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Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch
|
Ameer05
| 2022-03-08T05:53:14Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-08T05:33:06Z |
---
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch
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. -->
# bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch
This model is a fine-tuned version of [Ameer05/model-token-repo](https://huggingface.co/Ameer05/model-token-repo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5216
- Rouge1: 59.5791
- Rouge2: 51.3273
- Rougel: 56.9984
- Rougelsum: 59.1424
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 0.91 | 5 | 2.0124 | 53.776 | 46.7427 | 50.7565 | 53.5502 |
| No log | 1.91 | 10 | 1.6353 | 61.8019 | 53.8614 | 58.9744 | 61.339 |
| No log | 2.91 | 15 | 1.5321 | 59.7045 | 51.5968 | 57.0823 | 59.2417 |
| No log | 3.91 | 20 | 1.4569 | 62.4379 | 54.5464 | 59.9202 | 61.9242 |
| 1.5608 | 4.91 | 25 | 1.4613 | 63.3808 | 55.8818 | 61.432 | 63.0208 |
| 1.5608 | 5.91 | 30 | 1.4321 | 59.6761 | 50.9812 | 56.7977 | 59.1214 |
| 1.5608 | 6.91 | 35 | 1.4753 | 62.6439 | 54.7158 | 60.3831 | 62.1046 |
| 1.5608 | 7.91 | 40 | 1.4783 | 60.2735 | 52.7462 | 57.77 | 59.9725 |
| 0.6428 | 8.91 | 45 | 1.4974 | 62.8691 | 54.9062 | 60.3496 | 62.5132 |
| 0.6428 | 9.91 | 50 | 1.5216 | 59.5791 | 51.3273 | 56.9984 | 59.1424 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.10.3
|
willcai/wav2vec2-large-xls-r-300m-tr-colab
|
willcai
| 2022-03-08T03:06:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-05T22:48:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-tr-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tr-colab
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: 0.4121
- Wer: 0.3112
## 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
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.1868 | 1.83 | 400 | 0.9812 | 0.8398 |
| 0.691 | 3.67 | 800 | 0.5571 | 0.6298 |
| 0.3555 | 5.5 | 1200 | 0.4676 | 0.4779 |
| 0.2451 | 7.34 | 1600 | 0.4572 | 0.4541 |
| 0.1844 | 9.17 | 2000 | 0.4743 | 0.4389 |
| 0.1541 | 11.01 | 2400 | 0.4583 | 0.4300 |
| 0.1277 | 12.84 | 2800 | 0.4565 | 0.3950 |
| 0.1122 | 14.68 | 3200 | 0.4761 | 0.4087 |
| 0.0975 | 16.51 | 3600 | 0.4654 | 0.3786 |
| 0.0861 | 18.35 | 4000 | 0.4503 | 0.3667 |
| 0.0775 | 20.18 | 4400 | 0.4600 | 0.3581 |
| 0.0666 | 22.02 | 4800 | 0.4350 | 0.3504 |
| 0.0627 | 23.85 | 5200 | 0.4211 | 0.3349 |
| 0.0558 | 25.69 | 5600 | 0.4390 | 0.3333 |
| 0.0459 | 27.52 | 6000 | 0.4218 | 0.3185 |
| 0.0439 | 29.36 | 6400 | 0.4121 | 0.3112 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
gayanin/t5-small-paraphrasing-mlm
|
gayanin
| 2022-03-08T01:54:54Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T21:54:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-paraphrasing-mlm
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. -->
# t5-small-paraphrasing-mlm
This model is a fine-tuned version of [gayanin/t5-small-paraphrase-pubmed](https://huggingface.co/gayanin/t5-small-paraphrase-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7030
- Rouge2 Precision: 0.6576
- Rouge2 Recall: 0.4712
- Rouge2 Fmeasure: 0.532
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.9215 | 1.0 | 13833 | 0.8050 | 0.6352 | 0.454 | 0.5131 |
| 0.855 | 2.0 | 27666 | 0.7679 | 0.6411 | 0.4589 | 0.5184 |
| 0.8387 | 3.0 | 41499 | 0.7464 | 0.6464 | 0.4626 | 0.5226 |
| 0.8267 | 4.0 | 55332 | 0.7315 | 0.6513 | 0.4671 | 0.5273 |
| 0.7879 | 5.0 | 69165 | 0.7217 | 0.6534 | 0.4687 | 0.529 |
| 0.7738 | 6.0 | 82998 | 0.7142 | 0.6548 | 0.4688 | 0.5295 |
| 0.7793 | 7.0 | 96831 | 0.7094 | 0.6553 | 0.4694 | 0.53 |
| 0.7654 | 8.0 | 110664 | 0.7056 | 0.6573 | 0.4704 | 0.5313 |
| 0.7675 | 9.0 | 124497 | 0.7036 | 0.6577 | 0.4712 | 0.532 |
| 0.7662 | 10.0 | 138330 | 0.7030 | 0.6576 | 0.4712 | 0.532 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
gayanin/bart-paraphrasing-mlm
|
gayanin
| 2022-03-07T21:40:56Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T14:50:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-paraphrasing-mlm
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. -->
# bart-paraphrasing-mlm
This model is a fine-tuned version of [gayanin/bart-paraphrase-pubmed-1.1](https://huggingface.co/gayanin/bart-paraphrase-pubmed-1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5510
- Rouge2 Precision: 0.7148
- Rouge2 Recall: 0.5223
- Rouge2 Fmeasure: 0.5866
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6799 | 1.0 | 13833 | 0.5982 | 0.7016 | 0.5122 | 0.5756 |
| 0.5894 | 2.0 | 27666 | 0.5663 | 0.7093 | 0.5193 | 0.583 |
| 0.5329 | 3.0 | 41499 | 0.5540 | 0.7129 | 0.5212 | 0.5853 |
| 0.4953 | 4.0 | 55332 | 0.5510 | 0.7148 | 0.5223 | 0.5866 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
Manauu17/roberta_sentiments_es
|
Manauu17
| 2022-03-07T20:10:33Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-03T13:50:56Z |
# roberta_sentiments_es , a Sentiment Analysis model for Spanish sentences
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences
## Example of classification
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
import pandas as pd
from scipy.special import softmax
MODEL = 'Manauu17/roberta_sentiments_es_en'
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# PyTorch
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
text = ['@usuario siempre es bueno la opinión de un playo',
'Bendito año el que me espera']
encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
output = model(**encoded_input)
scores = output[0].detach().numpy()
# TensorFlow
model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
text = ['La guerra no es buena para nadie.','Espero que mi jefe me de mañana libre']
encoded_input = tokenizer(text, return_tensors='tf', padding=True, truncation=True)
output = model(encoded_input)
scores = output[0].numpy()
# Results
def get_scores(model_output, labels_dict):
scores = softmax(model_output)
frame = pd.DataFrame(scores, columns=labels.values())
frame.style.highlight_max(axis=1,color="green")
return frame
```
Output:
```
# PyTorch
get_scores(scores, labels_dict).style.highlight_max(axis=1, color="green")
Negative Neutral Positive
0 0.000607 0.004851 0.906596
1 0.079812 0.006650 0.001484
# TensorFlow
get_scores(scores, labels_dict).style.highlight_max(axis=1, color="green")
Negative Neutral Positive
0 0.017030 0.008920 0.000667
1 0.000260 0.001695 0.971429
```
|
espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer
|
espnet
| 2022-03-07T19:38:16Z | 1 | 0 |
espnet
|
[
"espnet",
"tensorboard",
"audio",
"automatic-speech-recognition",
"en",
"dataset:sinhala",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-07T16:09:26Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- sinhala
license: cc-by-4.0
---
## ESPnet2 ASR pretrained model
### `espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer`
This model was trained by Karthik using DSTC2/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
pyf98/librispeech_conformer_layerdrop0.1_last6
|
pyf98
| 2022-03-07T18:40:56Z | 2 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-07T18:37:56Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/librispeech_conformer_layerdrop0.1_last6`
This model was trained by Yifan Peng using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout c3569453a408fd4ff4173d9c1d2062c88d1fc060
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/librispeech_conformer_layerdrop0.1_last6
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Mar 7 12:21:40 EST 2022`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `c3569453a408fd4ff4173d9c1d2062c88d1fc060`
- Commit date: `Sun Mar 6 23:58:36 2022 -0500`
## asr_train_asr_conformer9_layerdrop0.1_last6_raw_en_bpe5000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam60_ctc0.2/dev_clean|2703|54402|98.0|1.8|0.2|0.2|2.2|26.5|
|beam60_ctc0.2/dev_other|2864|50948|95.4|4.2|0.4|0.5|5.1|43.3|
|beam60_ctc0.2/test_clean|2620|52576|97.9|1.9|0.2|0.3|2.4|27.9|
|beam60_ctc0.2/test_other|2939|52343|95.2|4.3|0.5|0.6|5.4|45.4|
|beam60_ctc0.2_lm0.6/dev_clean|2703|54402|98.2|1.5|0.3|0.2|2.0|23.7|
|beam60_ctc0.2_lm0.6/dev_other|2864|50948|96.3|3.2|0.5|0.4|4.1|36.5|
|beam60_ctc0.2_lm0.6/test_clean|2620|52576|98.1|1.6|0.3|0.2|2.1|24.0|
|beam60_ctc0.2_lm0.6/test_other|2939|52343|96.0|3.4|0.6|0.5|4.4|40.5|
|beam60_ctc0.3/dev_clean|2703|54402|98.1|1.8|0.2|0.2|2.1|26.6|
|beam60_ctc0.3/dev_other|2864|50948|95.4|4.2|0.4|0.5|5.1|43.3|
|beam60_ctc0.3/test_clean|2620|52576|97.9|1.9|0.2|0.3|2.4|28.1|
|beam60_ctc0.3/test_other|2939|52343|95.3|4.3|0.4|0.7|5.4|45.7|
