757 | 0.9981 |
| 0.0303 | 518.8 | 69000 | 3.4129 | 0.9987 |
| 0.0298 | 520.3 | 69200 | 3.1453 | 1.0 |
| 0.0257 | 521.8 | 69400 | 3.4649 | 1.0 |
| 0.0252 | 523.31 | 69600 | 3.2707 | 0.9994 |
| 0.0254 | 524.81 | 69800 | 3.0304 | 1.0056 |
| 0.0199 | 526.32 | 70000 | 3.4323 | 1.0019 |
| 0.0333 | 527.82 | 70200 | 3.4081 | 1.0006 |
| 0.0205 | 529.32 | 70400 | 2.9900 | 1.0188 |
| 0.0223 | 530.83 | 70600 | 3.3499 | 1.0031 |
| 0.0261 | 532.33 | 70800 | 3.2094 | 0.9975 |
| 0.0198 | 533.83 | 71000 | 3.2793 | 1.0063 |
| 0.021 | 535.34 | 71200 | 3.2266 | 0.9975 |
| 0.022 | 536.84 | 71400 | 3.1391 | 0.9994 |
| 0.0215 | 538.35 | 71600 | 3.4587 | 0.9994 |
| 0.0269 | 539.85 | 71800 | 3.2814 | 0.9994 |
| 0.0226 | 541.35 | 72000 | 3.2583 | 0.9981 |
| 0.0257 | 542.86 | 72200 | 3.7259 | 0.9987 |
| 0.0259 | 544.36 | 72400 | 3.5004 | 0.9994 |
| 0.0223 | 545.86 | 72600 | 3.3689 | 0.9975 |
| 0.0307 | 547.37 | 72800 | 3.0706 | 0.9950 |
| 0.0234 | 548.87 | 73000 | 3.0892 | 0.9969 |
| 0.0243 | 550.38 | 73200 | 3.4028 | 0.9987 |
| 0.0215 | 551.88 | 73400 | 2.9474 | 0.9981 |
| 0.0224 | 553.38 | 73600 | 3.3028 | 0.9981 |
| 0.0282 | 554.89 | 73800 | 3.5360 | 0.9994 |
| 0.0282 | 556.39 | 74000 | 3.3730 | 0.9981 |
| 0.0278 | 557.89 | 74200 | 3.2750 | 1.0 |
| 0.0198 | 559.4 | 74400 | 3.0983 | 1.0025 |
| 0.0229 | 560.9 | 74600 | 3.2628 | 1.0006 |
| 0.0234 | 562.41 | 74800 | 2.9351 | 0.9987 |
| 0.0255 | 563.91 | 75000 | 2.9952 | 0.9962 |
| 0.0267 | 565.41 | 75200 | 3.1814 | 0.9969 |
| 0.0217 | 566.92 | 75400 | 3.1355 | 1.0006 |
| 0.0236 | 568.42 | 75600 | 3.5030 | 1.0019 |
| 0.0249 | 569.92 | 75800 | 2.8262 | 1.0006 |
| 0.0192 | 571.43 | 76000 | 3.0427 | 1.0006 |
| 0.0194 | 572.93 | 76200 | 3.0055 | 0.9981 |
| 0.0257 | 574.44 | 76400 | 3.2506 | 0.9987 |
| 0.0214 | 575.94 | 76600 | 3.2980 | 0.9981 |
| 0.0314 | 577.44 | 76800 | 3.4304 | 0.9981 |
| 0.0186 | 578.95 | 77000 | 3.2306 | 0.9987 |
| 0.0219 | 580.45 | 77200 | 3.4410 | 0.9994 |
| 0.0244 | 581.95 | 77400 | 3.3982 | 0.9994 |
| 0.0232 | 583.46 | 77600 | 3.0487 | 0.9962 |
| 0.026 | 584.96 | 77800 | 2.9061 | 1.0 |
| 0.0206 | 586.47 | 78000 | 2.8331 | 1.0006 |
| 0.0202 | 587.97 | 78200 | 2.8493 | 0.9956 |
| 0.0251 | 589.47 | 78400 | 3.0203 | 0.9956 |
| 0.0231 | 590.98 | 78600 | 3.0142 | 0.9969 |
| 0.0183 | 592.48 | 78800 | 3.1480 | 0.9969 |
| 0.022 | 593.98 | 79000 | 3.0777 | 0.9975 |
| 0.0225 | 595.49 | 79200 | 3.3248 | 0.9975 |
| 0.0186 | 596.99 | 79400 | 3.3309 | 0.9994 |
| 0.0184 | 598.5 | 79600 | 3.4888 | 0.9981 |
| 0.0181 | 600.0 | 79800 | 3.2628 | 0.9962 |
| 0.0158 | 601.5 | 80000 | 3.4862 | 0.9975 |
| 0.0197 | 603.01 | 80200 | 3.2335 | 0.9962 |
| 0.0259 | 604.51 | 80400 | 3.1643 | 0.9987 |
| 0.0218 | 606.02 | 80600 | 3.6256 | 0.9969 |
| 0.0196 | 607.52 | 80800 | 3.3183 | 1.0 |
| 0.0127 | 609.02 | 81000 | 3.7223 | 0.9987 |
| 0.0182 | 610.53 | 81200 | 3.2768 | 0.9994 |
| 0.0234 | 612.03 | 81400 | 3.3749 | 1.0 |
| 0.0195 | 613.53 | 81600 | 3.2097 | 0.9987 |
| 0.0259 | 615.04 | 81800 | 3.2090 | 0.9987 |
| 0.0217 | 616.54 | 82000 | 3.3893 | 0.9981 |
| 0.0181 | 618.05 | 82200 | 3.2223 | 0.9994 |
| 0.0155 | 619.55 | 82400 | 3.1307 | 0.9969 |
| 0.0192 | 621.05 | 82600 | 3.0695 | 0.9975 |
| 0.0155 | 622.56 | 82800 | 3.3623 | 0.9975 |
| 0.0266 | 624.06 | 83000 | 3.0329 | 0.9937 |
| 0.017 | 625.56 | 83200 | 2.8108 | 0.9956 |
| 0.0224 | 627.07 | 83400 | 3.2747 | 0.9981 |
| 0.0202 | 628.57 | 83600 | 3.1392 | 0.9981 |
| 0.0157 | 630.08 | 83800 | 2.9493 | 0.9975 |
| 0.0187 | 631.58 | 84000 | 2.9518 | 0.9969 |
| 0.0234 | 633.08 | 84200 | 3.3938 | 0.9981 |
| 0.0205 | 634.59 | 84400 | 2.9594 | 0.9937 |
| 0.0161 | 636.09 | 84600 | 2.7611 | 0.9944 |
| 0.0187 | 637.59 | 84800 | 2.8704 | 0.9969 |
| 0.0285 | 639.1 | 85000 | 3.1030 | 0.9987 |
| 0.0163 | 640.6 | 85200 | 3.2914 | 0.9975 |
| 0.0216 | 642.11 | 85400 | 2.9154 | 0.9969 |
| 0.0233 | 643.61 | 85600 | 2.7749 | 0.9975 |
| 0.0218 | 645.11 | 85800 | 2.8473 | 1.0 |
| 0.0196 | 646.62 | 86000 | 2.5928 | 1.0038 |
| 0.0158 | 648.12 | 86200 | 2.8972 | 0.9962 |
| 0.0156 | 649.62 | 86400 | 2.9890 | 0.9981 |
| 0.0131 | 651.13 | 86600 | 2.7676 | 0.9987 |
| 0.0206 | 652.63 | 86800 | 2.8754 | 0.9944 |
| 0.0197 | 654.14 | 87000 | 3.0768 | 0.9962 |
| 0.0139 | 655.64 | 87200 | 2.6567 | 0.9975 |
| 0.0139 | 657.14 | 87400 | 3.1492 | 0.9987 |
| 0.0226 | 658.65 | 87600 | 2.7989 | 0.9975 |
| 0.0182 | 660.15 | 87800 | 3.0076 | 1.0013 |
| 0.0164 | 661.65 | 88000 | 2.9625 | 0.9975 |
| 0.0221 | 663.16 | 88200 | 2.9768 | 0.9987 |
| 0.0144 | 664.66 | 88400 | 3.0221 | 0.9962 |
| 0.0178 | 666.17 | 88600 | 2.9881 | 0.9981 |
| 0.0214 | 667.67 | 88800 | 3.3127 | 0.9994 |
| 0.0193 | 669.17 | 89000 | 3.0776 | 0.9969 |
| 0.0211 | 670.68 | 89200 | 2.8097 | 0.9975 |
| 0.0223 | 672.18 | 89400 | 3.1911 | 0.9975 |
| 0.0222 | 673.68 | 89600 | 3.0041 | 0.9969 |
| 0.0209 | 675.19 | 89800 | 2.9853 | 0.9975 |
| 0.0116 | 676.69 | 90000 | 2.9112 | 0.9994 |
| 0.0159 | 678.2 | 90200 | 2.8274 | 0.9969 |
| 0.0185 | 679.7 | 90400 | 2.7524 | 0.9975 |
| 0.0123 | 681.2 | 90600 | 2.9673 | 0.9981 |
| 0.0152 | 682.71 | 90800 | 3.0797 | 1.0 |
| 0.0117 | 684.21 | 91000 | 3.0703 | 0.9994 |
| 0.0154 | 685.71 | 91200 | 2.7031 | 0.9987 |
| 0.0148 | 687.22 | 91400 | 2.9449 | 0.9994 |
| 0.0196 | 688.72 | 91600 | 2.7991 | 0.9981 |
| 0.0168 | 690.23 | 91800 | 2.8938 | 0.9969 |
| 0.0155 | 691.73 | 92000 | 2.9104 | 0.9975 |
| 0.0167 | 693.23 | 92200 | 2.7905 | 0.9975 |
| 0.0175 | 694.74 | 92400 | 3.0061 | 0.9962 |
| 0.0134 | 696.24 | 92600 | 3.1880 | 0.9975 |
| 0.0145 | 697.74 | 92800 | 3.1567 | 0.9975 |
| 0.0191 | 699.25 | 93000 | 3.2099 | 0.9981 |
| 0.0192 | 700.75 | 93200 | 3.0210 | 0.9962 |
| 0.0143 | 702.26 | 93400 | 3.0640 | 0.9962 |
| 0.0145 | 703.76 | 93600 | 2.9882 | 0.9956 |
| 0.0151 | 705.26 | 93800 | 2.9178 | 0.9969 |
| 0.0192 | 706.77 | 94000 | 2.9631 | 0.9962 |
| 0.016 | 708.27 | 94200 | 2.8724 | 0.9950 |
| 0.0185 | 709.77 | 94400 | 2.8253 | 1.0013 |
| 0.0186 | 711.28 | 94600 | 2.8386 | 0.9950 |
| 0.015 | 712.78 | 94800 | 2.8134 | 0.9987 |
| 0.0137 | 714.29 | 95000 | 3.1884 | 0.9969 |
| 0.0168 | 715.79 | 95200 | 2.9308 | 0.9969 |
| 0.0092 | 717.29 | 95400 | 3.4363 | 0.9981 |
| 0.0167 | 718.8 | 95600 | 3.2090 | 0.9981 |
| 0.0095 | 720.3 | 95800 | 3.1477 | 0.9981 |
| 0.0166 | 721.8 | 96000 | 2.9676 | 0.9981 |
| 0.0162 | 723.31 | 96200 | 3.0809 | 0.9969 |
| 0.0214 | 724.81 | 96400 | 3.3432 | 0.9987 |
| 0.0153 | 726.32 | 96600 | 3.0991 | 0.9981 |
| 0.0126 | 727.82 | 96800 | 3.0080 | 0.9975 |
| 0.009 | 729.32 | 97000 | 2.7920 | 0.9981 |
| 0.0194 | 730.83 | 97200 | 2.9781 | 0.9950 |
| 0.0159 | 732.33 | 97400 | 3.0364 | 0.9969 |
| 0.0175 | 733.83 | 97600 | 2.8637 | 0.9975 |
| 0.0131 | 735.34 | 97800 | 3.0862 | 0.9956 |
| 0.0222 | 736.84 | 98000 | 2.7875 | 0.9987 |
| 0.0107 | 738.35 | 98200 | 2.9837 | 0.9962 |
| 0.0171 | 739.85 | 98400 | 2.9214 | 0.9981 |
| 0.0162 | 741.35 | 98600 | 2.9194 | 0.9956 |
| 0.0133 | 742.86 | 98800 | 2.7002 | 1.0069 |
| 0.0201 | 744.36 | 99000 | 2.9725 | 0.9962 |
| 0.0137 | 745.86 | 99200 | 2.8473 | 0.9975 |
| 0.0081 | 747.37 | 99400 | 2.9228 | 0.9981 |
| 0.0155 | 748.87 | 99600 | 2.7624 | 0.9981 |
| 0.0127 | 750.38 | 99800 | 2.8617 | 1.0019 |
| 0.0158 | 751.88 | 100000 | 2.6715 | 1.0044 |
| 0.0146 | 753.38 | 100200 | 2.7292 | 1.0019 |
| 0.0136 | 754.89 | 100400 | 2.7341 | 0.9987 |
| 0.0177 | 756.39 | 100600 | 2.8756 | 1.0 |
| 0.0166 | 757.89 | 100800 | 2.7004 | 0.9981 |
| 0.0125 | 759.4 | 101000 | 2.6595 | 1.0013 |
| 0.012 | 760.9 | 101200 | 2.9655 | 1.0031 |
| 0.0075 | 762.41 | 101400 | 2.8493 | 0.9969 |
| 0.0161 | 763.91 | 101600 | 3.0451 | 0.9931 |
| 0.0159 | 765.41 | 101800 | 2.8029 | 0.9956 |
| 0.0179 | 766.92 | 102000 | 2.7643 | 0.9975 |
| 0.0123 | 768.42 | 102200 | 2.7163 | 0.9962 |
| 0.0131 | 769.92 | 102400 | 2.7235 | 1.0 |
| 0.0134 | 771.43 | 102600 | 2.7199 | 0.9944 |
| 0.0148 | 772.93 | 102800 | 3.0776 | 0.9962 |
| 0.0132 | 774.44 | 103000 | 2.8314 | 0.9987 |
| 0.0104 | 775.94 | 103200 | 2.8094 | 0.9969 |
| 0.0166 | 777.44 | 103400 | 2.8420 | 0.9956 |
| 0.0104 | 778.95 | 103600 | 2.7902 | 0.9944 |
| 0.0065 | 780.45 | 103800 | 3.0951 | 0.9962 |
| 0.0138 | 781.95 | 104000 | 2.9704 | 0.9956 |
| 0.0113 | 783.46 | 104200 | 2.7171 | 0.9994 |
| 0.0111 | 784.96 | 104400 | 3.2024 | 0.9975 |
| 0.0123 | 786.47 | 104600 | 3.1505 | 0.9956 |
| 0.0111 | 787.97 | 104800 | 3.1629 | 0.9969 |
| 0.0105 | 789.47 | 105000 | 3.4622 | 0.9975 |
| 0.0083 | 790.98 | 105200 | 3.0705 | 0.9987 |
| 0.0123 | 792.48 | 105400 | 3.1194 | 1.0 |
| 0.0156 | 793.98 | 105600 | 2.9915 | 0.9969 |
| 0.0171 | 795.49 | 105800 | 2.9393 | 0.9969 |
| 0.0144 | 796.99 | 106000 | 3.1108 | 0.9981 |
| 0.0126 | 798.5 | 106200 | 3.1683 | 0.9994 |
| 0.0101 | 800.0 | 106400 | 2.8538 | 1.0019 |
| 0.0166 | 801.5 | 106600 | 2.8343 | 0.9987 |
| 0.014 | 803.01 | 106800 | 3.2088 | 0.9969 |