|beam60_ctc0.3_lm0.6/dev_clean|2703|54402|98.4|1.4|0.2|0.2|1.8|23.3|
|beam60_ctc0.3_lm0.6/dev_other|2864|50948|96.4|3.2|0.4|0.4|4.0|36.5|
|beam60_ctc0.3_lm0.6/test_clean|2620|52576|98.2|1.6|0.2|0.2|2.0|23.7|
|beam60_ctc0.3_lm0.6/test_other|2939|52343|96.2|3.4|0.5|0.5|4.3|40.4|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam60_ctc0.2/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|26.5|
|beam60_ctc0.2/dev_other|2864|265951|98.3|1.0|0.7|0.6|2.3|43.3|
|beam60_ctc0.2/test_clean|2620|281530|99.4|0.3|0.3|0.2|0.8|27.9|
|beam60_ctc0.2/test_other|2939|272758|98.3|1.0|0.7|0.6|2.3|45.4|
|beam60_ctc0.2_lm0.6/dev_clean|2703|288456|99.3|0.3|0.4|0.2|0.9|23.7|
|beam60_ctc0.2_lm0.6/dev_other|2864|265951|98.4|0.9|0.8|0.5|2.1|36.5|
|beam60_ctc0.2_lm0.6/test_clean|2620|281530|99.4|0.3|0.4|0.2|0.8|24.0|
|beam60_ctc0.2_lm0.6/test_other|2939|272758|98.4|0.8|0.8|0.5|2.1|40.5|
|beam60_ctc0.3/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.7|26.6|
|beam60_ctc0.3/dev_other|2864|265951|98.3|1.0|0.7|0.6|2.3|43.3|
|beam60_ctc0.3/test_clean|2620|281530|99.5|0.3|0.3|0.2|0.8|28.1|
|beam60_ctc0.3/test_other|2939|272758|98.4|1.0|0.7|0.6|2.3|45.7|
|beam60_ctc0.3_lm0.6/dev_clean|2703|288456|99.5|0.3|0.3|0.2|0.7|23.3|
|beam60_ctc0.3_lm0.6/dev_other|2864|265951|98.5|0.8|0.7|0.5|1.9|36.5|
|beam60_ctc0.3_lm0.6/test_clean|2620|281530|99.5|0.2|0.3|0.2|0.7|23.7|
|beam60_ctc0.3_lm0.6/test_other|2939|272758|98.5|0.7|0.7|0.5|2.0|40.4|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam60_ctc0.2/dev_clean|2703|68010|97.5|1.8|0.7|0.4|2.8|26.5|
|beam60_ctc0.2/dev_other|2864|63110|94.2|4.3|1.5|0.8|6.6|43.3|
|beam60_ctc0.2/test_clean|2620|65818|97.4|1.8|0.8|0.3|3.0|27.9|
|beam60_ctc0.2/test_other|2939|65101|94.1|4.1|1.8|0.8|6.7|45.4|
|beam60_ctc0.2_lm0.6/dev_clean|2703|68010|97.7|1.5|0.8|0.3|2.6|23.7|
|beam60_ctc0.2_lm0.6/dev_other|2864|63110|95.1|3.4|1.5|0.6|5.5|36.5|
|beam60_ctc0.2_lm0.6/test_clean|2620|65818|97.6|1.5|0.9|0.3|2.7|24.0|
|beam60_ctc0.2_lm0.6/test_other|2939|65101|94.8|3.3|1.9|0.6|5.7|40.5|
|beam60_ctc0.3/dev_clean|2703|68010|97.6|1.7|0.7|0.3|2.7|26.6|
|beam60_ctc0.3/dev_other|2864|63110|94.2|4.3|1.5|0.8|6.6|43.3|
|beam60_ctc0.3/test_clean|2620|65818|97.4|1.8|0.8|0.3|2.9|28.1|
|beam60_ctc0.3/test_other|2939|65101|94.2|4.1|1.7|0.8|6.6|45.7|
|beam60_ctc0.3_lm0.6/dev_clean|2703|68010|97.9|1.4|0.7|0.3|2.4|23.3|
|beam60_ctc0.3_lm0.6/dev_other|2864|63110|95.2|3.4|1.5|0.6|5.5|36.5|
|beam60_ctc0.3_lm0.6/test_clean|2620|65818|97.7|1.5|0.8|0.3|2.6|23.7|
|beam60_ctc0.3_lm0.6/test_other|2939|65101|95.0|3.2|1.8|0.6|5.6|40.4|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer9_layerdrop0.1_last6.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer9_layerdrop0.1_last6_raw_en_bpe5000_sp
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 3
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 53189
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 35000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_960_sp/wav.scp
- speech
- sound
- - dump/raw/train_960_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- sound
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0025
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 40000
token_list:
- <blank>
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- ▁BE
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- ▁ROOM
- ▁YET
- ▁SAME
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- ▁FATHER
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- ▁THOUGH
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- ▁HEART
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- ▁E
- ▁PART
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- ▁TOLD
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- H
- TON
- MENT
- ▁OH
- NE
- Z
- LING
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- TER
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- ▁QUITE
- ▁WANT
- ▁YEARS
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- ENT
- ▁HALF
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- ▁WHOLE
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- ▁TURNED
- ▁GAVE
- ▁C
- ▁POOR
- MAN
- UT
- NA
- ▁DEAR
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- ▁GIRL
- ▁MORNING
- ▁BETWEEN
- LED
- ▁NOR
- IA
- ▁AMONG
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- ▁SMALL
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- ▁WHOM
- ▁FELT
- ▁HANDS
- ▁MYSELF
- ▁HIGH
- ▁M
- ▁HOWEVER
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- ▁P
- CO
- ▁STOOD
- ID
- ▁KIND
- ▁HUNDRED
- AS
- ▁ROUND
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- TY
- ▁SINCE
- ▁G
- AM
- ▁LA
- SE
- ▁BOY
- ▁MA
- ▁PERHAPS
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- ▁TILL
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- TE
- ▁FACT
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- ▁POWER
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- ▁WORD
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- KE
- ▁INDEED
- TING
- ▁MATTER
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- ▁HOPE
- ▁DA
- ▁OPEN
- ▁FEET
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- OM
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- ▁B
- ▁CL
- ME
- ▁GONE
- ▁CHILD
- INE
- GG
- ▁BEST
- ▁DIS
- UM
- ▁HARD
- ▁LORD
- OUS
- ▁WIFE
- ▁SURE
- ▁FORM
- DE
- ▁DEATH
- ANT
- ▁NATURE
- ▁BA
- ▁CARE
- ▁BELIEVE
- PP
- ▁NEAR
- ▁RO
- ▁RED
- ▁WAR
- IE
- ▁SPEAK
- ▁FEAR
- ▁CASE
- ▁TAKEN
- ▁ALONG
- ▁CANNOT
- ▁HEAR
- ▁THEMSELVES
- CI
- ▁PRESENT
- AD
- ▁MASTER
- ▁SON
- ▁THUS
- ▁LI
- ▁LESS
- ▁SUN
- ▁TRUE
- IM
- IOUS
- ▁THOUSAND
- ▁MONEY
- ▁W
- ▁BEHIND
- ▁CHILDREN
- ▁DOCTOR
- AC
- ▁TWENTY
- ▁WISH
- ▁SOUND
- ▁WHOSE
- ▁LEAVE
- ▁ANSWERED
- ▁THOU
- ▁DUR
- ▁HA
- ▁CERTAIN
- ▁PO
- ▁PASSED
- GE
- TO
- ▁ARM
- ▁LO
- ▁STATE
- ▁ALONE
- TA
- ▁SHOW
- ▁NEED
- ▁LIVE
- ND
- ▁DEAD
- ENCE
- ▁STRONG
- ▁PRE
- ▁TI
- ▁GROUND
- SH
- TI
- ▁SHORT
- IAN
- UN
- ▁PRO
- ▁HORSE
- MI
- ▁PRINCE
- ARD
- ▁FELL
- ▁ORDER
- ▁CALL
- AT
- ▁GIVEN
- ▁DARK
- ▁THEREFORE
- ▁CLOSE
- ▁BODY
- ▁OTHERS
- ▁SENT
- ▁SECOND
- ▁OFTEN
- ▁CA
- ▁MANNER
- MO
- NI
- ▁BRING
- ▁QUESTION
- ▁HOUR
- ▁BO
- AGE
- ▁ST
- ▁TURN
- ▁TABLE
- ▁GENERAL
- ▁EARTH
- ▁BED
- ▁REALLY
- ▁SIX
- 'NO'
- IST
- ▁BECOME
- ▁USE
- ▁READ
- ▁SE
- ▁VI
- ▁COMING
- ▁EVERYTHING
- ▁EM
- ▁ABOVE
- ▁EVENING
- ▁BEAUTIFUL
- ▁FEEL
- ▁RAN
- ▁LEAST
- ▁LAW
- ▁ALREADY
- ▁MEAN
- ▁ROSE
- WARD
- ▁ITSELF
- ▁SOUL
- ▁SUDDENLY
- ▁AROUND
- RED
- ▁ANSWER
- ICAL
- ▁RA
- ▁WIND
- ▁FINE
- ▁WON
- ▁WHETHER
- ▁KNOWN
- BER
- NG
- ▁TA
- ▁CAPTAIN
- ▁EYE
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- ▁RETURNED
- ▁STRANGE
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- ▁EITHER
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- ▁DOUBT
- ▁YEAR
- ▁CLEAR
- ▁SIGHT
- ▁GRA
- ▁LOST
- ▁KEPT
- ▁F
- PE
- ▁BAR
- ▁TOWN
- ▁SLEEP
- ARY
- ▁HAIR
- ▁FRIENDS
- ▁DREAM
- ▁FELLOW
- PER
- ▁DEEP
- QUE
- ▁BECAME
- ▁REAL
- ▁PAST
- ▁MAKING
- RING
- ▁COMP
- ▁ACT
- ▁BAD
- HO
- STER
- ▁YE
- ▁MEANS
- ▁RUN
- MEN
- ▁DAUGHTER
- ▁SENSE
- ▁CITY
- ▁SOMETIMES
- ▁TOWARDS
- ▁ROAD
- ▁SP
- ▁LU
- ▁READY
- ▁FOOT
- ▁COLD
- ▁SA
- ▁LETTER
- ▁ELSE
- ▁MAR
- ▁STA
- BE
- ▁TRUTH
- ▁LE
- BO
- ▁BUSINESS
- CHE
- ▁JOHN
- ▁SUBJECT
- ▁COURT
- ▁IDEA
- ILY
- ▁RIVER
- ATING
- ▁FAMILY
- HE
- ▁DIDN
- ▁GLAD
- ▁SEVERAL
- IAL
- ▁UNDERSTAND
- ▁SC
- ▁POSSIBLE
- ▁DIFFERENT
- ▁RETURN
- ▁ARMS
- ▁LOW
- ▁HOLD
- ▁TALK
- ▁RU
- ▁WINDOW
- ▁INTEREST
- ▁SISTER
- SON
- ▁SH
- ▁BLOOD
- ▁SAYS
- ▁CAP
- ▁DI
- ▁HUMAN
- ▁CAUSE
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- ▁THANK
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- GO
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- ▁ACROSS
- ▁STORY
- NT
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- ▁AGE
- ▁TAKING
- ▁LINE
- ▁SEEM
- ▁HAPPY
- LAND
- EM
- ▁STAY
- ▁PLAY
- ▁COMMON
- ▁GA
- ▁BOOK
- ▁TIMES
- ▁OBJECT
- ▁SEVEN
- QUI
- DO
- UND
- ▁FL
- ▁PRETTY
- ▁FAIR
- WAY
- ▁WOOD
- ▁REACHED
- ▁APPEARED
- ▁SWEET
- ▁FALL
- BA
- ▁PASS
- ▁SIGN
- ▁TREE
- IONS
- ▁GARDEN
- ▁ILL
- ▁ART
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- ▁OPENED
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- ▁STREET
- ▁TROUBLE
- ▁PAIN
- ▁CONTINUED
- ▁SCHOOL
- OUR
- ▁CARRIED
- ▁SAYING
- HA
- ▁CHANGE
- ▁FOLLOW
- ▁GOLD
- ▁SW
- ▁FEELING
- ▁COMMAND
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- ▁BLUE
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- CA
- ▁WILD
- ▁ACCOUNT
- ▁OUGHT
- UD
- ▁T
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- ▁WANTED
- ▁RI
- ▁HEAVEN
- ▁PURPOSE
- ▁CHARACTER
- ▁RICH
- ▁PE
- ▁DRESS
- OS
- FA
- ▁TH
- ▁ENGLISH
- ▁CHANCE
- ▁SHIP
- ▁VIEW
- ▁TOWARD
- AK