| 0.0121 | 804.51 | 107000 | 2.9219 | 0.9987 |
| 0.012 | 806.02 | 107200 | 3.1733 | 0.9994 |
| 0.0116 | 807.52 | 107400 | 3.1237 | 1.0 |
| 0.0088 | 809.02 | 107600 | 2.9925 | 1.0 |
| 0.0119 | 810.53 | 107800 | 2.8498 | 1.0019 |
| 0.0128 | 812.03 | 108000 | 2.7667 | 0.9950 |
| 0.0099 | 813.53 | 108200 | 2.8768 | 0.9975 |
| 0.0135 | 815.04 | 108400 | 3.0824 | 0.9994 |
| 0.01 | 816.54 | 108600 | 3.0932 | 1.0006 |
| 0.0106 | 818.05 | 108800 | 3.1329 | 0.9975 |
| 0.0127 | 819.55 | 109000 | 3.0865 | 0.9962 |
| 0.01 | 821.05 | 109200 | 2.8887 | 0.9975 |
| 0.0156 | 822.56 | 109400 | 3.1740 | 0.9962 |
| 0.0131 | 824.06 | 109600 | 3.2385 | 0.9981 |
| 0.0081 | 825.56 | 109800 | 3.3594 | 0.9981 |
| 0.0138 | 827.07 | 110000 | 3.4093 | 0.9981 |
| 0.0103 | 828.57 | 110200 | 3.2142 | 0.9987 |
| 0.009 | 830.08 | 110400 | 3.1570 | 0.9981 |
| 0.0065 | 831.58 | 110600 | 3.0934 | 0.9987 |
| 0.0101 | 833.08 | 110800 | 2.7369 | 0.9975 |
| 0.0084 | 834.59 | 111000 | 2.9523 | 0.9994 |
| 0.0123 | 836.09 | 111200 | 2.9521 | 0.9987 |
| 0.0061 | 837.59 | 111400 | 2.9804 | 0.9962 |
| 0.0098 | 839.1 | 111600 | 3.0332 | 0.9969 |
| 0.0086 | 840.6 | 111800 | 2.9643 | 0.9956 |
| 0.008 | 842.11 | 112000 | 2.9080 | 0.9956 |
| 0.0074 | 843.61 | 112200 | 2.9878 | 0.9962 |
| 0.0125 | 845.11 | 112400 | 3.1683 | 0.9956 |
| 0.0112 | 846.62 | 112600 | 3.1810 | 1.0019 |
| 0.0093 | 848.12 | 112800 | 3.1701 | 0.9994 |
| 0.0133 | 849.62 | 113000 | 3.1736 | 1.0019 |
| 0.0093 | 851.13 | 113200 | 3.0622 | 0.9950 |
| 0.0085 | 852.63 | 113400 | 3.0084 | 0.9994 |
| 0.0119 | 854.14 | 113600 | 3.0510 | 1.0038 |
| 0.0132 | 855.64 | 113800 | 3.0081 | 0.9975 |
| 0.0068 | 857.14 | 114000 | 3.1519 | 0.9975 |
| 0.0103 | 858.65 | 114200 | 3.0568 | 0.9950 |
| 0.0116 | 860.15 | 114400 | 2.9386 | 0.9969 |
| 0.0098 | 861.65 | 114600 | 2.9005 | 0.9975 |
| 0.0102 | 863.16 | 114800 | 2.8415 | 0.9981 |
| 0.0061 | 864.66 | 115000 | 3.0904 | 0.9969 |
| 0.0111 | 866.17 | 115200 | 2.7751 | 1.0 |
| 0.011 | 867.67 | 115400 | 2.9156 | 1.0006 |
| 0.0159 | 869.17 | 115600 | 2.7727 | 1.0019 |
| 0.0093 | 870.68 | 115800 | 2.9028 | 1.0013 |
| 0.009 | 872.18 | 116000 | 2.9202 | 1.0 |
| 0.012 | 873.68 | 116200 | 3.0764 | 1.0006 |
| 0.0126 | 875.19 | 116400 | 3.0276 | 1.0031 |
| 0.0091 | 876.69 | 116600 | 3.1787 | 0.9987 |
| 0.0105 | 878.2 | 116800 | 3.0780 | 1.0 |
| 0.0088 | 879.7 | 117000 | 3.1704 | 1.0056 |
| 0.0075 | 881.2 | 117200 | 2.9814 | 1.0 |
| 0.0111 | 882.71 | 117400 | 2.9560 | 0.9987 |
| 0.0086 | 884.21 | 117600 | 2.9376 | 0.9994 |
| 0.0137 | 885.71 | 117800 | 3.1393 | 0.9981 |
| 0.0093 | 887.22 | 118000 | 3.0911 | 0.9994 |
| 0.0087 | 888.72 | 118200 | 3.1769 | 0.9987 |
| 0.0103 | 890.23 | 118400 | 3.2035 | 0.9969 |
| 0.0093 | 891.73 | 118600 | 3.0411 | 0.9981 |
| 0.0099 | 893.23 | 118800 | 3.1438 | 0.9975 |
| 0.0082 | 894.74 | 119000 | 3.0581 | 0.9975 |
| 0.0079 | 896.24 | 119200 | 3.2609 | 0.9981 |
| 0.0134 | 897.74 | 119400 | 3.0753 | 0.9975 |
| 0.012 | 899.25 | 119600 | 3.0611 | 0.9981 |
| 0.0127 | 900.75 | 119800 | 3.0978 | 0.9987 |
| 0.0092 | 902.26 | 120000 | 3.0483 | 0.9981 |
| 0.0107 | 903.76 | 120200 | 3.1183 | 0.9969 |
| 0.0104 | 905.26 | 120400 | 3.0339 | 1.0006 |
| 0.0066 | 906.77 | 120600 | 3.0664 | 1.0 |
| 0.0136 | 908.27 | 120800 | 3.2616 | 0.9987 |
| 0.0075 | 909.77 | 121000 | 3.2563 | 0.9994 |
| 0.0096 | 911.28 | 121200 | 3.2538 | 1.0019 |
| 0.0094 | 912.78 | 121400 | 3.4125 | 0.9994 |
| 0.0059 | 914.29 | 121600 | 3.2795 | 0.9981 |
| 0.011 | 915.79 | 121800 | 3.0787 | 0.9981 |
| 0.01 | 917.29 | 122000 | 3.0978 | 0.9962 |
| 0.0105 | 918.8 | 122200 | 3.3389 | 1.0006 |
| 0.0116 | 920.3 | 122400 | 3.3616 | 1.0006 |
| 0.0098 | 921.8 | 122600 | 3.3360 | 1.0 |
| 0.0091 | 923.31 | 122800 | 3.0145 | 1.0006 |
| 0.0134 | 924.81 | 123000 | 3.2691 | 0.9987 |
| 0.0124 | 926.32 | 123200 | 2.9881 | 1.0038 |
| 0.0093 | 927.82 | 123400 | 3.2461 | 0.9981 |
| 0.0077 | 929.32 | 123600 | 3.2159 | 0.9975 |
| 0.0088 | 930.83 | 123800 | 2.9951 | 1.0 |
| 0.0047 | 932.33 | 124000 | 3.1991 | 1.0 |
| 0.0078 | 933.83 | 124200 | 3.1098 | 0.9975 |
| 0.0066 | 935.34 | 124400 | 3.0344 | 0.9969 |
| 0.0108 | 936.84 | 124600 | 3.1163 | 0.9981 |
| 0.0086 | 938.35 | 124800 | 3.0668 | 0.9981 |
| 0.0071 | 939.85 | 125000 | 2.9860 | 0.9981 |
| 0.0096 | 941.35 | 125200 | 3.0639 | 0.9981 |
| 0.0066 | 942.86 | 125400 | 3.0273 | 0.9987 |
| 0.0071 | 944.36 | 125600 | 2.9351 | 0.9981 |
| 0.0084 | 945.86 | 125800 | 3.2054 | 0.9981 |
| 0.0099 | 947.37 | 126000 | 3.0305 | 1.0006 |
| 0.0084 | 948.87 | 126200 | 3.0412 | 1.0 |
| 0.0085 | 950.38 | 126400 | 2.9697 | 0.9994 |
| 0.0072 | 951.88 | 126600 | 2.9454 | 0.9956 |
| 0.007 | 953.38 | 126800 | 2.9225 | 0.9981 |
| 0.0068 | 954.89 | 127000 | 2.9063 | 0.9962 |
| 0.0121 | 956.39 | 127200 | 2.7681 | 0.9975 |
| 0.0063 | 957.89 | 127400 | 3.0608 | 0.9975 |
| 0.0155 | 959.4 | 127600 | 2.9355 | 0.9994 |
| 0.0098 | 960.9 | 127800 | 3.0073 | 0.9987 |
| 0.0083 | 962.41 | 128000 | 3.0921 | 0.9987 |
| 0.0042 | 963.91 | 128200 | 3.2363 | 0.9981 |
| 0.0083 | 965.41 | 128400 | 3.0678 | 0.9987 |
| 0.0097 | 966.92 | 128600 | 2.8499 | 1.0019 |
| 0.0095 | 968.42 | 128800 | 2.9329 | 1.0038 |
| 0.0058 | 969.92 | 129000 | 2.7835 | 1.0038 |
| 0.0052 | 971.43 | 129200 | 2.9134 | 1.0019 |
| 0.0081 | 972.93 | 129400 | 2.9436 | 1.0031 |
| 0.0084 | 974.44 | 129600 | 2.8237 | 0.9994 |
| 0.0069 | 975.94 | 129800 | 2.9216 | 0.9994 |
| 0.0089 | 977.44 | 130000 | 2.8717 | 0.9994 |
| 0.0074 | 978.95 | 130200 | 3.0350 | 0.9994 |
| 0.0072 | 980.45 | 130400 | 2.8856 | 1.0013 |
| 0.0103 | 981.95 | 130600 | 2.9572 | 1.0 |
| 0.01 | 983.46 | 130800 | 2.9522 | 1.0031 |
| 0.0071 | 984.96 | 131000 | 2.9940 | 1.0031 |
| 0.0042 | 986.47 | 131200 | 3.0343 | 1.0019 |
| 0.0048 | 987.97 | 131400 | 2.9638 | 1.0069 |
| 0.0038 | 989.47 | 131600 | 2.9672 | 1.0063 |
| 0.0043 | 990.98 | 131800 | 2.9115 | 1.0063 |
| 0.0129 | 992.48 | 132000 | 2.7689 | 1.0006 |
| 0.0059 | 993.98 | 132200 | 2.9322 | 1.0038 |
| 0.0098 | 995.49 | 132400 | 2.8063 | 1.0013 |
| 0.0083 | 996.99 | 132600 | 3.0642 | 0.9981 |
| 0.0055 | 998.5 | 132800 | 2.7167 | 1.0056 |
| 0.0073 | 1000.0 | 133000 | 2.7715 | 1.0075 |
| 0.0048 | 1001.5 | 133200 | 2.9235 | 1.0019 |
| 0.0148 | 1003.01 | 133400 | 3.0357 | 1.0031 |
| 0.0059 | 1004.51 | 133600 | 2.9261 | 1.0044 |
| 0.008 | 1006.02 | 133800 | 2.9927 | 0.9994 |
| 0.0093 | 1007.52 | 134000 | 2.9374 | 1.0019 |
| 0.0102 | 1009.02 | 134200 | 2.9598 | 0.9987 |
| 0.0045 | 1010.53 | 134400 | 3.0381 | 1.0 |
| 0.0058 | 1012.03 | 134600 | 3.0556 | 1.0 |
| 0.0089 | 1013.53 | 134800 | 3.1323 | 1.0006 |
| 0.0058 | 1015.04 | 135000 | 3.0611 | 1.0013 |
| 0.0043 | 1016.54 | 135200 | 3.0465 | 0.9981 |
| 0.004 | 1018.05 | 135400 | 3.1712 | 0.9962 |
| 0.0056 | 1019.55 | 135600 | 2.8690 | 0.9956 |
| 0.0098 | 1021.05 | 135800 | 3.0515 | 0.9987 |
| 0.0055 | 1022.56 | 136000 | 3.1504 | 0.9987 |
| 0.0054 | 1024.06 | 136200 | 3.0969 | 0.9969 |
| 0.005 | 1025.56 | 136400 | 3.0669 | 0.9969 |
| 0.0073 | 1027.07 | 136600 | 3.0097 | 0.9975 |
| 0.005 | 1028.57 | 136800 | 3.0517 | 0.9994 |
| 0.0076 | 1030.08 | 137000 | 3.0154 | 1.0 |
| 0.0084 | 1031.58 | 137200 | 2.9610 | 1.0025 |
| 0.0044 | 1033.08 | 137400 | 2.9468 | 0.9994 |
| 0.0063 | 1034.59 | 137600 | 3.0337 | 1.0006 |
| 0.0031 | 1036.09 | 137800 | 3.1654 | 1.0006 |
| 0.0066 | 1037.59 | 138000 | 3.0546 | 0.9987 |
| 0.0053 | 1039.1 | 138200 | 3.0273 | 1.0006 |
| 0.0086 | 1040.6 | 138400 | 3.0024 | 0.9962 |
| 0.0061 | 1042.11 | 138600 | 2.9791 | 0.9994 |
| 0.0057 | 1043.61 | 138800 | 3.1378 | 0.9994 |
| 0.0052 | 1045.11 | 139000 | 3.0045 | 0.9987 |
| 0.0094 | 1046.62 | 139200 | 2.9606 | 0.9994 |
| 0.0027 | 1048.12 | 139400 | 3.1745 | 0.9981 |
| 0.0024 | 1049.62 | 139600 | 3.1448 | 0.9981 |
| 0.0045 | 1051.13 | 139800 | 3.0968 | 1.0 |
| 0.0048 | 1052.63 | 140000 | 3.1278 | 0.9975 |
| 0.0079 | 1054.14 | 140200 | 3.2618 | 0.9969 |
| 0.0078 | 1055.64 | 140400 | 3.1333 | 0.9969 |
| 0.0077 | 1057.14 | 140600 | 3.0945 | 0.9987 |
| 0.0029 | 1058.65 | 140800 | 3.1253 | 0.9969 |
| 0.0022 | 1060.15 | 141000 | 3.0270 | 0.9969 |
| 0.0054 | 1061.65 | 141200 | 3.1937 | 0.9975 |
| 0.0097 | 1063.16 | 141400 | 2.9620 | 0.9987 |
| 0.006 | 1064.66 | 141600 | 2.9298 | 0.9994 |
| 0.0041 | 1066.17 | 141800 | 2.9171 | 1.0013 |
| 0.006 | 1067.67 | 142000 | 3.0870 | 0.9994 |
| 0.0059 | 1069.17 | 142200 | 2.9893 | 0.9987 |
| 0.0054 | 1070.68 | 142400 | 3.0031 | 0.9975 |
| 0.0076 | 1072.18 | 142600 | 2.9808 | 0.9969 |
| 0.0091 | 1073.68 | 142800 | 3.0710 | 0.9975 |
| 0.0081 | 1075.19 | 143000 | 3.2000 | 0.9956 |
| 0.0037 | 1076.69 | 143200 | 3.1321 | 0.9956 |
| 0.0042 | 1078.2 | 143400 | 3.0672 | 0.9950 |
| 0.0031 | 1079.7 | 143600 | 3.0094 | 0.9969 |
| 0.003 | 1081.2 | 143800 | 3.0956 | 0.9956 |
| 0.0087 | 1082.71 | 144000 | 3.0842 | 0.9969 |