- ▁JOY
- ▁JA
- ▁HAR
- ▁NEITHER
- ▁FORCE
- ▁UNCLE
- DER
- ▁PLAN
- ▁PRINCESS
- DI
- ▁CHIEF
- ▁HAT
- ▁LIVED
- ▁AB
- ▁VISIT
- ▁MOR
- TEN
- ▁WALL
- UC
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- ▁PLEASURE
- ▁SMILE
- ▁FRONT
- ▁HU
- ▁DEAL
- OW
- ▁FURTHER
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- ▁TRIED
- DA
- VA
- ▁NONE
- ▁ENTERED
- ▁QUEEN
- ▁PAY
- ▁EL
- ▁EXCEPT
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- ▁EIGHT
- ▁ADDED
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- ▁EIGHTEEN
- ▁STAR
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- ▁LED
- ▁WALKED
- ▁ALTHOUGH
- ▁LATER
- ▁SPIRIT
- ▁WALK
- ▁BIT
- ▁MEET
- LIN
- ▁FI
- LT
- ▁MOUTH
- ▁WAIT
- ▁HOURS
- ▁LIVING
- ▁YOURSELF
- ▁FAST
- ▁CHA
- ▁HALL
- ▁BEYOND
- ▁BOAT
- ▁SECRET
- ENS
- ▁CHAIR
- RN
- ▁RECEIVED
- ▁CAT
- RESS
- ▁DESIRE
- ▁GENTLEMAN
- UGH
- ▁LAID
- EVER
- ▁OCCASION
- ▁WONDER
- ▁GU
- ▁PARTY
- DEN
- ▁FISH
- ▁SEND
- ▁NEARLY
- ▁TRY
- CON
- ▁SEEMS
- RS
- ▁BELL
- ▁BRA
- ▁SILENCE
- IG
- ▁GUARD
- ▁DIE
- ▁DOING
- ▁TU
- ▁COR
- ▁EARLY
- ▁BANK
- ▁FIGURE
- IF
- ▁ENGLAND
- ▁MARY
- ▁AFRAID
- LER
- ▁FO
- ▁WATCH
- ▁FA
- ▁VA
- ▁GRE
- ▁AUNT
- PED
- ▁SERVICE
- ▁JE
- ▁PEN
- ▁MINUTES
- ▁PAN
- ▁TREES
- NED
- ▁GLASS
- ▁TONE
- ▁PLEASE
- ▁FORTH
- ▁CROSS
- ▁EXCLAIMED
- ▁DREW
- ▁EAT
- ▁AH
- ▁GRAVE
- ▁CUR
- PA
- URE
- CENT
- ▁MILES
- ▁SOFT
- ▁AGO
- ▁POSITION
- ▁WARM
- ▁LENGTH
- ▁NECESSARY
- ▁THINKING
- ▁PICTURE
- ▁PI
- SHIP
- IBLE
- ▁HEAVY
- ▁ATTENTION
- ▁DOG
- ABLY
- ▁STANDING
- ▁NATURAL
- ▁APPEAR
- OV
- ▁CAUGHT
- VO
- ISM
- ▁SPRING
- ▁EXPERIENCE
- ▁PAT
- OT
- ▁STOPPED
- ▁REGARD
- ▁HARDLY
- ▁SELF
- ▁STRENGTH
- ▁GREW
- ▁KNIGHT
- ▁OPINION
- ▁WIDE
- ▁INSTEAD
- ▁SOUTH
- ▁TRANS
- ▁CORNER
- ▁LEARN
- ▁ISLAND
- ▁MI
- ▁THIRD
- ▁STE
- ▁STRAIGHT
- ▁TEA
- ▁BOUND
- ▁SEEING
- ▁JU
- ▁DINNER
- ▁BEAUTY
- ▁PEACE
- AH
- ▁REP
- ▁SILENT
- ▁CRE
- ALLY
- RIC
- ▁STEP
- ▁VER
- ▁JO
- GER
- ▁SITTING
- ▁THIRTY
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- ▁POSSIBLY
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- ▁ORIGINAL
- ▁HEARING
- ▁WORTHY
- ▁SELL
- NEY
- ICK
- ▁COTTAGE
- ▁SACRIFICE
- ▁PROGRESS
- ▁SHOCK
- ▁DESIGN
- ▁SOUGHT
- ▁PIT
- ▁SUNDAY
- ▁OTHERWISE
- ▁CABIN
- ▁PRAYER
- ▁DWELL
- ▁GAIN
- ▁BRIDGE
- ▁PARTICULARLY
- ▁YIELD
- ▁TREAT
- RIGHT
- ▁OAK
- ▁ROPE
- WIN
- ▁ORDERS
- ▁SUSPECT
- ▁EDWARD
- AB
- ▁ELEVEN
- ▁TEETH
- ▁OCCURRED
- DDING
- ▁AMERICA
- ▁FALLING
- ▁LION
- ▁DEPART
- ▁KEEPING
- ▁DEMAND
- ▁PAUSED
- ▁CEASED
- INA
- ▁FUN
- ▁CHEER
- ▁PARDON
- ▁NATIVE
- LUS
- LOW
- ▁DOGS
- ▁REQUIRED
- ILITY
- ▁ELECT
- ▁ENTERTAIN
- ITUDE
- ▁HUGE
- ▁CARRYING
- ▁BLU
- ▁INSIST
- ▁SATISFACTION
- ▁HUNT
- ▁COUNTENANCE
- ▁UPPER
- ▁MAIDEN
- ▁FAILED
- ▁JAMES
- ▁FOREIGN
- ▁GATHER
- ▁TEST
- BOARD
- ▁TERMS
- ▁SILK
- ▁BEG
- ▁BROTHERS
- ▁PAGE
- ▁KNEES
- ▁SHOWN
- ▁PROFESSOR
- ▁MIGHTY
- ▁DEFI
- ▁CHARM
- ▁REQUIRE
- ▁LOG
- MORE
- ▁PROOF
- ▁POSSESSED
- ▁SOFTLY
- ▁UNFORTUNATE
- ▁PRICE
- ▁SEVERE
- ▁SINGING
- ▁STAGE
- ▁FREEDOM
- ▁SHOUTED
- ▁FARTHER
- ▁MAJESTY
- ▁PREVIOUS
- ▁GUIDE
- ▁MATCH
- ▁CHEST
- ▁INTENDED
- ▁BI
- ▁EXCITEMENT
- ▁OFFICERS
- ▁SUR
- ▁SHAKE
- ▁SENTIMENT
- ▁GENTLY
- ▁SUCCEEDED
- ▁MENTION
- ▁LOCK
- ▁ACQUAINTANCE
- ▁IMAGINATION
- ▁PHYSICAL
- ▁LEADING
- ▁SLAVE
- ▁CART
- ▁POINTED
- ▁STEAM
- ▁SHADE
- ▁PIPE
- ▁BASE
- ▁INVENT
- ▁ALAS
- ▁WORKED
- ▁REGRET
- ▁BUR
- ▁FAITHFUL
- ▁MENTIONED
- ▁RECORD
- ▁COMPLAIN
- ▁SUPERIOR
- ▁BAY
- ▁PAL
- EMENT
- UE
- ▁SEVENTY
- ▁HOTEL
- ▁SHEEP
- ▁MEAL
- ▁ADVICE
- ▁HIDDEN
- ▁DEMANDED
- ▁CONSCIOUS
- ▁BROW
- ▁POSSESS
- ▁FOURTH
- ▁EVENTS
- ▁FRI
- ▁PRAISE
- ▁ADVANCED
- ▁RESOLVED
- ▁STUFF
- ▁CHEERFUL
- ▁BIRTH
- ▁GRIEF
- ▁AFFORD
- ▁FAIRY
- ▁WAKE
- ▁SIDES
- ▁SUBSTANCE
- ▁ARTICLE
- ▁LEVEL
- ▁MIST
- ▁JOINED
- ▁PRACTICAL
- ▁CLEARLY
- ▁TRACE
- ▁AWAKE
- ▁OBSERVE
- ▁BASKET
- ▁LACK
- VILLE
- ▁SPIRITS
- ▁EXCITED
- ▁ABANDON
- ▁SHINING
- ▁FULLY
- ▁CALLING
- ▁CONSIDERABLE
- ▁SPRANG
- ▁MILE
- ▁DOZEN
- ▁PEA
- ▁DANGEROUS
- ▁WIT
- ▁JEW
- ▁POUNDS
- ▁FOX
- ▁INFORMATION
- ▁LIES
- ▁DECK
- NNY
- ▁PAUL
- ▁STARS
- ▁ANGER
- ▁SETTLE
- ▁WILLING
- ▁ADAM
- ▁FACES
- ▁SMITH
- ▁IMPORTANCE
- ▁STRAIN
- WAR
- ▁SAM
- ▁FEATHER
- ▁SERVED
- ▁AUTHOR
- ▁PERCEIVED
- ▁FLAME
- ▁DIVINE
- ▁TRAIL
- ▁ANYBODY
- ▁SIGH
- ▁DELICATE
- KY
- ▁FOLD
- ▁HAVEN
- ▁DESIRED
- ▁CURIOSITY
- ▁PRACTICE
- ▁CONSIDERATION
- ▁ABSOLUTELY
- ▁CITIZEN
- ▁BOTTLE
- ▁INTERESTED
- ▁MEAT
- ▁OCCUPIED
- ▁CHOOSE
- ▁THROAT
- ETTE
- ▁CANDLE
- ▁DAWN
- ▁PROTECT
- ▁SENTENCE
- IED
- ▁ROCKS
- ▁PORTION
- ▁APPARENTLY
- ▁PRESENTED
- ▁TIGHT
- ▁ACTUALLY
- ▁DYING
- ▁HAM
- ▁DAILY
- ▁SUFFERED
- ▁POLITICAL
- ▁BODIES
- ▁MODERN
- ▁COMPLETELY
- ▁SOONER
- TAN
- ▁PROP
- ▁ADVANCE
- ▁REFUSED
- ▁FARMER
- ▁POLITE
- ▁THUNDER
- ▁BRIEF
- ▁ELSIE
- ▁SAILOR
- ▁SUGGESTED
- ▁PLATE
- ▁AID
- ▁FLESH
- ▁WEEP
- ▁BUCK
- ▁ANTI
- ▁OCEAN
- ▁SPEND
- WELL
- ▁ODD
- ▁GOVERNOR
- ▁ENTRANCE
- ▁SUSPICION
- ▁STEPPED
- ▁RAPIDLY
- ▁CHECK
- ▁HIDE
- ▁FLIGHT
- ▁CLUB
- ▁ENTIRE
- ▁INDIANS
- ASH
- ▁CAPITAL
- ▁MAMMA
- HAR
- ▁CORRECT
- ▁CRACK
- ▁SENSATION
- ▁WORST
- ▁PACE
- ▁MIDST
- ▁AUGUST
- ▁PROPORTION
- ▁INNOCENT
- LINESS
- ▁REGARDED
- ▁DRIVEN
- ORD
- ▁HASTE
- ▁EDUCATION
- ▁EMPLOY
- ▁TRULY
- ▁INSTRUMENT
- ▁MAG
- ▁FRAME
- ▁FOOLISH
- ▁TAUGHT
- ▁HANG
- ▁ARGUMENT
- ▁NINETEEN
- ▁ELDER
- ▁NAY
- ▁NEEDED
- ▁NEIGHBOR
- ▁INSTRUCT
- ▁PAPERS
- ▁REWARD
- ▁EQUALLY
- ▁FIELDS
- ▁DIG
- HIN
- ▁CONDITIONS
- JA
- ▁SPAR
- ▁REQUEST
- ▁WORN
- ▁REMARKABLE
- ▁LOAD
- ▁WORSHIP
- ▁PARK
- ▁KI
- ▁INTERRUPTED
- ▁SKILL
- ▁TERM
- LAC
- ▁CRITIC
- ▁DISTRESS
- ▁BELIEF
- ▁STERN
- IGHT
- ▁TRACK
- ▁HUNTING
- ▁JEWEL
- ▁GRADUALLY
- ▁GLOW
- ▁RUSHED
- ▁MENTAL
- ▁VISITOR
- ▁PICKED
- ▁BEHOLD
- ▁EXPRESSED
- ▁RUB
- ▁SKI
- ARTAGNAN
- ▁MOREOVER
- ▁OPERATION
- ▁CAREFUL
- ▁KEEN
- ▁ASSERT
- ▁WANDER
- ▁ENEMIES
- ▁MYSTERIOUS
- ▁DEPTH
- ▁PREFER
- ▁CROSSED
- ▁CHARMING
- ▁DREAD
- ▁FLOUR
- ▁ROBIN
- ▁TRE
- ▁RELIEF
- ▁INQUIRED
- ▁APPLE
- ▁HENCE
- ▁WINGS
- ▁CHOICE
- ▁JUD
- OO
- ▁SPECIES
- ▁DELIGHTED
- IUM
- ▁RAPID
- ▁APPEAL
- ▁FAMOUS
- ▁USEFUL
- ▁HELEN
- ▁NEWSPAPER
- ▁PLENTY
- ▁BEARING
- ▁NERVOUS
- ▁PARA
- ▁URGE
- ▁ROAR
- ▁WOUNDED
- ▁CHAIN
- ▁PRODUCE
- ▁REFLECTION
- ▁MERCHANT
- ▁QUARREL
- ▁GLORY
- ▁BEGUN
- ▁BARON
- CUS
- ▁QUEER
- ▁MIX
- ▁GAZE
- ▁WHISPER
- ▁BURIED
- ▁DIV
- ▁CARD
- ▁FREQUENTLY
- ▁TIP
- ▁KNEE
- ▁REGION
- ▁ROOT
- ▁LEST
- ▁JEALOUS
- CTOR
- ▁SAVED
- ▁ASKING
- ▁TRIP
- QUA
- ▁UNION
- HY
- ▁COMPANIONS
- ▁SHIPS
- ▁HALE
- ▁APPROACHED
- ▁HARRY
- ▁DRUNK
- ▁ARRIVAL
- ▁SLEPT
- ▁FURNISH
- HEAD
- ▁PIG
- ▁ABSENCE
- ▁PHIL
- ▁HEAP
- ▁SHOES
- ▁CONSCIOUSNESS
- ▁KINDLY
- ▁EVIDENT
- ▁SCAR
- ▁DETERMIN
- ▁GRASP
- ▁STEAL
- ▁OWE
- ▁KNIFE
- ▁PRECIOUS
- ▁ELEMENT
- ▁PROCEEDED
- ▁FEVER
- ▁LEADER
- ▁RISK
- ▁EASE
- ▁GRIM
- ▁MOUNT
- ▁MEANWHILE
- ▁CENTURY
- OON
- ▁JUDGMENT
- ▁AROSE
- ▁VISION
- ▁SPARE
- ▁EXTREME
- ▁CONSTANT
- ▁OBSERVATION
- ▁THRUST
- ▁DELAY
- ▁CENT
- ▁INCLUD
- ▁LIFT
- ▁ADMIRE
- ▁ISSUE
- ▁FRIENDSHIP
- ▁LESSON
- ▁PRINCIPAL
- ▁MOURN
- ▁ACCEPTED
- ▁BURNING
- ▁CAPABLE
- ▁EXTRAORDINARY
- ▁SANG
- ▁REMOVED
- ▁HOPED
- ▁HORN
- ▁ALICE
- ▁MUD
- ▁APARTMENT
- ▁FIGHTING
- ▁BLAME
- ▁TREMBLING
- ▁SOMEBODY
- ▁ANYONE
- ▁BRIDE
- ▁READER
- ▁ROB
- ▁EVERYWHERE
- ▁LABOUR
- ▁RECALL
- ▁BULL
- ▁HIT
- ▁COUNCIL
- ▁POPULAR
- ▁CHAP
- ▁TRIAL
- ▁DUN
- ▁WISHES
- ▁BRILLIANT
- ▁ASSURED
- ▁FORGOT
- ▁CONTINUE
- ▁ACKNOWLEDG
- ▁RETREAT
- ▁INCREASED
- ▁CONTEMPT
- ▁GRANDFATHER
- ▁SYMPATHY
- ▁GHOST
- ▁STRETCHED
- ▁CREATURES
- ▁CAB
- ▁HIND
- ▁PLAYING
- ▁MISERABLE
- ▁MEMBERS
- ▁KINDNESS
- ▁HIGHEST
- ▁PRIM
- ▁KISSED
- ▁DESERVE
- ▁HUT
- ▁BEGGED
- ▁EIGHTY
- ▁CLOSELY
- ▁WONDERED
- ▁MILITARY
- ▁REMIND
- ▁ACCORDINGLY
- ▁LARGER
- ▁MAINTAIN
- ▁ENGINE
- ▁MOTIVE
- ▁DESTROY
- ▁STRIP
- ▁HANS
- ▁AHEAD
- ▁INFINITE
- ▁PROMPT
- ▁INFORMED
- TTLE
- ▁PEER
- ▁PRESSED
- ▁TRAP
- ▁SOMEWHERE
- ▁BOUGHT
- ▁VISIBLE
- ▁ASHAMED
- ▁TEAR
- ▁NEIGHBOUR
- ▁CONSTITUTION
- ▁INTELLIGENCE
- ▁PROFESSION
- ▁HUNGRY
- RIDGE
- ▁SMELL
- ▁STORIES
- ▁LISTENING
- ▁APPROACH
- ▁STRING
- ▁EXPLANATION
- ▁IMMENSE
- ▁RELIGIOUS
- ▁THROUGHOUT
- ▁HOLLOW
- ▁AWAIT
- ▁FLYING
- ▁SCREAM
- ▁ACTIVE
- ▁RUM
- ▁PRODUCT
- ▁UNHAPPY
- ▁VAGUE
- ARIES
- ▁ELIZABETH
- ▁STUPID
- ▁DIGNITY
- ▁ISABEL
- GAR
- ▁BRO
- ▁PITCH
- ▁COMRADE
- ▁STIFF
- ▁RECKON
- ▁SOLD
- ▁SPARK
- ▁STRO
- ▁CRYING
- ▁MAGIC
- ▁REPEAT
- PORT
- ▁MARKED
- ▁COMFORTABLE
- ▁PROJECT
- ▁BECOMING
- ▁PARENTS
- ▁SHELTER
- ▁STOLE
- ▁HINT
- ▁NEST
- ▁TRICK
- ▁THOROUGHLY
- ▁HOSPITAL
- ▁WEAPON
- ▁ROME
- ▁STYLE
- ▁ADMITTED
- ▁SAFETY
- FIELD
- ▁UNDERSTANDING
- ▁TREMBLE
- ▁PRINT
- ▁SLAVES
- ▁WEARY
- ▁ARTIST
- ▁CREDIT
- BURG
- ▁CONCLUSION
- ▁SELDOM
- ▁UNUSUAL
- ▁CLOUDS
- ▁UNABLE
- ▁GAY
- ▁HANGING
- ▁SCR
- ▁BOWED
- ▁DAVID
- ▁VOL
- ▁PUSHED
- ▁ESCAPED
- MOND
- ▁WARN
- ▁BETRAY
- ▁EGGS
- ▁PLAINLY
- ▁EXHIBIT
- ▁DISPLAY
- ▁MEMBER
- ▁GRIN
- ▁PROSPECT
- ▁BRUSH
- ▁BID
- ▁SUCCESSFUL
- ▁EXTENT
- ▁PERSUADE
- ▁MID
- ▁MOOD
- ▁ARRANGED
- ▁UNIVERSAL
- ▁JIM
- ▁SIGNAL
- ▁WHILST
- ▁PHILIP
- ▁WOLF
- RATE
- ▁EAGERLY
- ▁BILLY
- ▁RETURNING
- ▁CONSCIENCE
- ▁FORTUNATE
- ▁FEMALE
- ▁GLEAM
- ▁HASTILY
- ▁PROVIDED
- ▁OBTAIN
- ▁INSTINCT
- ▁CONCERNED
- ▁CONCERNING
- ▁SOMEHOW
- ▁PINK
- ▁RAGE
- ▁ACCUSTOMED
- ▁UNCONSCIOUS
- ▁ADVISE
- ▁BRANCHES
- ▁TINY
- ▁REFUSE
- ▁BISHOP
- ▁SUPPLY
- ▁PEASANT
- ▁LAWYER
- ▁WASTE
- ▁CONNECTION
- ▁DEVELOP
- ▁CORRESPOND
- ▁PLUM
- ▁NODDED
- ▁SLIPPED
- ▁EU
- ▁CONSTANTLY
- CUM
- MMED
- ▁FAIRLY
- HOUSE
- ▁KIT
- ▁RANG
- ▁FEATURES
- ▁PAUSE
- ▁PAINFUL
- ▁JOE
- ▁WHENCE
- ▁LAUGHTER
- ▁COACH
- ▁CHRISTMAS
- ▁EATING
- ▁WHOLLY
- ▁APART
- ▁SUPER
- ▁REVOLUTION
- ▁LONELY
- ▁CHEEKS
- ▁THRONE
- ▁CREW
- ▁ATTAIN
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- TIME
- ▁DASH
- ▁FRIENDLY
- ▁OPERA
- ▁EARL
- ▁EXHAUST
- ▁CLIFF
- ▁REVEAL
- ▁ADOPT
- ▁CENTRE
- ▁MERRY
- ▁SYLVIA
- ▁IDEAL
- ▁MISFORTUNE
- ▁FEAST
- ▁ARAB
- ▁NUT
- ▁FETCH
- ▁FOUGHT
- ▁PILE
- ▁SETTING
- ▁SOURCE
- ▁PERSIST
- ▁MERCY
- ▁BARK
- ▁LUC
- ▁DEEPLY
- ▁COMPARE
- ▁ATTITUDE
- ▁ENDURE
- ▁DELIGHTFUL
- ▁BEARD
- ▁PATIENCE
- ▁LOCAL
- ▁UTTERED
- ▁VICTORY
- ▁TREATED
- ▁SEPARATE
- ▁WAG
- ▁DRAGG
- ▁TITLE
- ▁TROOPS
- ▁TRIUMPH
- ▁REAR
- ▁GAINED
- ▁SINK
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- ▁TIED
- ▁FLED
- ▁DARED
- ▁INCREASE
- ▁POND
- ▁CONQUER
- ▁FOREHEAD
- ▁FAN
- ▁ANXIETY
- ▁ENCOUNTER
- ▁SEX
- ▁HALT
- ▁SANK
- ▁CHEEK
- ▁HUMBLE
- ▁WRITER
- ▁EMPLOYED
- ▁DISTINGUISHED
- ▁RAISE
- ▁WHIP
- ▁GIANT
- ▁RANGE
- ▁OBTAINED
- ▁FLAG
- ▁MAC
- ▁JUMPED
- ▁DISCOVERY
- ▁NATIONAL
- ▁COMMISSION
- ▁POSITIVE
- ▁LOVING
- ▁EXACT
- ▁MURMURED
- ▁GAZED
- ▁REFER
- ▁COLLEGE
- ▁ENCOURAGE
- ▁NOVEL
- ▁CLOCK
- ▁MORTAL
- ▁ROLLED
- ▁RAT
- IZING
- ▁GUILTY
- ▁VICTOR
- WORTH
- ▁PRA
- ▁APPROACHING
- ▁RELATIVE
- ▁ESTATE
- ▁UGLY
- ▁METAL
- ▁ROBERT
- ▁TENT
- ▁ADMIRATION
- ▁FOURTEEN
- ▁BARBAR
- ▁WITCH
- ELLA
- ▁CAKE
- ▁SHONE
- ▁MANAGED
- ▁VOLUME
- ▁GREEK
- ▁DANCING
- ▁WRETCHED
- ▁CONDEMN
- ▁MAGNIFICENT
- ▁CONSULT
- J
- ▁ORGAN
- ▁FLEET
- ▁ARRANGEMENT
- ▁INCIDENT
- ▁MISERY
- ▁ARROW
- ▁STROKE
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- ▁BUILD
- ▁SUCCEED
- ▁DESPERATE
- ▁WIDOW
- UDE
- ▁MARKET
- ▁WISDOM
- ▁PRECISE
- ▁CURRENT
- ▁SPOIL
- ▁BADE
- ▁WOODEN
- ▁RESIST
- ▁OBVIOUS
- ▁SENSIBLE
- FALL
- ▁ADDRESSED
- ▁GIL
- ▁COUNSEL
- ▁PURCHASE
- ▁SELECT
- ▁USELESS
- ▁STARED
- ▁ARREST
- ▁POISON
- ▁FIN
- ▁SWALLOW
- ▁BLOCK
- ▁SLID
- ▁NINETY
- ▁SPORT
- ▁PROVIDE
- ▁ANNA
- ▁LAMB
- ▁INTERVAL
- ▁JUMP
- ▁DESCRIBED
- ▁STRIKING
- ▁PROVISION
- ▁PROPOSED
- ▁MELANCHOLY
- ▁WARRIOR
- ▁SUGGEST
- ▁DEPARTURE
- ▁BURDEN
- ▁LIMB
- ▁TROUBLED
- ▁MEADOW
- ▁SACRED
- ▁SOLID
- ▁TRU
- ▁LUCY
- ▁RECOVER
- ▁ENERGY
- ▁POWDER
- ▁RESUMED
- ▁INTENSE
- ▁BRITISH
- ▁STRAW
- ▁AGREEABLE
- ▁EVERYONE
- ▁CONCERN
- ▁VOYAGE
- ▁SOUTHERN
- ▁BOSOM
- ▁UTTERLY
- ▁FEED
- ▁ESSENTIAL
- ▁CONFINE
- ▁HOUSEHOLD
- ▁EXTREMELY
- ▁WONDERING
- ▁LIST
- ▁PINE
- PHA
- ▁EXPERIMENT
- ▁JOSEPH
- ▁MYSTERY
- ▁RESTORE
- ▁BLUSH
- FOLD
- ▁CHOSEN
- ▁INTELLECT
- ▁CURTAIN
- OLOGY
- ▁MOUNTED
- ▁LAP
- ▁EPI
- ▁PUNISH
- ▁WEDDING
- ▁RECOGNIZED
- ▁DRIFT
- ▁PREPARATION
- ▁RESOLUTION
- ▁OPPRESS
- ▁FIX
- ▁VICTIM
- OGRAPH
- ▁SUMMON
- ▁JULIA
- ▁FLOOD
- ▁WAL
- ULATION
- ▁SLIGHTLY
- ▁LODGE
- ▁WIRE
- ▁CONFUSION
- ▁UNEXPECTED
- ▁CONCEIVE
- ▁PRIZE
- ▁JESUS
- ▁ADDITION
- ▁RUDE
- ▁FATAL
- ▁CARELESS
- ▁PATCH
- ▁KO
- ▁CATHERINE
- ▁PARLIAMENT
- ▁PROFOUND
- ▁ALOUD
- ▁RELIEVE
- ▁PUSH
- ABILITY
- ▁ACCOMPANIED
- ▁SOVEREIGN
- ▁SINGULAR
- ▁ECHO
- ▁COMPOSED
- ▁SHAKING
- ATORY
- ▁ASSISTANCE
- ▁TEACHER
- ▁HORRIBLE
- ▁STRICT
- ▁VERSE
- ▁PUNISHMENT
- ▁GOWN
- ▁MISTAKEN
- ▁VARI
- ▁SWEPT
- ▁GESTURE
- ▁BUSH
- ▁STEEL
- ▁AFFECTED
- ▁DIRECTED
- ▁SURROUNDED
- ▁ABSURD
- ▁SUGAR
- ▁SCRAP
- ▁IMMEDIATE
- ▁SADDLE
- ▁TY
- ▁ARISE
- ▁SIGHED
- ▁EXCHANGE
- ▁IMPATIENT
- ▁SNAP
- ▁EMBRACE
- ▁DISEASE
- ▁PROFIT
- ▁RIDING
- ▁RECOVERED
- ▁GOVERN
- ▁STRETCH
- ▁CONVINCED
- ▁LEANING
- ▁DOMESTIC
- ▁COMPLEX
- ▁MANIFEST
- ▁INDULGE
- ▁GENIUS
- ▁AGENT
- ▁VEIL
- ▁DESCRIPTION
- ▁INCLINED
- ▁DECEIVE
- ▁DARLING
- ▁REIGN
- HU
- ▁ENORMOUS
- ▁RESTRAIN
- ▁DUTIES
- BURY
- TTERED
- ▁POLE
- ▁ENABLE
- ▁EXCEPTION
- ▁INTIMATE
- ▁COUNTESS
- ▁TRIBE
- ▁HANDKERCHIEF
- ▁MIDNIGHT
- ▁PROBLEM
- ▁TRAMP
- ▁OIL
- CAST
- ▁CRUSH
- ▁DISCUSS
- ▁RAM
- ▁TROT
- ▁UNRE
- ▁WHIRL
- ▁LOCKED
- ▁HORIZON
- ▁OFFICIAL
- ▁SCHEME
- ▁DROWN
- ▁PIERRE
- ▁PERMITTED
- ▁CONNECTED
- ▁ASSURE
- ▁COCK
- ▁UTMOST
- ▁DEVOTED
- ▁RELI
- ▁SUFFICIENTLY
- ▁INTELLECTUAL
- ▁CARPET
- ▁OBJECTION
- ▁AFTERWARD
- ▁REALITY
- ▁NEGRO
- ▁RETAIN
- ▁ASCEND
- ▁CEASE
- ▁KATE
- ▁MARVEL
- KO
- ▁BOND
- MOST
- ▁COAL
- GATE
- ▁IGNORANT
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- ▁APPOINTED
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- ▁LIGHTNING
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- ▁TOTAL
- ▁DISPOSED
- ▁RIFLE
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- ▁DEFINITE
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- ▁COMMITTEE
- ▁SIMON
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- ▁APPLIED
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- ▁REVENGE
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- ▁IMITAT
- ▁PASSIONATE
- ▁SELFISH
- ▁NORMAN
- ▁REPAIR
- ▁THRILL
- ▁TREATMENT
- ▁ROSA
- ▁MARTIN
- ▁INDIFFERENT
- ▁THITHER
- ▁GALLANT
- ▁PEPPER
- ▁RECOLLECT
- ▁VINE
- ▁SCARCE
- ▁SHIELD
- ▁MINGLED
- CLOSE
- ▁HARSH
- ▁BRICK
- ▁HUMOR
- ▁MISCHIEF
- ▁TREMENDOUS
- ▁FUNCTION
- ▁SMART
- ▁SULTAN
- ▁DISMISS
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- ▁CHEAP
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- ▁PILLOW
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- ▁PERISH
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- ▁WHISTLE
- ▁BERNARD
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- ▁IMPROVE
- ▁SUP
- ▁PERPLEX
- ▁FOUNTAIN
- ▁LEAGUE
- ▁DESPISE
- ▁IGNORANCE
- ▁REFERENCE
- ▁DUCK
- ▁GROVE
- ▁PURSE
- ▁PARTNER
- ▁PROPHET
- ▁SHIVER
- ▁NEIGHBOURHOOD
- ▁REPRESENTATIVE
- SAIL
- ▁WIP
- ▁ACQUIRED
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- ▁DOCTRINE
- ▁MAXIM
- ▁ANGLE
- ▁MAJORITY
- ▁AUTUMN
- ▁CONFUSED
- ▁CRISTO
- ▁ACHIEVE
- ▁DISGUISE
- ▁REDUCED
- ▁EARLIER
- ▁THEATRE
- ▁DECIDE
- MINATED
- OLOGICAL
- ▁OCCUPATION
- ▁VIGOROUS
- ▁CONTINENT
- ▁DECLINE
- ▁COMMUNITY
- ▁MOTIONLESS
- ▁HATRED
- ▁COMMUNICATION
- ▁BOWL
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- ▁SHIFT
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- ▁UNCERTAIN
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- ▁APPLY
- ▁PLANET
- ▁NOTWITHSTANDING
- ▁SWUNG
- ▁STIRRED
- ▁ATTENDANT
- ▁ENJOYMENT
- ▁WORRY
- ▁ALBERT
- ▁NAKED
- ▁TALENT
- ▁MARIAN
- ▁REFORM
- ▁DELIBERATE
- ▁INTELLIGENT
- ▁SENSITIVE
- ▁YONDER
- ▁PUPIL
- ▁FRIGHTFUL
- ▁DOUBTFUL
- ▁STANDARD
- ▁MAGISTRATE
- ▁SHEPHERD
- ▁STOMACH
- ▁DEPOSIT
- ▁RENEW
- ▁HEDGE
- ▁FRANCS
- ▁POSSIBILITY
- ▁RESEMBLE
- ▁FATIGUE
- ▁PORTRAIT
- ▁FAVORITE
- ▁CREAM
- ▁BURG
- ▁SECRETARY
- ▁DIVERS
- ▁ACTIVITY
- ▁SPECULAT
- ▁HUMOUR
- ▁FITTED
- ▁EXTERNAL
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- ▁WHIT
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- ▁EXAMINATION
- ▁LODGING
- ▁OWING
- ▁JAW
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- ▁PUFF
- ▁TENDERNESS
- ▁PORTHOS
- ▁ANCHOR
- ▁INTERRUPT
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- ▁AGONY
- ▁POPE
- ▁SCHOLAR
- ▁SCOTLAND
- ▁SUPPRESS
- ▁WRATH
- ▁WRECK
- ▁EXCEED
- ▁PERFECTION
- ▁INDIA
- ▁TRADITION
- ▁SECTION
- ▁EASTERN
- ▁DOORWAY
- ▁WIVES
- ▁CONVENTION
- ▁ANNOUNC
- ▁EGYPT
- ▁CONTRADICT
- ▁SCRATCH
- ▁CENTRAL
- ▁GLOVE
- ▁WAX
- ▁PREPARE
- ▁ACCOMPANY
- ▁INCREASING
- ▁LIBERAL
- ▁RAISING
- ▁ORANGE
- ▁SHOE
- ▁ATTRIBUTE
- ▁LITERATURE
- ▁PUZZLED
- ▁WITHDRAW
- ▁WHITHER
- ▁HAWK
- ▁MOONLIGHT
- ▁EXAMINE
- ▁HAPPILY
- ▁PRECEDE
- ▁DETECTIVE
- ▁INCHES
- ▁SOLITARY
- ▁DUTCH
- ▁NAPOLEON
- ▁UNEASY
- ▁CARDINAL
- ▁BLEW
- ▁FOWL
- ▁DECORAT
- ▁CHILDHOOD
- ▁TORMENT
- ▁LOSING
- ▁PERMISSION
- ▁BLANK
- ▁UPSTAIRS
- ▁CAPACITY
- ▁TRIFLE
- ▁FOLLY
- ▁RECOGNIZE
- ▁REMOVE
- ▁VENGEANCE
- ▁ENTERPRISE
- ▁BEDROOM
- ▁ANYHOW
- ▁INQUIRY
- ▁ASHES
- ▁DRAG
- ▁HUSH
- ▁AWKWARD
- ▁SATURDAY
- ▁GENUINE
- ▁SURVIV
- ▁SKIRT
- ▁AFFECTIONATE
- ▁TANG
- ▁MUTUAL
- ▁DISPUTE
- ▁EAGLE
- ▁INCOME
- ▁BIND
- ▁FAME
- ▁IMPROVEMENT
- ROVING
- ▁DIFFER
- ▁AWOKE
- ▁SLEEVE
- ▁SOLITUDE
- ▁FAVOURITE
- JI
- ▁DETECT
- ▁COMPREHEND
- ▁PREPARING
- ▁SERPENT
- ▁SUMMIT
- ▁KNOT
- ▁KNIT
- ▁COPY
- ▁STOPPING
- ▁FADED
- ▁HIDEOUS
- ▁JULIE
- STEAD
- ▁SHINE
- ▁CONFLICT
- ▁PROPOSITION
- ▁REFUGE
- ▁GALLERY
- ▁BUNDLE
- ▁AXE
- ▁SLAVERY
- ▁MASK
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- ▁DEPRESS
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- ▁SCARLET
- ▁KITTY
- ▁RECEIVING
- ▁SURRENDER
- ▁SUSTAIN