| 0.0039 | 1084.21 | 144200 | 2.9722 | 0.9962 |
| 0.0059 | 1085.71 | 144400 | 3.0395 | 0.9962 |
| 0.0088 | 1087.22 | 144600 | 2.9895 | 0.9950 |
| 0.0056 | 1088.72 | 144800 | 2.9265 | 0.9956 |
| 0.0031 | 1090.23 | 145000 | 2.9205 | 0.9969 |
| 0.0089 | 1091.73 | 145200 | 2.9462 | 0.9969 |
| 0.0037 | 1093.23 | 145400 | 2.9021 | 0.9975 |
| 0.0047 | 1094.74 | 145600 | 2.9209 | 0.9987 |
| 0.0058 | 1096.24 | 145800 | 2.9616 | 0.9975 |
| 0.0068 | 1097.74 | 146000 | 2.9260 | 0.9981 |
| 0.0051 | 1099.25 | 146200 | 2.9815 | 0.9969 |
| 0.003 | 1100.75 | 146400 | 3.0032 | 0.9969 |
| 0.0055 | 1102.26 | 146600 | 2.9972 | 0.9975 |
| 0.0064 | 1103.76 | 146800 | 3.0282 | 0.9969 |
| 0.0045 | 1105.26 | 147000 | 3.0201 | 0.9969 |
| 0.0075 | 1106.77 | 147200 | 3.1340 | 0.9956 |
| 0.0052 | 1108.27 | 147400 | 3.1120 | 0.9956 |
| 0.0075 | 1109.77 | 147600 | 3.1448 | 0.9969 |
| 0.0053 | 1111.28 | 147800 | 3.1039 | 0.9981 |
| 0.0061 | 1112.78 | 148000 | 3.2061 | 1.0006 |
| 0.0105 | 1114.29 | 148200 | 3.1274 | 0.9994 |
| 0.0071 | 1115.79 | 148400 | 3.0449 | 1.0013 |
| 0.0042 | 1117.29 | 148600 | 3.1285 | 0.9994 |
| 0.0017 | 1118.8 | 148800 | 3.0261 | 0.9975 |
| 0.0083 | 1120.3 | 149000 | 3.0042 | 0.9944 |
| 0.0026 | 1121.8 | 149200 | 2.9851 | 0.9950 |
| 0.003 | 1123.31 | 149400 | 2.9439 | 0.9956 |
| 0.005 | 1124.81 | 149600 | 2.9231 | 0.9944 |
| 0.0039 | 1126.32 | 149800 | 2.9187 | 0.9944 |
| 0.0031 | 1127.82 | 150000 | 2.9250 | 0.9950 |
| 0.002 | 1129.32 | 150200 | 2.9452 | 0.9969 |
| 0.0057 | 1130.83 | 150400 | 2.9566 | 0.9944 |
| 0.0046 | 1132.33 | 150600 | 3.0111 | 0.9975 |
| 0.0076 | 1133.83 | 150800 | 2.9526 | 0.9962 |
| 0.0048 | 1135.34 | 151000 | 2.9313 | 0.9969 |
| 0.0052 | 1136.84 | 151200 | 2.9782 | 0.9975 |
| 0.0086 | 1138.35 | 151400 | 3.0282 | 0.9975 |
| 0.0018 | 1139.85 | 151600 | 3.0765 | 0.9962 |
| 0.0046 | 1141.35 | 151800 | 3.0391 | 0.9969 |
| 0.0043 | 1142.86 | 152000 | 3.0364 | 0.9962 |
| 0.006 | 1144.36 | 152200 | 3.0741 | 0.9975 |
| 0.0059 | 1145.86 | 152400 | 3.0201 | 0.9975 |
| 0.0038 | 1147.37 | 152600 | 3.0556 | 0.9969 |
| 0.007 | 1148.87 | 152800 | 2.9943 | 0.9962 |
| 0.0042 | 1150.38 | 153000 | 2.9979 | 0.9981 |
| 0.006 | 1151.88 | 153200 | 3.0304 | 0.9981 |
| 0.0063 | 1153.38 | 153400 | 2.9844 | 0.9981 |
| 0.0067 | 1154.89 | 153600 | 2.9773 | 0.9994 |
| 0.0037 | 1156.39 | 153800 | 2.9956 | 0.9994 |
| 0.0029 | 1157.89 | 154000 | 2.9977 | 1.0 |
| 0.0056 | 1159.4 | 154200 | 2.9639 | 0.9987 |
| 0.0063 | 1160.9 | 154400 | 2.9705 | 0.9994 |
| 0.0063 | 1162.41 | 154600 | 3.0182 | 0.9994 |
| 0.0045 | 1163.91 | 154800 | 3.0294 | 0.9994 |
| 0.0107 | 1165.41 | 155000 | 2.9982 | 1.0 |
| 0.0047 | 1166.92 | 155200 | 3.0119 | 1.0 |
| 0.0043 | 1168.42 | 155400 | 3.0040 | 1.0006 |
| 0.0025 | 1169.92 | 155600 | 3.0062 | 1.0 |
| 0.0044 | 1171.43 | 155800 | 2.9886 | 1.0006 |
| 0.0032 | 1172.93 | 156000 | 3.0112 | 1.0006 |
| 0.0056 | 1174.44 | 156200 | 3.0036 | 1.0006 |
| 0.0074 | 1175.94 | 156400 | 3.0185 | 1.0 |
| 0.0056 | 1177.44 | 156600 | 3.0197 | 1.0 |
| 0.0051 | 1178.95 | 156800 | 3.0232 | 0.9994 |
| 0.0034 | 1180.45 | 157000 | 3.0373 | 1.0 |
| 0.0038 | 1181.95 | 157200 | 3.0297 | 1.0 |
| 0.0058 | 1183.46 | 157400 | 3.0453 | 0.9994 |
| 0.0052 | 1184.96 | 157600 | 3.0235 | 1.0006 |
| 0.0023 | 1186.47 | 157800 | 3.0338 | 1.0006 |
| 0.0035 | 1187.97 | 158000 | 3.0402 | 1.0 |
| 0.0035 | 1189.47 | 158200 | 3.0333 | 1.0 |
| 0.0042 | 1190.98 | 158400 | 3.0572 | 0.9994 |
| 0.0049 | 1192.48 | 158600 | 3.0431 | 1.0 |
| 0.0017 | 1193.98 | 158800 | 3.0432 | 1.0 |
| 0.0089 | 1195.49 | 159000 | 3.0372 | 1.0 |
| 0.0089 | 1196.99 | 159200 | 3.0366 | 1.0006 |
| 0.0053 | 1198.5 | 159400 | 3.0345 | 1.0006 |
| 0.0054 | 1200.0 | 159600 | 3.0348 | 1.0006 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.11.6
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] |
huggingtweets/kc_lyricbot | 3506e514498e9bea6a8f66e7e959e481f57f8278 | 2022-04-13T21:14:37.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/kc_lyricbot | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/kc_lyricbot/1649884470723/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/1448393533921112064/q3fCXTyu_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">King Crimson Lyric Bot</div>
<div style="text-align: center; font-size: 14px;">@kc_lyricbot</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 King Crimson Lyric Bot.
| Data | King Crimson Lyric Bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 231 |
| Tweets kept | 3019 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yn81k4o/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 @kc_lyricbot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15ndpk6d) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15ndpk6d/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/kc_lyricbot')
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)
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kangela/Metaphor-FineTuned-BERT | a8be63c2a11e22afc7a455d43fa556bb9835da39 | 2022-05-19T17:44:32.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kangela | null | kangela/Metaphor-FineTuned-BERT | 0 | null | transformers | Entry not found | [
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huggan/projected_gan_color_field | 8d2331fc46c18dcd0b043f5ef20d049f1c8b61af | 2022-04-25T11:16:28.000Z | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation"
] | unconditional-image-generation | false | huggan | null | huggan/projected_gan_color_field | 0 | null | pytorch | ---
library_name: pytorch
tags:
- gan
- dcgan
- projected-gan
- huggan
- unconditional-image-generation
---
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matijević](https://huggingface.co/Cropinky)<br/>
[Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
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] |
huggan/projected_gan_popart | fc975cd4031baccc2d6fb37f8a9fe74438fc5f9d | 2022-04-25T11:16:48.000Z | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation"
] | unconditional-image-generation | false | huggan | null | huggan/projected_gan_popart | 0 | null | pytorch | ---
library_name: pytorch
tags:
- gan
- dcgan
- projected-gan
- huggan
- unconditional-image-generation
---
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matijević](https://huggingface.co/Cropinky)<br/>
[Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
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huggan/projected_gan_abstract_expressionism | 477e0e9cac181ed43f328397c60f0e94c40109c8 | 2022-04-25T11:16:38.000Z | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation"
] | unconditional-image-generation | false | huggan | null | huggan/projected_gan_abstract_expressionism | 0 | null | pytorch | ---
library_name: pytorch
tags:
- gan
- dcgan
- projected-gan
- huggan
- unconditional-image-generation
---
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matijević](https://huggingface.co/Cropinky)<br/>
[Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
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obokkkk/bert-base-multilingual-cased-finetuned-klue | cd6c21273c9d61dcbbcc5882631a6796394f5358 | 2022-04-14T12:57:25.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | obokkkk | null | obokkkk/bert-base-multilingual-cased-finetuned-klue | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-multilingual-cased-finetuned-klue
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-base-multilingual-cased-finetuned-klue
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4197
## 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
- gradient_accumulation_steps: 36
- total_train_batch_size: 288
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6323 | 5.0 | 500 | 1.6799 |
| 1.3765 | 10.0 | 1000 | 1.3027 |
| 0.8433 | 15.0 | 1500 | 1.2946 |
| 0.5224 | 20.0 | 2000 | 1.4197 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1
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lisdarr/fuck_shanghai_covid19 | 3dae4b2416f259632eb5d0ce2242b91defc50287 | 2022-04-14T04:27:57.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | lisdarr | null | lisdarr/fuck_shanghai_covid19 | 0 | 0 | transformers | ---
license: apache-2.0
---
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pitiwat/argument_wangchanberta | 06248b420a1bb15c048dd0a1b1a204d88a678e54 | 2022-04-14T11:35:08.000Z | [
"pytorch",
"camembert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | pitiwat | null | pitiwat/argument_wangchanberta | 0 | null | transformers | Used for extracting arguments from Thai text. | [
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TalTechNLP/voxlingua107-xls-r-300m-wav2vec | a96a1c36191f8b7a5676fec8228279318b9057a3 | 2022-04-19T11:08:36.000Z | [
"wav2vec2",
"multilingual",
"dataset:voxlingua107",
"speechbrain",
"language-identification",
"pytorch",
"embeddings",
"Language",
"Identification",
"audio-classification",
"wav2vec2.0",
"XLS-R-300M",
"VoxLingua107",
"license:cc-by-4.0"
] | audio-classification | false | TalTechNLP | null | TalTechNLP/voxlingua107-xls-r-300m-wav2vec | 0 | null | speechbrain | ---
language: multilingual
license: cc-by-4.0
tags:
- language-identification
- speechbrain
- wav2vec2
- pytorch
- embeddings
- Language
- Identification
- audio-classification
- wav2vec2.0
- XLS-R-300M
- VoxLingua107
datasets:
- voxlingua107
metrics:
- Accuracy
---
# VoxLingua107 Wav2Vec Spoken Language Identification Model
## Model description
This is a spoken language identification model trained on the VoxLingua107 dataset using SpeechBrain.
The model is trained using weights of pretrained [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model, Wav2Vec2.0 architecture and negative log likelihood loss.
The model can classify a speech utterance according to the language spoken.
It covers 107 different languages (
Abkhazian,
Afrikaans,
Amharic,
Arabic,
Assamese,
Azerbaijani,
Bashkir,
Belarusian,
Bulgarian,
Bengali,
Tibetan,
Breton,
Bosnian,
Catalan,
Cebuano,
Czech,
Welsh,
Danish,
German,
Greek,
English,
Esperanto,
Spanish,
Estonian,
Basque,
Persian,
Finnish,
Faroese,
French,
Galician,
Guarani,
Gujarati,
Manx,
Hausa,
Hawaiian,
Hindi,
Croatian,
Haitian,
Hungarian,
Armenian,
Interlingua,
Indonesian,
Icelandic,
Italian,
Hebrew,
Japanese,
Javanese,
Georgian,
Kazakh,
Central Khmer,
Kannada,
Korean,
Latin,
Luxembourgish,
Lingala,
Lao,
Lithuanian,
Latvian,
Malagasy,
Maori,
Macedonian,
Malayalam,
Mongolian,
Marathi,
Malay,
Maltese,
Burmese,
Nepali,
Dutch,
Norwegian Nynorsk,
Norwegian,
Occitan,
Panjabi,
Polish,
Pushto,
Portuguese,
Romanian,
Russian,
Sanskrit,
Scots,
Sindhi,
Sinhala,
Slovak,
Slovenian,
Shona,
Somali,
Albanian,
Serbian,
Sundanese,
Swedish,
Swahili,
Tamil,
Telugu,
Tajik,
Thai,
Turkmen,
Tagalog,
Turkish,
Tatar,
Ukrainian,
Urdu,
Uzbek,
Vietnamese,
Waray,
Yiddish,
Yoruba,
Mandarin Chinese).
## Intended uses & limitations
The model has two uses:
- use 'as is' for spoken language recognition
- use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data
The model is trained on automatically collected YouTube data. For more
information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/).