- ▁TWILIGHT
- ▁CONGRESS
- ▁IRELAND
- ▁FUNNY
- ▁LEND
- ▁CONSTITUTE
- ▁FUNERAL
- ▁CRYSTAL
- ▁SPAIN
- ▁EXCEEDINGLY
- ▁DAMN
- ▁COMMUN
- ▁CIVILIZATION
- ▁PREJUDICE
- ▁PORCH
- ▁ASSISTANT
- ▁INDUSTRY
- ▁TUMBLE
- ▁DEFENCE
- ▁HITHER
- ▁SMOT
- ▁COLONI
- ▁AMAZEMENT
- ▁MARGUERITE
- ▁MIRACLE
- ▁INHERIT
- ▁BEGGAR
- ▁ENVELOPE
- ▁INDIGNATION
- ▁NATASHA
- ▁PROPOSAL
- ▁FRAGMENT
- ▁ROUSED
- ▁ROAST
- ENCIES
- ▁COMMENCED
- ▁RESOURCE
- ▁POPULATION
- ▁QUOTH
- ▁PURSUE
- ▁EDUCAT
- ▁AFFLICT
- ▁CONTACT
- ▁CRIMSON
- ▁DIVISION
- ▁DISORDER
- ▁COPPER
- ▁SOLICIT
- ▁MODERATE
- ▁DRUM
- ▁SWIM
- ▁SALUTE
- ▁ASSUME
- ▁MUSCLE
- ▁OVERWHELM
- ▁SHAKESPEARE
- ▁STRUGGLING
- ▁TRANQUIL
- ▁CHICKEN
- ▁TREAD
- ▁CLAW
- ▁BIBLE
- ▁RIDGE
- ▁THREAT
- ▁VELVET
- ▁EXPOSED
- ▁IDIOT
- ▁BARREL
- ▁PENNY
- ▁TEMPTATION
- ▁DANGLARS
- ▁CENTURIES
- ▁DISTRIBUT
- ▁REJECT
- ▁RETORTED
- ▁CONCENTRAT
- ▁CORDIAL
- ▁MOTOR
- ▁CANNON
- KEEP
- ▁WRETCH
- ▁ASSURANCE
- ▁THIEF
- ▁SURVEY
- ▁VITAL
- ▁RAILWAY
- ▁JACKSON
- ▁CRASH
- ▁GROWL
- ▁COMBAT
- ▁RECOLLECTION
- ▁SECURITY
- ▁JACOB
- ▁CLUTCH
- ▁BLANKET
- ▁NANCY
- ▁CELLAR
- ▁CONVENIENT
- ▁INDIGNANT
- ▁COARSE
- ▁WORM
- ▁SCREEN
- ▁TRANSPORT
- ▁BULLET
- ▁APPRECIATE
- ▁DEVOTION
- ▁INVISIBLE
- ▁DRIED
- ▁MIXTURE
- ▁CANDID
- ▁PERFORMANCE
- ▁RIPE
- ▁EXQUISITE
- ▁BARGAIN
- ▁TOBACCO
- ▁LOYAL
- ▁MOULD
- ▁ATTENTIVE
- ▁DOROTHY
- ▁BRUTE
- ▁ESTABLISHMENT
- ▁ABILITY
- ▁INHABIT
- ▁OBSCURE
- ▁BORROW
- ▁ESSENCE
- ▁DISMAY
- ▁FLEE
- ▁BLADE
- ▁PLUCK
- ▁COFFIN
- ▁SUNSET
- ▁STEPHEN
- ▁ECONOMIC
- ▁HOLIDAY
- ▁MECHANICAL
- ▁COTTON
- ▁AWAKENED
- ▁SEIZE
- ▁RIDICULOUS
- ▁SANCHO
- ▁HESITATION
- ▁CORPSE
- ▁SAVING
- HOLD
- FOOT
- ▁ELDEST
- ▁DESPITE
- ▁EDITH
- ▁CHERISH
- ▁RESISTANCE
- ▁WILSON
- ▁ARGUE
- ▁INQUIRE
- ▁APPREHENSION
- ▁AVENUE
- ▁DRAKE
- ▁PROPOSE
- HURST
- ▁INFERIOR
- ▁STAIRCASE
- ▁WHEREFORE
- ▁CARLYLE
- ▁COUCH
- ▁ROUTE
- ▁POLITICS
- ▁TOMORROW
- ▁THRONG
- ▁NAUGHT
- ▁SUNLIGHT
- ▁INDIFFERENCE
- ▁OBEDIENCE
- ▁RECEPTION
- ▁VEGETABLE
- ▁IMPERFECT
- ▁RESIDENCE
- ▁TURKEY
- ▁VIOLET
- ▁SARAH
- ▁ALTAR
- ▁GRIEVE
- ▁JERK
- ▁ENSU
- ▁MAGICIAN
- ▁BLOSSOM
- ▁LANTERN
- ▁RESOLUTE
- ▁THOUGHTFULLY
- ▁FORTNIGHT
- ▁TRUMPET
- ▁VALJEAN
- ▁UNWILLING
- ▁LECTURE
- ▁WHEREUPON
- ▁HOLLAND
- ▁CHANGING
- ▁CREEK
- ▁SLICE
- ▁NORMAL
- ▁ANNIE
- ▁ACCENT
- ▁FREDERICK
- ▁DISAGREEABLE
- ▁RUBBED
- ▁DUMB
- ▁ESTABLISH
- ▁IMPORT
- ▁AFFIRM
- ▁MATTHEW
- ▁BRISK
- ▁CONVERT
- ▁BENDING
- ▁IVAN
- ▁MADEMOISELLE
- ▁MICHAEL
- ▁EASIER
- ▁JONES
- ▁FACING
- ▁EXCELLENCY
- ▁LITERARY
- ▁GOSSIP
- ▁DEVOUR
- ▁STAGGER
- ▁PENCIL
- ▁AVERAGE
- ▁HAMMER
- ▁TRIUMPHANT
- ▁PREFERRED
- ▁APPLICATION
- ▁OCCUPY
- ▁AUTHORITIES
- BURN
- ▁ASCERTAIN
- ▁CORRIDOR
- ▁DELICIOUS
- ▁PRACTISE
- ▁UNIVERSE
- ▁SHILLING
- ▁CONTEST
- ▁ASHORE
- ▁COMMIT
- ▁ADMINISTRATION
- ▁STUDIED
- ▁RIGID
- ▁ADORN
- ▁ELSEWHERE
- ▁INNOCENCE
- ▁JOURNAL
- ▁LANDSCAPE
- ▁TELEGRAPH
- ▁ANGRILY
- ▁CAMPAIGN
- ▁UNJUST
- ▁CHALLENGE
- ▁TORRENT
- ▁RELATE
- ▁ASSEMBLED
- ▁IMPRESSED
- ▁CANOE
- ▁CONCLUD
- ▁QUIXOTE
- ▁SATISFACTORY
- ▁NIECE
- ▁DEAF
- ▁RAFT
- ▁JIMMY
- ▁GLID
- ▁REGULAT
- ▁CHATTER
- ▁GLACIER
- ▁ENVY
- ▁STATUE
- ▁BOSTON
- ▁RICHMOND
- ▁DENIED
- ▁FANNY
- ▁SOLOMON
- ▁VULGAR
- ▁STALK
- ▁REPLACE
- ▁SPOON
- ▁BASIN
- ▁FEATURE
- ▁CONVICT
- ▁ARCHITECT
- ▁ADMIRAL
- ▁RIBBON
- ▁PERMANENT
- ▁APRIL
- ▁JOLLY
- ▁NEIGHBORHOOD
- ▁IMPART
- BOROUGH
- CAMP
- ▁HORRID
- ▁IMMORTAL
- ▁PRUDENCE
- ▁SPANIARD
- ▁SUPPOSING
- ▁TELEPHONE
- ▁TEMPERATURE
- ▁PENETRATE
- ▁OYSTER
- ▁APPOINTMENT
- ▁EGYPTIAN
- ▁DWELT
- ▁NEPHEW
- ▁RAILROAD
- ▁SEPTEMBER
- ▁DEVICE
- ▁WHEAT
- ▁GILBERT
- ▁ELEGANT
- ▁ADVERTISE
- ▁RATIONAL
- ▁TURTLE
- ▁BROOD
- ▁ASSEMBLY
- ▁CULTIVATE
- ▁EDITOR
- ▁SPECIMEN
- ▁UNDOUBTEDLY
- ▁WHALE
- ▁DROPPING
- ▁BALLOON
- ▁MEDICAL
- COMB
- ▁COMPOSITION
- ▁FOOTSTEPS
- ▁LAUNCELOT
- ▁DISCOURSE
- ▁ERRAND
- ▁CONVERSE
- ▁ADVANCING
- ▁DOWNSTAIRS
- ▁TUMULT
- ▁CORRUPT
- ▁SUFFICE
- ▁ANGUISH
- ▁SHAGGY
- ▁RETIRE
- ▁TIMBER
- ▁BLAZE
- ▁ABSTRACT
- ▁EMBROIDER
- ▁PHOTOGRAPH
- ▁PROSPERITY
- ▁TERRIBLY
- ▁TERRITORY
- ▁THRESHOLD
- ▁PAVEMENT
- ▁INJURED
- ▁LIMP
- ▁AGITATION
- ▁RASCAL
- ▁PRESUME
- ▁OBSERVING
- ▁OBSTACLE
- ▁SIMPLICITY
- ▁SLUMBER
- ▁SUPPLIED
- ▁COMBINATION
- ▁DRAIN
- ▁WILDERNESS
- ▁BELIEVING
- ▁VILLAIN
- ▁RECKLESS
- ▁INJURY
- ▁CLAPP
- ▁FRIDAY
- ▁HERCULES
- ▁KENNEDY
- ▁SYMPTOM
- ▁SLEDGE
- ▁CEILING
- ▁LEMON
- ▁PLAGUE
- ▁MONDAY
- ▁CANVAS
- ▁IMPATIENCE
- ▁UNCOMFORTABLE
- ▁ACCESS
- ▁FROZEN
- ▁SENATOR
- ▁FRANZ
- ▁SWIMMING
- ▁BARRIER
- ▁ADJUST
- ▁COMPARISON
- ▁PROCLAIM
- ▁WRINKL
- ▁OVERLOOK
- ▁MITYA
- ▁GUILT
- ▁PERCEPTION
- ▁PRECAUTION
- ▁SPECTATOR
- ▁SURPRISING
- ▁DISTRACT
- ▁DISDAIN
- ▁BONNET
- ▁MAGNET
- ▁PROFESS
- ▁CONFOUND
- ▁NARRATIVE
- ▁STRUCTURE
- ▁SKETCH
- ▁ULTIMATE
- ▁GLOBE
- ▁INSECT
- FICIENCY
- ▁ORCHARD
- ▁AMIABLE
- ▁DESCENT
- ▁INDEPENDENCE
- ▁MANUFACTURE
- ▁SPRINKLE
- ▁NIGHTINGALE
- ▁CUSHION
- ▁EMINENT
- ▁SCOTT
- ▁ARRAY
- ▁COSETTE
- ▁WAVING
- ▁EXTRACT
- ▁IRREGULAR
- ▁PERSECUT
- ▁DERIVED
- ▁WITHDREW
- ▁CAUTION
- ▁SUSPICIOUS
- ▁MEMORIES
- ▁NOWHERE
- ▁SUBTLE
- ▁THOROUGH
- Q
- ▁APPROPRIATE
- ▁SLAUGHTER
- ▁YOURSELVES
- ▁THUMB
- ▁TWAS
- ▁ABODE
- ▁BIDDING
- ▁CONSPICUOUS
- ▁REBECCA
- ▁SERGEANT
- ▁APRON
- ▁ANTICIPATE
- ▁DISCIPLINE
- ▁GLANCING
- ▁PILGRIM
- ▁SULLEN
- ▁CONTRIBUTE
- ▁PRAIRIE
- ▁CARVED
- ▁COMMERCE
- ▁EXCLAMATION
- ▁MUSCULAR
- ▁NOVEMBER
- ▁PHENOMENA
- ▁SYMBOL
- ▁UMBRELLA
- ▁DIMINISH
- ▁PARLOUR
- ▁THREATENING
- ▁STUMP
- ▁EXTENSIVE
- ▁PLEASING
- ▁REMEMBRANCE
- ▁COMBINED
- ▁SHERIFF
- ▁SHAFT
- ▁LAURA
- ▁INTERCOURSE
- ▁STRICKEN
- ▁SUPPLIES
- ▁LANDLORD
- ▁SHRINK
- ▁PRICK
- ▁CAESAR
- ▁DRUG
- ▁BEWILDERED
- ▁NAUTILUS
- ▁BRUTAL
- ▁COMMERCIAL
- ▁MAGGIE
- ▁SPHERE
- ▁VIRGIN
- ▁BRETHREN
- ▁DESTINY
- ▁POLICY
- ▁TERRIFIED
- ▁HOUSEKEEPER
- ▁CRAZY
- ▁ARDENT
- ▁DISCERN
- ▁WRAP
- ▁MARQUIS
- ▁RUSSIA
- MOUTH
- ▁BRITAIN
- ▁HARBOUR
- ▁CONCERT
- ▁DONKEY
- ▁DAMAGE
- ▁SLIM
- ABOUT
- ▁LUXURY
- ▁MONSTROUS
- ▁TENDENCY
- ▁PARADISE
- ▁CULTURE
- ▁JULIUS
- ▁RAOUL
- ▁REMEDY
- ▁DECAY
- ▁SCOLD
- ▁SPLIT
- ▁ASSAULT
- ▁DECEMBER
- ▁MOSCOW
- ▁EXPLORE
- ▁TROUSERS
- ▁WRIST
- PIECE
- ▁MUSKET
- ▁VALENTINE
- ▁TYRANT
- ▁ABRAHAM
- ▁MEDIUM
- ▁ARTIFICIAL
- ▁FACULTY
- ▁OBLIGATION
- ▁RESEMBLANCE
- ▁INQUIRIES
- ▁DETAIN
- ▁SWARM
- ▁PLEDGE
- ▁ADMIRABLE
- ▁DEFECT
- ▁SUPERINTEND
- ▁PATRIOT
- ▁CLUNG
- ▁DISMAL
- ▁RECIT
- ▁IGNOR
- ▁AMELIA
- ▁JUSTIFY
- ▁ELEPHANT
- ▁ESTIMATE
- ▁KNELT
- ▁SERVING
- ▁WHIM
- ▁SHRILL
- ▁STUDIO
- ▁TEXT
- ▁ALEXANDER
- ▁WROUGHT
- ▁ABUNDANT
- ▁SITUATED
- ▁REGAIN
- ▁FIERY
- ▁SNEER
- ▁SWEAT
- ▁GLARE
- ▁NIGH
- ▁ESCORT
- ▁INEVITABLE
- ▁PSMITH
- ▁RELUCTANT
- ▁PRECEDING
- ▁RESORT
- ▁OUTRAGE
- ▁AMBASSADOR
- ▁CONSOLATION
- ▁RECOGNITION
- ▁REMORSE
- ▁BEHALF
- ▁FORMIDABLE
- ▁GRAVITY
- ▁DIVIDE
- ▁CONFRONT
- ▁GIGANTIC
- ▁OCTOBER
- ▁FLANK
- ▁SLEW
- ▁CLARA
- ▁FILM
- ▁BULK
- ▁POMP
- ▁ELEANOR
- ▁EMPHASIS
- ▁JAPANESE
- ▁CAVALRY
- ▁EXCLUSIVE
- ▁PERFUME
- ▁BRONZE
- ▁FEDERAL
- ▁LIQUID
- ▁RUBBING
- ▁OVEN
- DOLPH
- ▁CONVULS
- ▁DEPRIVED
- ▁RESPONSIBILITY
- ▁SIGNIFICANT
- ▁WAISTCOAT
- ▁CLUSTER
- ▁MARTHA
- ▁REVERSE
- ▁ATTORNEY
- ▁DROOP
- ▁SKILFUL
- ▁HABITUAL
- ▁PUMP
- ▁INTERVEN
- ▁OWL
- ▁CONJECTURE
- ▁FANTASTIC
- ▁RESPONSIBLE
- ▁DESTINED
- ▁DOCUMENT
- ▁THEREUPON
- ▁GODDESS
- ▁PACIFIC
- ▁WARRANT
- ▁COSTUME
- ▁BRIDLE
- ▁CALIFORNIA
- ▁DEMOCRATIC
- ▁EUSTACE
- ▁SQUIRREL
- ▁UNCOMMON
- ▁MARVELLOUS
- ▁PLOUGH
- ▁TRAGEDY
- ▁VAULT
- ▁HESITATE
- ▁REFRAIN
- ▁ADMIRING
- ▁CORPORAL
- ▁ENTITLED
- ▁SHREWD
- ▁SQUEEZ
- ▁ACCURATE
- ▁TEMPEST
- ▁MONUMENT
- ▁SIEGE
- ▁CHINESE
- ▁RAVEN
- ▁LOUNG
- ▁ASSASSIN
- ▁INFLICT
- ▁AGITATED
- ▁DESIRABLE
- ▁EARLIEST
- ▁LAUNCH
- ▁PILOT
- ▁PULSE
- ▁MUTE
- LEIGH
- ▁LIQUOR
- ▁SCARECROW
- ▁SKULL
- ▁DESOLATE
- ▁SUBLIME
- ▁SERENE
- ▁RECESS
- ▁WAKING
- ▁CHARLOTTE
- ▁CIRCULAR
- ▁INJUSTICE
- ▁PINOCCHIO
- ▁PRISCILLA
- ▁THYSELF
- ▁OCCURRENCE
- ▁CASUAL
- ▁FRANTIC
- ▁LEGEND
- ▁FERTIL
- ▁BACKGROUND
- ▁DELICACY
- ▁ESTRALLA
- ▁MANUSCRIPT
- ▁RESPONSE
- ▁UNIVERSITY
- ▁WOLVES
- ▁SCANDAL
- ▁STUMBLE
- ▁HOARSE
- ▁BODILY
- ▁CONVENT
- ▁EXAMINING
- ▁INCAPABLE
- ▁PERCEIVING
- ▁PHILADELPHIA
- ▁SUBSEQUENT
- ▁THIEVES
- ▁ACCUMULAT
- ▁DAMSEL
- ▁SCOTCH
- ▁UNDERNEATH
- ▁NOBILITY
- ▁SMASH
- ▁REVOLT
- ▁ENGAGE
- ▁CATHEDRAL
- ▁CHAMPION
- ▁DESPATCH
- ▁ETERNITY
- ▁JANUARY
- ▁PLEADED
- ▁PROBABILITY
- ▁JIMMIE
- ▁PARALLEL
- ▁FISHERMAN
- ▁JERRY
- ▁SWORE
- ▁DRAUGHT
- ▁OPPONENT
- ▁PRIMITIVE
- ▁SIGNIFICANCE
- ▁SUBSTANTIAL
- ▁AMAZED
- ▁DUNBAR
- ▁COMMEND
- ▁CONTEMPLATE
- ▁TESTIMONY
- ▁IMPERIAL
- ▁ADAPT
- ▁JUICE
- ▁CALAMIT
- CULAR
- ▁CHATEAU
- ▁PHOENIX
- ▁PRUDENT
- ▁SOLUTION
- ▁VILLEFORT
- ▁REACTION
- ▁RELAX
- ▁YU
- ▁PROHIBIT
- ▁DISTRUST
- ▁PLUNDER
- ▁WELFARE
- ▁NAVIGAT
- ▁PARLOR
- ▁LAZY
- ▁DETACH
- OMETER
- ▁PRIV
- ▁DISCOURAGE
- ▁OBSTINATE
- ▁REJOICING
- ▁SERMON
- ▁VEHICLE
- ▁FANCIES
- ▁ENLIGHTEN
- ▁ACUTE
- ▁ILLUSION
- ▁ANTHEA
- ▁MARTIAN
- ▁EXCITE
- ▁GENEROSITY
- OLOGIST