#### How to use
```python
import torchaudio
from speechbrain.pretrained.interfaces import foreign_class
language_id = foreign_class(source="TalTechNLP/voxlingua107-xls-r-300m-wav2vec", pymodule_file="encoder_wav2vec_classifier.py", classname="EncoderWav2vecClassifier", hparams_file='inference_wav2vec.yaml', savedir="tmp")
# Download Thai language sample from Omniglot and convert to suitable form
wav_file = "https://omniglot.com/soundfiles/udhr/udhr_th.mp3"
out_prob, score, index, text_lab = language_id.classify_file(wav_file)
print("probability:", out_prob)
print("label:", text_lab)
print("score:", score)
print("index:", index)
probability: tensor([[[-2.2849e+01, -2.4349e+01, -2.3686e+01, -2.3632e+01, -2.0218e+01,
-2.7241e+01, -2.6715e+01, -2.2301e+01, -2.6076e+01, -2.1716e+01,
-1.9923e+01, -2.7303e+01, -2.1211e+01, -2.2998e+01, -2.4436e+01,
-2.6437e+01, -2.2686e+01, -2.4244e+01, -2.0416e+01, -2.8329e+01,
-1.7788e+01, -2.4829e+01, -2.4186e+01, -2.7036e+01, -2.5993e+01,
-1.9677e+01, -2.2746e+01, -2.9192e+01, -2.4941e+01, -2.7135e+01,
-2.6653e+01, -2.2791e+01, -2.4599e+01, -2.1066e+01, -2.4855e+01,
-2.1874e+01, -2.2914e+01, -2.4174e+01, -2.0902e+01, -2.3197e+01,
-2.6108e+01, -2.3941e+01, -2.3103e+01, -2.2363e+01, -2.8969e+01,
-2.5302e+01, -2.4862e+01, -2.2392e+01, -2.4042e+01, -2.1221e+01,
-2.3656e+01, -2.1286e+01, -1.9209e+01, -2.3254e+01, -2.8291e+01,
-5.9105e+00, -2.4525e+01, -2.4937e+01, -2.8349e+01, -2.4420e+01,
-2.7439e+01, -2.6329e+01, -2.3317e+01, -2.3842e+01, -2.2114e+01,
-2.3637e+01, -1.7217e+01, -1.8342e+01, -2.4332e+01, -2.6090e+01,
-2.5452e+01, -2.3854e+01, -2.6082e+01, -2.4992e+01, -2.0618e+01,
-2.9351e+01, -2.4153e+01, -2.3156e+01, -2.6893e+01, -2.5314e+01,
-2.8374e+01, -2.4009e+01, -2.3604e+01, -2.4063e+01, -2.3538e+01,
-2.4953e+01, -2.5607e+01, -2.3960e+01, -2.6471e+01, -2.3348e+01,
-2.1681e+01, -2.7610e+01, -2.5023e+01, -2.3585e+01, -2.7146e-03,
-2.0338e+01, -1.8737e+01, -2.5158e+01, -2.7491e+01, -2.3623e+01,
-2.5718e+01, -2.3465e+01, -1.8305e+01, -2.1064e+01, -2.9880e+01,
-2.2809e+01, -1.9856e+01]]])
# The identified language ISO code is given in score[0][0]
label: [['th']]
score: tensor([[-0.0027]])
index: tensor([[94]])
# The scores in the out_prob tensor can be interpreted as log-likelihoods that
# the given utterance belongs to the given language (i.e., the larger the better)
# The linear-scale likelihood can be retrieved using the following:
print(score.exp())
tensor([0.9973])
# Alternatively, use the utterance embedding extractor:
signal, fs = torchaudio.load(wav_file)
embeddings = language_id.encode_batch(signal)
print(embeddings.shape)
torch.Size([2, 1, 2048])
```
#### Limitations and bias
Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are:
- Probably it's accuracy on smaller languages is quite limited
- Probably it works worse on female speech than male speech (because YouTube data includes much more male speech)
- Based on experiments, it performs satisfactory on accented speech
- Probably it doesn't work well on children's speech and on persons with speech disorders
## Training data
The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/).
VoxLingua107 is a speech dataset for training spoken language identification models.
The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours.
The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language.
## Training procedure
We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model.
Training recipe will be published soon.
## Evaluation results
| Version | Error Rate (%) |
|-----------------------|:------:|
| 2022-04-14 | 5.6 |
Error rate is calculated on VoxLingua107 development dataset.
### BibTeX entry and citation info
```bibtex
@inproceedings{valk2021slt,
title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
booktitle={Proc. IEEE SLT Workshop},
year={2021},
}
``` | [
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huggingtweets/elonmusk-joebiden | 67e9ad350fbad49e9485da977f5b374eb8218c6c | 2022-04-14T12:38:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/elonmusk-joebiden | 0 | null | transformers | ---
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/1503591435324563456/foUrqiEw_400x400.jpg')">
</div>
<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/1308769664240160770/AfgzWVE7_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>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Joe Biden</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-joebiden</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 Elon Musk & Joe Biden.
| Data | Elon Musk | Joe Biden |
| --- | --- | --- |
| Tweets downloaded | 200 | 3249 |
| Retweets | 15 | 571 |
| Short tweets | 60 | 34 |
| Tweets kept | 125 | 2644 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ne2s3c4/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 @elonmusk-joebiden's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ka86kb6l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ka86kb6l/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/elonmusk-joebiden')
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)
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] |
huggan/pix2pix-night2day | 150e94d5ccdecfb61a98bceee3962c981d175d2e | 2022-04-15T04:27:40.000Z | [
"pytorch",
"dataset:huggan/night2day",
"arxiv:1611.07004",
"huggan",
"gan",
"license:mit"
] | null | false | huggan | null | huggan/pix2pix-night2day | 0 | null | null | ---
tags:
- huggan
- gan
datasets:
- huggan/night2day
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
[Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
## Intended uses & limitations:
Used for reconstruction of images from edges
#### How to use
```python
from torchvision.transforms import Compose, Resize, ToTensor, Normalize
from PIL import Image
from torchvision.utils import save_image
import cv2
from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet
transform = Compose(
[
Resize((256, 256), Image.BICUBIC),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
model = GeneratorUNet.from_pretrained('huggan/pix2pix-night2day')
def predict_fn(img):
inp = transform(img).unsqueeze(0)
out = model(inp)
save_image(out, 'out.png', normalize=True)
return 'out.png'
predict_fn(img)
```
#### Limitations and bias
* Gives unrealistic colors in the image
* Gives Blurry image sometimes
## Training data
* [night2day](https://huggingface.co/datasets/huggan/night2day)
## Training procedure
```
# clone the repository
git clone https://github.com/huggingface/community-events.git
pip install .
# change directory
cd community-events/huggan/pytorch/pix2pix/
# define config
accelerate config
# launch training with required parameters
accelerate launch train.py --checkpoint_interval 5 --dataset huggan/night2day --push_to_hub --model_name pix2pix-night2day --batch_size 128 --n_epochs 50
```
## Generated Images
Here,
* First Image Row: Input Image
* Second Image Row: Generated Image
* Third Image Row: Target Image
### BibTeX entry and citation info
```bibtex
@article{pix2pix2017,
title={Image-to-Image Translation with Conditional Adversarial Networks},
author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
journal={CVPR},
year={2017}
}
``` | [
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lilitket/20220414-201056 | 5eff65cdccdf7e273cd2184df8cc210a9800439d | 2022-04-14T21:41:58.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220414-201056 | 0 | null | transformers | Entry not found | [
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lilitket/20220414-210228 | 5a8fff0ed7110a8cbb35c22d8bfb5bda86df4e08 | 2022-04-15T10:40:24.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220414-210228 | 0 | null | transformers | Entry not found | [
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huggan/projected_gan_impressionism | 87948de0e87971987fa5a0244884d6ae32f03ec4 | 2022-04-18T21:29:45.000Z | [
"pytorch"
] | null | false | huggan | null | huggan/projected_gan_impressionism | 0 | null | null | dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matijević](https://huggingface.co/Cropinky)<br/>
[Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
| [
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huggan/projected_gan_Hana_Hanak | df86ca72afc7bbd8956769fe14ac105d5dfbb076 | 2022-04-25T11:17:23.000Z | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation"
] | unconditional-image-generation | false | huggan | null | huggan/projected_gan_Hana_Hanak | 0 | null | pytorch | ---
library_name: pytorch
tags:
- gan
- dcgan
- projected-gan
- huggan
- unconditional-image-generation
---
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matijević](https://huggingface.co/Cropinky)<br/>
[Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
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huggan/projected_gan_color_field_hana | 7f952f515856c2dfd298cceeffd4f908c434aa10 | 2022-04-25T11:16:59.000Z | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation"
] | unconditional-image-generation | false | huggan | null | huggan/projected_gan_color_field_hana | 0 | null | pytorch | ---
library_name: pytorch
tags:
- gan
- dcgan
- projected-gan