- ▁AMAZING
- ▁UNWORTHY
- ▁INTERNAL
- ▁INCENSE
- ▁VIBRAT
- ▁ADHERE
- ROACH
- ▁FEBRUARY
- ▁MEXICAN
- ▁POTATOES
- ▁INCESSANT
- ▁INTERPOSED
- ▁PARCEL
- ▁VEXED
- ▁PROMOTE
- MIDST
- ▁ARISTOCRAT
- ▁CYRIL
- ▁EMBARK
- ▁ABUNDANCE
- ▁LITERALLY
- ▁SURGEON
- ▁TERRACE
- ▁ATLANTIC
- ▁MARTYR
- ▁SPECK
- ▁SENATE
- ▁LOAF
- ▁ADMINISTER
- ▁APPREHEND
- ▁SUBDUED
- ▁TEMPORARY
- ▁DOMINION
- ▁ELABORATE
- ▁DIGNIFIED
- ▁ELIZA
- ▁SPLASH
- ▁CONSEIL
- ▁DEXTER
- ▁UNSEEN
- ▁TRAGIC
- VOCATION
- ▁GRATIFY
- ▁BACHELOR
- ▁DEFENSE
- ▁EXCURSION
- ▁FACULTIES
- ▁PROPRIETOR
- ▁SYMPATHETIC
- ▁UNNECESSARY
- ▁RADIANT
- ▁VACANT
- ▁OUNCE
- ▁SCREW
- ▁PHENOMENON
- ▁PROMINENT
- ▁WORRIED
- ▁STUDIES
- ▁CLIMATE
- ▁KEITH
- ▁ARAMIS
- ▁BLISS
- ▁CONTINUAL
- ▁SURPASS
- ▁HEBREW
- ▁IDENTITY
- ▁PROVOKE
- ▁TEMPERAMENT
- ▁CHARIOT
- ▁HARBOR
- ▁NINTH
- ▁PRIOR
- ▁DESIROUS
- ▁JERUSALEM
- ▁UNDERTAKING
- ▁EDISON
- ▁MIRTH
- ▁SCOUT
- ▁APPARATUS
- ▁ILLUSTRATION
- ▁INTELLIGIBLE
- ▁INVARIABLY
- ▁PIERCED
- ▁REVIEW
- ▁FLICKER
- ▁HAZARD
- ▁REVELATION
- ▁DIXON
- ▁EXCITING
- ▁GOSPEL
- ▁CONSTANCE
- ▁OVERTAKE
- ▁GUINEA
- ▁ALADDIN
- ▁CHICAGO
- ▁TULLIVER
- ▁HAMILTON
- ▁GARRISON
- ▁DISCIPLE
- ▁INTENSITY
- ▁TRAITOR
- ▁CHANCELLOR
- ▁PROVERB
- ▁DAGGER
- ▁FORESEE
- ▁CONFIDE
- ▁GLIMMER
- ▁CHAUVELIN
- ▁ILLUSTRATE
- ▁VOLUNTEER
- ▁JUNGLE
- ▁STREAK
- ▁SUNRISE
- ▁DISSOLV
- ▁QUEST
- ▁AWHILE
- ▁FELICITY
- ▁LEGISLATURE
- ▁LEONORA
- ▁MAGAZINE
- ▁PITIFUL
- ▁COLONY
- ▁SHAWL
- ▁ARRIVING
- ▁FUNDAMENTAL
- ▁CARPENTER
- ▁OVERFLOW
- ▁EXPAND
- ▁HARVEST
- ▁FEMININE
- ▁INNUMERABLE
- ▁SCRAMBLE
- ▁TWENTIETH
- ▁TRIFLING
- ▁GHASTL
- ▁CONQUEST
- ▁DANIEL
- ▁FACILIT
- ▁FORSAKE
- ▁BEHAVIOUR
- ▁GORGEOUS
- ▁PRODUCING
- ▁HAPPIER
- ▁PROMISING
- ▁RAINBOW
- ▁INSTINCTIVELY
- ▁DECREE
- ▁EYEBROWS
- ▁IRRESISTIBLE
- ▁PHARAOH
- ▁SCROOGE
- ▁UNNATURAL
- ▁CRUMBS
- ▁REFINED
- ▁DREARY
- ▁TRENCH
- ▁CONVINCE
- ▁FRINGE
- ▁EXTREMITY
- ▁INTIMACY
- ▁SCOUNDREL
- ▁SUFFRAGE
- ▁UNEASINESS
- ▁BARRICADE
- ▁CIRCULAT
- ▁SAMUEL
- ▁BRUCE
- ▁DARCY
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
n_fft: 512
hop_length: 256
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 10
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
stochastic_depth_rate:
- 0.0
- 0.0
- 0.0
- 0.0
- 0.0
- 0.0
- 0.1
- 0.1
- 0.1
- 0.1
- 0.1
- 0.1
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.7a1
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
hyechanjun/interview-question-remake
|
hyechanjun
| 2022-03-07T17:57:47Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T17:48:30Z |
---
datasets:
- "INTERVIEW: NPR Media Dialog Transcripts"
---
# AI Interviewer Question-Asking Model
For a Senior Project at Calvin University
Created by: Hyechan Jun, Ha-Ram Koo, and Advait Scaria
This model is fine-tuned on facebook/bart-base to generate sequences ending in a question mark (?). It is a remake of an earlier model that had errors in its training and validation datasets.
|
Kuray107/librispeech-semi-supervised-without-LM
|
Kuray107
| 2022-03-07T17:14:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-07T03:31:57Z |
---
tags:
- generated_from_trainer
model-index:
- name: librispeech-semi-supervised-without-LM
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. -->
# librispeech-semi-supervised-without-LM
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1837
- Wer: 0.0580
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- 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: 1000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0565 | 0.56 | 1000 | 0.1354 | 0.0641 |
| 0.0548 | 1.12 | 2000 | 0.1320 | 0.0628 |
| 0.0478 | 1.68 | 3000 | 0.1247 | 0.0612 |
| 0.0451 | 2.24 | 4000 | 0.1256 | 0.0613 |
| 0.0401 | 2.8 | 5000 | 0.1269 | 0.0606 |
| 0.035 | 3.36 | 6000 | 0.1370 | 0.0595 |
| 0.0344 | 3.92 | 7000 | 0.1280 | 0.0589 |
| 0.031 | 4.48 | 8000 | 0.1350 | 0.0589 |
| 0.031 | 5.04 | 9000 | 0.1418 | 0.0614 |
| 0.0278 | 5.61 | 10000 | 0.1382 | 0.0604 |
| 0.0272 | 6.17 | 11000 | 0.1502 | 0.0615 |
| 0.0246 | 6.73 | 12000 | 0.1443 | 0.0609 |
| 0.0233 | 7.29 | 13000 | 0.1548 | 0.0589 |
| 0.0224 | 7.85 | 14000 | 0.1547 | 0.0599 |
| 0.0202 | 8.41 | 15000 | 0.1570 | 0.0590 |
| 0.0199 | 8.97 | 16000 | 0.1564 | 0.0594 |
| 0.0186 | 9.53 | 17000 | 0.1598 | 0.0595 |
| 0.0187 | 10.09 | 18000 | 0.1657 | 0.0585 |
| 0.017 | 10.65 | 19000 | 0.1690 | 0.0584 |
| 0.016 | 11.21 | 20000 | 0.1689 | 0.0588 |
| 0.0156 | 11.77 | 21000 | 0.1745 | 0.0585 |
| 0.0151 | 12.33 | 22000 | 0.1777 | 0.0583 |
| 0.0144 | 12.89 | 23000 | 0.1778 | 0.0590 |
| 0.0142 | 13.45 | 24000 | 0.1803 | 0.0585 |
| 0.0137 | 14.01 | 25000 | 0.1796 | 0.0581 |
| 0.0132 | 14.57 | 26000 | 0.1837 | 0.0580 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
kenjis2542/mt5-small-finetuned-5k-th-to-en
|
kenjis2542
| 2022-03-07T14:11:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T12:49:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-5k-th-to-en
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. -->
# mt5-small-finetuned-5k-th-to-en
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
Splend1dchan/byt5small-glue-mprc2
|
Splend1dchan
| 2022-03-07T12:47:22Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T12:28:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: byt5small-glue-mprc2
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. -->
# byt5small-glue-mprc2
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.6.0a0+bf2bbd9
- Datasets 1.12.1
- Tokenizers 0.11.6
|
anjandash/finetuned-bert-java-cmpx-v1
|
anjandash
| 2022-03-07T12:19:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"dataset:giganticode/java-cmpx-v1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-07T11:56:12Z |
---
language:
- java
license: mit
datasets:
- giganticode/java-cmpx-v1
---
|
cammy/bart-large-cnn-1000-pad-early-lit
|
cammy
| 2022-03-07T10:56:33Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T10:31:23Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-1000-pad-early-lit
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. -->
# bart-large-cnn-1000-pad-early-lit
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4800
- Rouge1: 28.4538
- Rouge2: 13.5656
- Rougel: 22.2066
- Rougelsum: 25.3361
- Gen Len: 66.53
## 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: 1
- eval_batch_size: 1
- seed: 42
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.1556 | 1.0 | 1000 | 0.4383 | 29.1275 | 14.1415 | 22.5802 | 26.37 | 65.93 |
| 0.0853 | 2.0 | 2000 | 0.4800 | 28.4538 | 13.5656 | 22.2066 | 25.3361 | 66.53 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
spy24/autonlp-parrot_paraphrasing-615317556
|
spy24
| 2022-03-07T09:36:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:spy24/autonlp-data-parrot_paraphrasing",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T09:35:01Z |
---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- spy24/autonlp-data-parrot_paraphrasing
co2_eq_emissions: 0.8335491678002559
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 615317556
- CO2 Emissions (in grams): 0.8335491678002559
## Validation Metrics
- Loss: 0.0001514342293376103
- Rouge1: 100.0
- Rouge2: 51.4451
- RougeL: 100.0
- RougeLsum: 100.0
- Gen Len: 4.104
## 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 AutoNLP"}' https://api-inference.huggingface.co/spy24/autonlp-parrot_paraphrasing-615317556
```
|
spy24/autonlp-optimized-paraphrasing-615217541
|
spy24
| 2022-03-07T08:56:14Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:spy24/autonlp-data-optimized-paraphrasing",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T08:54:32Z |
---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- spy24/autonlp-data-optimized-paraphrasing
co2_eq_emissions: 1.166696812121839
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 615217541
- CO2 Emissions (in grams): 1.166696812121839
## Validation Metrics
- Loss: 0.00019549368880689144
- Rouge1: 100.0
- Rouge2: 51.4451
- RougeL: 100.0
- RougeLsum: 100.0
- Gen Len: 4.104
## 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 AutoNLP"}' https://api-inference.huggingface.co/spy24/autonlp-optimized-paraphrasing-615217541
```
|
diwank/silicone-deberta-pair
|
diwank
| 2022-03-07T08:43:13Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"deberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
---
# diwank/silicone-deberta-pair
`deberta-base`-based dialog acts classifier. Trained on the `balanced` variant of the [silicone-merged](https://huggingface.co/datasets/diwank/silicone-merged) dataset: a simplified merged dialog act data from datasets in the [silicone](https://huggingface.co/datasets/silicone) collection.
Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. **Outputs one of 11 labels**:
```python
(0, 'acknowledge')
(1, 'answer')
(2, 'backchannel')
(3, 'reply_yes')
(4, 'exclaim')
(5, 'say')
(6, 'reply_no')
(7, 'hold')
(8, 'ask')
(9, 'intent')
(10, 'ask_yes_no')
```
## Example:
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/silicone-deberta-pair")
convert_to_label = lambda n: [
['acknowledge',
'answer',
'backchannel',
'reply_yes',
'exclaim',
'say',
'reply_no',
'hold',
'ask',
'intent',
'ask_yes_no'
][i] for i in n
]
predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]])
convert_to_label(predictions) # answer
```
## Report from W&B
https://wandb.ai/diwank/da-silicone-combined/reports/silicone-deberta-pair--VmlldzoxNTczNjE5?accessToken=yj1jz4c365z0y5b3olgzye7qgsl7qv9lxvqhmfhtb6300hql6veqa5xiq1skn8ys
|
AdapterHub/bioASQfactoid
|
AdapterHub
| 2022-03-07T08:19:22Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:qa/bioasq",
"bart",
"region:us"
] | null | 2022-03-07T08:19:06Z |
---
tags:
- adapterhub:qa/bioasq
- adapter-transformers
- bart
---
# Adapter `AdapterHub/bioASQfactoid` for facebook/bart-base
An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [qa/bioasq](https://adapterhub.ml/explore/qa/bioasq/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("facebook/bart-base")
adapter_name = model.load_adapter("AdapterHub/bioASQfactoid", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
cammy/bart-large-cnn-finetuned-weaksup-1000-pad-early-new1
|
cammy
| 2022-03-07T06:18:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T06:01:26Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-weaksup-1000-pad-early-new1
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. -->
# bart-large-cnn-finetuned-weaksup-1000-pad-early-new1
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4948
- Rouge1: 28.1465
- Rouge2: 13.4076
- Rougel: 22.2763
- Rougelsum: 25.2087
- Gen Len: 68.58
## 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: 1
- eval_batch_size: 1
- seed: 42
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.156 | 1.0 | 1000 | 0.4377 | 27.8782 | 13.1274 | 21.2329 | 24.6465 | 66.25 |
| 0.0843 | 2.0 | 2000 | 0.4948 | 28.1465 | 13.4076 | 22.2763 | 25.2087 | 68.58 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
cammy/bart-large-cnn-1000-sum-pad-early-tfidf1
|
cammy
| 2022-03-07T05:57:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T05:28:36Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-1000-sum-pad-early-tfidf1
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. -->
# bart-large-cnn-1000-sum-pad-early-tfidf1
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8527
- Rouge1: 24.6303
- Rouge2: 11.0396
- Rougel: 19.1384
- Rougelsum: 20.94
- Gen Len: 67.84
## 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: 1
- eval_batch_size: 1
- seed: 42
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.3304 | 1.0 | 1000 | 0.7234 | 25.9428 | 12.5482 | 21.0784 | 23.6041 | 64.68 |
| 0.1502 | 2.0 | 2000 | 0.8527 | 24.6303 | 11.0396 | 19.1384 | 20.94 | 67.84 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
SAI2-EXP/TNANA-th-th
|
SAI2-EXP
| 2022-03-07T05:56:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-07T05:49:43Z |
---
license: apache-2.0
---
|
timothyshi/bart-large-cnn-finetuned-booksum-chapter
|
timothyshi
| 2022-03-07T05:13:01Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-04T20:32:40Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-booksum-chapter
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. -->
# bart-large-cnn-finetuned-booksum-chapter
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1373
- Rouge1: 18.1222
- Rouge2: 3.5783
- Rougel: 13.4084
- Rougelsum: 13.5832
- Gen Len: 63.5121
## 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: 1
- eval_batch_size: 1
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.5297 | 1.0 | 23094 | 3.1373 | 18.1222 | 3.5783 | 13.4084 | 13.5832 | 63.5121 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
billfrench/autonlp-cyberlandr-ai-4-614417501
|
billfrench
| 2022-03-07T00:57:12Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:billfrench/autonlp-data-cyberlandr-ai-4",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-07T00:54:15Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- billfrench/autonlp-data-cyberlandr-ai-4
co2_eq_emissions: 1.6912535041856878
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 614417501
- CO2 Emissions (in grams): 1.6912535041856878
## Validation Metrics
- Loss: 1.305419921875
- Accuracy: 0.5
- Macro F1: 0.3333333333333333
- Micro F1: 0.5
- Weighted F1: 0.4444444444444444
- Macro Precision: 0.375
- Micro Precision: 0.5
- Weighted Precision: 0.5
- Macro Recall: 0.375
- Micro Recall: 0.5
- Weighted Recall: 0.5
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/billfrench/autonlp-cyberlandr-ai-4-614417501
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417501", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417501", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
smartiros/BERT_for_sentiment_50k_2_epochs_preprocessed
|
smartiros
| 2022-03-07T00:22:36Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-07T00:22:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: tmpmrwiph1p
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmpmrwiph1p
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1382
- Train Accuracy: 0.9482
- Validation Loss: 0.7241
- Validation Accuracy: 0.8109
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3773 | 0.8313 | 0.4627 | 0.8131 | 0 |
| 0.1382 | 0.9482 | 0.7241 | 0.8109 | 1 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Tokenizers 0.11.6
|
PhilSad/GPT-J6B-Guided-SCP
|
PhilSad
| 2022-03-06T22:52:07Z | 9 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Attempt of guided text generation to replace GPT-3 for :[This SCP Does Not Exist](https://www.thisscpdoesnotexist.ml)
Work in Porgress
Finetuned on a dataset of 1700 automatically generated samples from the [official SCP wiki](https://scp-wiki.wikidot.com/)
Exemple input :
```Prompt: SCP-9741 is a pair of jeans that looks really cool ### Generation: Item #: SCP-9741\nObject Class: Safe\nSpecial Containment Procedures:```
# Acknowledgment
This work was made possible thanks to the TPU Research Cloud program by Google
|
Kevincp560/distilbart-cnn-12-6-finetuned-pubmed
|
Kevincp560
| 2022-03-06T22:33:03Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-06T16:25:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-finetuned-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 40.0985
---
<!-- 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. -->
# distilbart-cnn-12-6-finetuned-pubmed
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9895
- Rouge1: 40.0985
- Rouge2: 16.5016
- Rougel: 24.8319
- Rougelsum: 36.0775
- Gen Len: 141.884
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.1709 | 1.0 | 4000 | 2.0257 | 38.1012 | 15.112 | 23.4064 | 33.9373 | 141.9195 |
| 1.9495 | 2.0 | 8000 | 1.9593 | 39.529 | 16.1693 | 24.487 | 35.5238 | 141.9785 |
| 1.756 | 3.0 | 12000 | 1.9488 | 39.9623 | 16.5799 | 24.949 | 35.9194 | 141.8855 |
| 1.6032 | 4.0 | 16000 | 1.9732 | 39.672 | 16.1994 | 24.5996 | 35.7021 | 141.921 |
| 1.4817 | 5.0 | 20000 | 1.9895 | 40.0985 | 16.5016 | 24.8319 | 36.0775 | 141.884 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Ayham/roberta_ernie_summarization_cnn_dailymail
|
Ayham
| 2022-03-06T22:01:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-06T14:27:11Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: roberta_ernie_summarization_cnn_dailymail
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
clu-ling/roberta-finetuned-stsbenchmark
|
clu-ling
| 2022-03-06T21:32:04Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-06T19:55:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer, util
query = "What is the large instrument the man is playing?"
docs = ["A man is playing a large flute.", "A man is playing a flute."]
#Load the model
model = SentenceTransformer('clu-ling/roberta-finetuned-stsbenchmark')
#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)
#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 125 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Kuray107/swbd-5percent-supervised
|
Kuray107
| 2022-03-06T16:14:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-05T15:36:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: swbd-5percent-supervised
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. -->
# swbd-5percent-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6970
- Wer: 0.1352
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.8534 | 0.64 | 1000 | 2.9535 | 1.0 |
| 1.8605 | 1.28 | 2000 | 0.7878 | 0.3719 |
| 0.9862 | 1.92 | 3000 | 0.5906 | 0.2684 |
| 0.8405 | 2.56 | 4000 | 0.5555 | 0.2151 |
| 0.6972 | 3.2 | 5000 | 0.5905 | 0.1992 |
| 0.6033 | 3.84 | 6000 | 0.4867 | 0.1781 |
| 0.5393 | 4.48 | 7000 | 0.5447 | 0.1805 |
| 0.529 | 5.12 | 8000 | 0.5398 | 0.1746 |
| 0.5072 | 5.77 | 9000 | 0.5093 | 0.1706 |
| 0.4331 | 6.41 | 10000 | 0.4990 | 0.1627 |
| 0.4837 | 7.05 | 11000 | 0.5319 | 0.1634 |
| 0.3867 | 7.69 | 12000 | 0.4866 | 0.1595 |
| 0.345 | 8.33 | 13000 | 0.5202 | 0.1582 |
| 0.372 | 8.97 | 14000 | 0.5396 | 0.1547 |
| 0.355 | 9.61 | 15000 | 0.5992 | 0.1493 |
| 0.3258 | 10.25 | 16000 | 0.5247 | 0.1527 |
| 0.3327 | 10.89 | 17000 | 0.5664 | 0.1512 |
| 0.3422 | 11.53 | 18000 | 0.5819 | 0.1456 |
| 0.2815 | 12.17 | 19000 | 0.5692 | 0.1453 |
| 0.2719 | 12.81 | 20000 | 0.5012 | 0.1476 |
| 0.2838 | 13.45 | 21000 | 0.5286 | 0.1454 |
| 0.2418 | 14.09 | 22000 | 0.6238 | 0.1486 |
| 0.2412 | 14.73 | 23000 | 0.5889 | 0.1456 |
| 0.2227 | 15.37 | 24000 | 0.5901 | 0.1459 |
| 0.2129 | 16.02 | 25000 | 0.5959 | 0.1454 |
| 0.2071 | 16.66 | 26000 | 0.6259 | 0.1427 |
| 0.2185 | 17.3 | 27000 | 0.6581 | 0.1437 |
| 0.1982 | 17.94 | 28000 | 0.6194 | 0.1411 |
| 0.1928 | 18.58 | 29000 | 0.5940 | 0.1409 |
| 0.1885 | 19.22 | 30000 | 0.6733 | 0.1417 |
| 0.1835 | 19.86 | 31000 | 0.6363 | 0.1393 |
| 0.1756 | 20.5 | 32000 | 0.6675 | 0.1382 |
| 0.1776 | 21.14 | 33000 | 0.6147 | 0.1407 |
| 0.1758 | 21.78 | 34000 | 0.6405 | 0.1420 |
| 0.1645 | 22.42 | 35000 | 0.6999 | 0.1401 |
| 0.1631 | 23.06 | 36000 | 0.6224 | 0.1385 |
| 0.1494 | 23.7 | 37000 | 0.6639 | 0.1374 |
| 0.1472 | 24.34 | 38000 | 0.6471 | 0.1373 |
| 0.1514 | 24.98 | 39000 | 0.6570 | 0.1395 |
| 0.1527 | 25.62 | 40000 | 0.6876 | 0.1375 |
| 0.1514 | 26.27 | 41000 | 0.6835 | 0.1376 |
| 0.1344 | 26.91 | 42000 | 0.6987 | 0.1372 |
| 0.1267 | 27.55 | 43000 | 0.7026 | 0.1362 |
| 0.1384 | 28.19 | 44000 | 0.7021 | 0.1366 |
| 0.1264 | 28.83 | 45000 | 0.7016 | 0.1355 |
| 0.1227 | 29.47 | 46000 | 0.6970 | 0.1352 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
MaryaAI/opus-mt-ar-en-finetunedTanzil-v5-ar-to-en
|
MaryaAI
| 2022-03-06T14:37:08Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: opus-mt-ar-en-finetunedTanzil-v5-ar-to-en
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# opus-mt-ar-en-finetunedTanzil-v5-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8101
- Validation Loss: 0.9477
- Train Bleu: 9.3241
- Train Gen Len: 88.73
- Train Rouge1: 56.4906
- Train Rouge2: 34.2668
- Train Rougel: 53.2279
- Train Rougelsum: 53.7836
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Bleu | Train Gen Len | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:------------:|:------------:|:------------:|:---------------:|:-----:|
| 0.8735 | 0.9809 | 11.0863 | 78.68 | 56.4557 | 33.3673 | 53.4828 | 54.1197 | 0 |
| 0.8408 | 0.9647 | 9.8543 | 88.955 | 57.3797 | 34.3539 | 53.8783 | 54.3714 | 1 |
| 0.8101 | 0.9477 | 9.3241 | 88.73 | 56.4906 | 34.2668 | 53.2279 | 53.7836 | 2 |
### Framework versions
- Transformers 4.17.0.dev0
- TensorFlow 2.7.0
- Datasets 1.18.4.dev0
- Tokenizers 0.10.3
|
orisuchy/Descriptive_Classifier
|
orisuchy
| 2022-03-06T13:20:02Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"he",
"dataset:orisuchy/Descriptive_Sentences_He",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: afl-3.0
language: "he"
tags:
- Text Classification
widget:
- text: "היער השחור והגדול"
- text: "ואז הוא הלך לטייל בתוך היער השחור והגדול"
datasets:
- orisuchy/Descriptive_Sentences_He
metrics:
- accuracy
- f1
---
# **Descriptive Sentences Classifier**
Based on [AlephBERT](https://huggingface.co/onlplab/alephbert-base) model.