- huggan
- unconditional-image-generation
---
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matijević](https://huggingface.co/Cropinky)<br/>
[Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
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] |
huggan/projected_gan_abstract_expressionism_hana | e43eb18a4185a4ea0e084e100c97466528b53fb6 | 2022-04-25T11:17:09.000Z | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation"
] | unconditional-image-generation | false | huggan | null | huggan/projected_gan_abstract_expressionism_hana | 0 | null | pytorch | ---
library_name: pytorch
tags:
- gan
- dcgan
- projected-gan
- huggan
- unconditional-image-generation
---
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matijević](https://huggingface.co/Cropinky)<br/>
[Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
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mT0/mt0_11B_t0_train_ckpt_1012500 | 91af79ba953686b099e9df3c4de7fc109984dcd8 | 2022-04-16T03:40:02.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mT0 | null | mT0/mt0_11B_t0_train_ckpt_1012500 | 0 | null | transformers | Entry not found | [
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ntoldalagi/nick_asr_LID | 061feb844ed87dd44535a706e1a809bc480c6c22 | 2022-04-21T10:47:23.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | ntoldalagi | null | ntoldalagi/nick_asr_LID | 0 | null | transformers | ---
tags:
- generated_from_trainer
model-index:
- name: nick_asr_LID
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. -->
# nick_asr_LID
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
- Cer: 1.0
## 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:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 50.7955 | 1.0 | 458 | 54.9678 | 1.0 | 1.0 |
| 29.3958 | 2.0 | 916 | 37.1618 | 0.9928 | 0.9887 |
| 27.1413 | 3.0 | 1374 | 32.5933 | 0.9856 | 0.9854 |
| 24.0847 | 4.0 | 1832 | 34.2804 | 0.9784 | 0.9447 |
| 492.7757 | 5.0 | 2290 | nan | 0.9736 | 0.9428 |
| 0.0 | 6.0 | 2748 | nan | 1.0 | 1.0 |
| 0.0 | 7.0 | 3206 | nan | 1.0 | 1.0 |
| 0.0 | 8.0 | 3664 | nan | 1.0 | 1.0 |
| 0.0 | 9.0 | 4122 | nan | 1.0 | 1.0 |
| 0.0 | 10.0 | 4580 | nan | 1.0 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
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mimicheng/codeparrot-ds-sample-2ep-14apr | a68a7876fa2c3a8e046a85485293025dff44b9ec | 2022-04-15T18:40:03.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | mimicheng | null | mimicheng/codeparrot-ds-sample-2ep-14apr | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds-sample-2ep-14apr
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. -->
# codeparrot-ds-sample-2ep-14apr
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6319
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: tpu
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.1367 | 0.37 | 1000 | 2.5592 |
| 2.2049 | 0.74 | 2000 | 2.0419 |
| 1.8302 | 1.11 | 3000 | 1.8245 |
| 1.6214 | 1.49 | 4000 | 1.6913 |
| 1.5125 | 1.86 | 5000 | 1.6319 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
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ntoldalagi/nick_asr_COMBO | 9af031677926de377eda1c7af586fc88f26b1794 | 2022-04-24T01:24:22.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"transformers",
"generated_from_trainer",
"model-index"
] | null | false | ntoldalagi | null | ntoldalagi/nick_asr_COMBO | 0 | null | transformers | ---
tags:
- generated_from_trainer
model-index:
- name: nick_asr_COMBO
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. -->
# nick_asr_COMBO
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4313
- Wer: 0.6723
- Cer: 0.2408
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.0857 | 1.0 | 687 | 1.3883 | 0.7082 | 0.2576 |
| 0.0627 | 2.0 | 1374 | 1.4099 | 0.7076 | 0.2561 |
| 0.0697 | 3.0 | 2061 | 1.3864 | 0.6906 | 0.2486 |
| 0.0575 | 4.0 | 2748 | 1.4356 | 0.6906 | 0.2455 |
| 0.0552 | 5.0 | 3435 | 1.4061 | 0.6778 | 0.2440 |
| 0.0631 | 6.0 | 4122 | 1.4541 | 0.6839 | 0.2444 |
| 0.0418 | 7.0 | 4809 | 1.4258 | 0.6930 | 0.2465 |
| 0.0407 | 8.0 | 5496 | 1.4193 | 0.6809 | 0.2451 |
| 0.0487 | 9.0 | 6183 | 1.4261 | 0.6778 | 0.2424 |
| 0.0371 | 10.0 | 6870 | 1.4313 | 0.6723 | 0.2408 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
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Cristianoo/nezha-large-zh | 3dd8e95e7311ad9b8cbdb83f4ea04de1f63aa9c2 | 2022-04-15T07:22:08.000Z | [
"pytorch"
] | null | false | Cristianoo | null | Cristianoo/nezha-large-zh | 0 | null | null | Entry not found | [
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tau/false_large_t5_single_mask_5_1024_0.3_epoch1 | 9287fecd54a3f903ebe1c89ff33d20a10dde3b13 | 2022-04-15T07:47:45.000Z | [
"pytorch",
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"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/false_large_t5_single_mask_5_1024_0.3_epoch1 | 0 | null | transformers | Entry not found | [
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tau/false_large_random_paraNone_sentNone_span0_multi_masks_5_1024_0.3_epoch1 | 48b4581e39f8d6672827e3c54a0a70cbea16b1fa | 2022-04-15T08:21:14.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
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] | text2text-generation | false | tau | null | tau/false_large_random_paraNone_sentNone_span0_multi_masks_5_1024_0.3_epoch1 | 0 | null | transformers | Entry not found | [
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masakhane/afrimt5_fr_ewe_news | cc8f256e3e6ca0aadc5f756993888350e6d9734c | 2022-04-15T09:01:20.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimt5_fr_ewe_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/afrimt5_ewe_fr_news | e684c873016b389e702c7eb6649111e74bd7f5a3 | 2022-04-15T09:01:24.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimt5_ewe_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/afrimbart_fr_ewe_news | bd4f6a98462ff632ffe131c86239ebff041ce6aa | 2022-04-15T09:01:37.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afrimbart_fr_ewe_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/afribyt5_ewe_fr_news | b4cb039988fab88d399b322c0c9175001ea4af76 | 2022-04-15T10:06:29.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afribyt5_ewe_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/afribyt5_fr_ewe_news | b156c367f02e69be1ecf61df61cca4d3564d622c | 2022-04-15T10:06:25.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/afribyt5_fr_ewe_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/byt5_fr_ewe_news | 0fa6efaa879d2a909e5f850a7335ee435877f15d | 2022-04-15T10:06:33.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/byt5_fr_ewe_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/byt5_ewe_fr_news | e9bb988159362cdcab353a392a4177d52a98c611 | 2022-04-15T10:06:37.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/byt5_ewe_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/mt5_fr_ewe_news | 666d3974bddc414c547a345fbae0f768520fa35c | 2022-04-15T11:19:46.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mt5_fr_ewe_news | 0 | null | transformers | ---
license: afl-3.0
---
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] |
masakhane/mt5_ewe_fr_news | 1996364930ac66520871602c9640fb8ddfe21f8e | 2022-04-15T11:19:38.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mt5_ewe_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/mbart50_fr_ewe_news | 6e559e03c4f762b5189b77c1c99847ec4c91a622 | 2022-04-15T11:19:26.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mbart50_fr_ewe_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/mbart50_ewe_fr_news | 535139d1b9482217748159aeb1242b58d018e976 | 2022-04-15T11:19:33.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mbart50_ewe_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_ewe_fr_news | 404bb64c3207a0a5377c1a08c484180aa94e10ab | 2022-04-15T13:28:00.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_ewe_fr_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_ewe_fr_rel_news | cbaeff3d677296bad068633176cabfba44c61a80 | 2022-04-15T13:28:09.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_ewe_fr_rel_news | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_fr_ewe_rel_news_ft | 43c7d60c1276258d5184f899d5dd6f5bbb5d251a | 2022-04-15T16:28:07.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_fr_ewe_rel_news_ft | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_ewe_fr_rel_news_ft | 25080abbe934676a34fe86a0efd01408ca6c53ec | 2022-04-15T16:27:42.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_ewe_fr_rel_news_ft | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_ewe_fr_rel_ft | a08335a51e2e84fa37eae59410fd80d31a27bbf8 | 2022-04-15T16:28:00.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_ewe_fr_rel_ft | 0 | null | transformers | ---
license: afl-3.0
---
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masakhane/m2m100_418M_ewe_fr_rel | 36fc61609692fa8fd4a60502a12fdb7d8acfad90 | 2022-04-15T17:39:03.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_ewe_fr_rel | 0 | null | transformers | ---
license: afl-3.0
---
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Subsets and Splits