# **Metrics**
[accuracy](https://huggingface.co/metrics/accuracy): 0.813953488372093
</br>
[f1](https://huggingface.co/metrics/f1): 0.8181818181818182
## How to Use the model:
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='orisuchy/Descriptive_Classifier', return_all_scores=True)
outputs = classifier("מסווג חתיך במיוחד")
print(outputs)
"""
Output:
[[
{'label': 'Descriptive', 'score': 0.999764621257782},
{'label': 'Not Descriptive', 'score': 0.00023541577684227377}]]
"""
```
#### Or, if you want only the final class:
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='orisuchy/Descriptive_Classifier')
output = classifier("הלכתי אליו הביתה וחיכיתי")
print(output)
"""
Output:
[{'label': 'Not Descriptive', 'score': 0.999901533126831}]
"""
```
Created by Daniel Smotritsky & Ori Suchy
<br>
[GitHub](https://github.com/orisuchy/miniProject_DHU)
<iframe src="https://wandb.ai/orisuchy/huggingface/reports/Shared-panel-22-03-01-15-03-08--VmlldzoxNjI5MjM0?highlightShare" style="border:none;height:1024px;width:100%">
|
crabz/distil-slovakbert-upos
|
crabz
| 2022-03-06T12:38:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:universal_dependencies",
"model-index",
"autotrain_compatible",
"region:us"
] |
token-classification
| 2022-03-05T19:42:43Z |
---
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
inference: false
model-index:
- name: distil-slovakbert-upos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: universal_dependencies sk_snk
type: universal_dependencies
args: sk_snk
metrics:
- name: Precision
type: precision
value: 0.9771104035797263
- name: Recall
type: recall
value: 0.9785418821096173
- name: F1
type: f1
value: 0.9778256189451022
- name: Accuracy
type: accuracy
value: 0.9800851200513933
---
<!-- 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. -->
# distil-slovakbert-upos
This model is a fine-tuned version of [crabz/distil-slovakbert](https://huggingface.co/crabz/distil-slovakbert) on the universal_dependencies sk_snk dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Precision: 0.9771
- Recall: 0.9785
- F1: 0.9778
- Accuracy: 0.9801
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 266 | 0.2168 | 0.9570 | 0.9554 | 0.9562 | 0.9610 |
| 0.3935 | 2.0 | 532 | 0.1416 | 0.9723 | 0.9736 | 0.9730 | 0.9740 |
| 0.3935 | 3.0 | 798 | 0.1236 | 0.9722 | 0.9735 | 0.9728 | 0.9747 |
| 0.0664 | 4.0 | 1064 | 0.1195 | 0.9722 | 0.9741 | 0.9732 | 0.9766 |
| 0.0664 | 5.0 | 1330 | 0.1160 | 0.9764 | 0.9772 | 0.9768 | 0.9789 |
| 0.0377 | 6.0 | 1596 | 0.1194 | 0.9763 | 0.9776 | 0.9770 | 0.9790 |
| 0.0377 | 7.0 | 1862 | 0.1188 | 0.9740 | 0.9755 | 0.9748 | 0.9777 |
| 0.024 | 8.0 | 2128 | 0.1188 | 0.9762 | 0.9777 | 0.9769 | 0.9793 |
| 0.024 | 9.0 | 2394 | 0.1207 | 0.9774 | 0.9789 | 0.9781 | 0.9802 |
| 0.0184 | 10.0 | 2660 | 0.1207 | 0.9771 | 0.9785 | 0.9778 | 0.9801 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.11.0
|
crabz/distil-slovakbert
|
crabz
| 2022-03-06T12:30:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"sk",
"dataset:c4-sk",
"license:mit",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-03-04T15:42:01Z |
---
language: sk
license: mit
tags:
- fill-mask
- roberta
datasets:
- c4-sk
inference: false
---
|
crabz/bertoslav-limited-ner
|
crabz
| 2022-03-06T12:29:42Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"sk",
"dataset:wikiann",
"model-index",
"autotrain_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
inference: false
language:
- sk
model-index:
- name: bertoslav-limited-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann sk
type: wikiann
args: sk
metrics:
- name: Precision
type: precision
value: 0.8985571260306242
- name: Recall
type: recall
value: 0.9173994738819993
- name: F1
type: f1
value: 0.9078805459481573
- name: Accuracy
type: accuracy
value: 0.9700235061239639
---
# Named Entity Recognition based on bertoslav-limited
This model is a fine-tuned version of [crabz/bertoslav-limited](https://huggingface.co/crabz/bertoslav-limited) on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2119
- Precision: 0.8986
- Recall: 0.9174
- F1: 0.9079
- Accuracy: 0.9700
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2953 | 1.0 | 834 | 0.1516 | 0.8413 | 0.8647 | 0.8529 | 0.9549 |
| 0.0975 | 2.0 | 1668 | 0.1304 | 0.8787 | 0.9056 | 0.8920 | 0.9658 |
| 0.0487 | 3.0 | 2502 | 0.1405 | 0.8916 | 0.8958 | 0.8937 | 0.9660 |
| 0.025 | 4.0 | 3336 | 0.1658 | 0.8850 | 0.9116 | 0.8981 | 0.9669 |
| 0.0161 | 5.0 | 4170 | 0.1739 | 0.8974 | 0.9127 | 0.9050 | 0.9693 |
| 0.0074 | 6.0 | 5004 | 0.1888 | 0.8900 | 0.9144 | 0.9020 | 0.9687 |
| 0.0051 | 7.0 | 5838 | 0.1996 | 0.8946 | 0.9145 | 0.9044 | 0.9693 |
| 0.0039 | 8.0 | 6672 | 0.2052 | 0.8993 | 0.9158 | 0.9075 | 0.9697 |
| 0.0024 | 9.0 | 7506 | 0.2112 | 0.8946 | 0.9171 | 0.9057 | 0.9696 |
| 0.0018 | 10.0 | 8340 | 0.2119 | 0.8986 | 0.9174 | 0.9079 | 0.9700 |
### Framework versions
- Transformers 4.14.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AG/pretraining
|
AG
| 2022-03-06T12:27:50Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:04Z |
Pre trained on clus_ chapter only.
|
swcrazyfan/Dekingify-T5-Large
|
swcrazyfan
| 2022-03-06T09:44:13Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
|
Kuray107/librispeech-100h-supervised
|
Kuray107
| 2022-03-06T08:07:22Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: librispeech-100h-supervised
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. -->
# librispeech-100h-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0955
- Wer: 0.0345
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- 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: 1000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.8277 | 0.42 | 500 | 2.9071 | 1.0 |
| 2.0261 | 0.84 | 1000 | 0.3060 | 0.2496 |
| 0.2181 | 1.26 | 1500 | 0.1172 | 0.0873 |
| 0.1255 | 1.68 | 2000 | 0.0894 | 0.0637 |
| 0.0971 | 2.1 | 2500 | 0.0821 | 0.0560 |
| 0.078 | 2.52 | 3000 | 0.0751 | 0.0500 |
| 0.0706 | 2.94 | 3500 | 0.0721 | 0.0456 |
| 0.0609 | 3.36 | 4000 | 0.0755 | 0.0464 |
| 0.0572 | 3.78 | 4500 | 0.0705 | 0.0431 |
| 0.0528 | 4.2 | 5000 | 0.0715 | 0.0423 |
| 0.0481 | 4.62 | 5500 | 0.0691 | 0.0403 |
| 0.0471 | 5.04 | 6000 | 0.0743 | 0.0401 |
| 0.0412 | 5.46 | 6500 | 0.0757 | 0.0399 |
| 0.0416 | 5.88 | 7000 | 0.0688 | 0.0378 |
| 0.0391 | 6.3 | 7500 | 0.0704 | 0.0383 |
| 0.0367 | 6.72 | 8000 | 0.0742 | 0.0387 |
| 0.0349 | 7.14 | 8500 | 0.0732 | 0.0388 |
| 0.033 | 7.56 | 9000 | 0.0719 | 0.0374 |
| 0.0327 | 7.98 | 9500 | 0.0750 | 0.0369 |
| 0.0292 | 8.4 | 10000 | 0.0734 | 0.0368 |
| 0.0303 | 8.82 | 10500 | 0.0733 | 0.0365 |
| 0.0283 | 9.24 | 11000 | 0.0766 | 0.0357 |
| 0.0269 | 9.66 | 11500 | 0.0761 | 0.0350 |
| 0.0268 | 10.08 | 12000 | 0.0802 | 0.0359 |
| 0.0245 | 10.42 | 12500 | 0.0758 | 0.0354 |
| 0.023 | 10.84 | 13000 | 0.0775 | 0.0349 |
| 0.0186 | 11.26 | 13500 | 0.0817 | 0.0355 |
| 0.0176 | 11.68 | 14000 | 0.0853 | 0.0354 |
| 0.0163 | 12.1 | 14500 | 0.0880 | 0.0347 |
| 0.0156 | 12.52 | 15000 | 0.0864 | 0.0357 |
| 0.0141 | 12.94 | 15500 | 0.0897 | 0.0355 |
| 0.0134 | 13.36 | 16000 | 0.0915 | 0.0349 |
| 0.013 | 13.78 | 16500 | 0.0928 | 0.0350 |
| 0.0097 | 13.42 | 17000 | 0.0955 | 0.0345 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
clisi2000/distilbert-base-uncased-finetuned-emotion
|
clisi2000
| 2022-03-06T07:09:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-05T04:03:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9246284188099615
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2183
- Accuracy: 0.9245
- F1: 0.9246
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8174 | 1.0 | 250 | 0.3166 | 0.905 | 0.9023 |
| 0.2534 | 2.0 | 500 | 0.2183 | 0.9245 | 0.9246 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cpu
- Datasets 1.16.1
- Tokenizers 0.10.1
|
Kuray107/librispeech-5h-supervised
|
Kuray107
| 2022-03-06T06:43:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-05T23:00:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: librispeech-5h-supervised
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. -->
# librispeech-5h-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2041
- Wer: 0.0624
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- 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: 1000
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7758 | 11.11 | 1000 | 0.3120 | 0.2337 |
| 0.1238 | 22.22 | 2000 | 0.1651 | 0.0826 |
| 0.0383 | 33.33 | 3000 | 0.1667 | 0.0712 |
| 0.023 | 44.44 | 4000 | 0.1893 | 0.0685 |
| 0.0166 | 55.56 | 5000 | 0.2008 | 0.0666 |
| 0.0131 | 66.67 | 6000 | 0.1942 | 0.0639 |
| 0.0106 | 77.78 | 7000 | 0.1979 | 0.0628 |
| 0.0091 | 88.89 | 8000 | 0.2027 | 0.0628 |
| 0.008 | 100.0 | 9000 | 0.2041 | 0.0624 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
xinzhel/gpt2-ag-news
|
xinzhel
| 2022-03-06T00:08:03Z | 33 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-05T04:44:40Z |
---
license: apache-2.0
---
|
BigSalmon/Points3
|
BigSalmon
| 2022-03-05T22:03:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-05T21:39:49Z |
Example Prompt:
```
###
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
###
-
```
|
nimrah/my-wav2vec2-base-timit-demo-colab-my
|
nimrah
| 2022-03-05T17:06:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-05T15:19:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my-wav2vec2-base-timit-demo-colab-my
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-wav2vec2-base-timit-demo-colab-my
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5569
- Wer: 0.3481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4083 | 4.0 | 500 | 1.0932 | 0.7510 |
| 0.5536 | 8.0 | 1000 | 0.4965 | 0.4819 |
| 0.2242 | 12.0 | 1500 | 0.4779 | 0.4077 |
| 0.1249 | 16.0 | 2000 | 0.4921 | 0.4006 |
| 0.0844 | 20.0 | 2500 | 0.4809 | 0.3753 |
| 0.0613 | 24.0 | 3000 | 0.5307 | 0.3680 |
| 0.0459 | 28.0 | 3500 | 0.5569 | 0.3481 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
batterydata/batteryscibert-cased
|
batterydata
| 2022-03-05T16:11:45Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- batterypapers
---
# BatterySciBERT-cased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. It was introduced in
[this paper](paper_link) and first released in
[this repository](https://github.com/ShuHuang/batterybert). This model is case-sensitive: it makes a difference between english and English.
## Model description
BatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Training data
The BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt).
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 31,116. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='batterydata/batteryscibert-cased')
>>> unmasker("Hello I'm a <mask> model.")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-cased')
model = BertModel.from_pretrained('batterydata/batteryscibert-cased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-cased')
model = TFBertModel.from_pretrained('batterydata/batteryscibert-cased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
Final loss: 1.0505.
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batteryonlybert-cased
|
batterydata
| 2022-03-05T16:04:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-03T19:09:24Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- batterypapers
---
# BatteryOnlyBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in
[this paper](paper_link) and first released in
[this repository](https://github.com/ShuHuang/batterybert). This model is uncased: it does not make a difference
between english and English.
## Model description
BatteryOnlyBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Training data
The BatteryOnlyBERT model was pretrained on the full text of battery papers only. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt).
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 30,522. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 8 NVIDIA DGX A100 GPUs for 1,500,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='batterydata/batteryonlybert-uncased')
>>> unmasker("Hello I'm a <mask> model.")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('batterydata/batteryonlybert-uncased')
model = BertModel.from_pretrained('batterydata/batteryonlybert-uncased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('batterydata/batteryonlybert-uncased')
model = TFBertModel.from_pretrained('batterydata/batteryonlybert-uncased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
Final loss: 1.1012.
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batteryonlybert-cased-abstract
|
batterydata
| 2022-03-05T14:54:53Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryOnlyBERT-cased for Battery Abstract Classification
**Language model:** batteryonlybert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 14
base_LM_model = "batteryonlybert-cased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 97.33,
"Test accuracy": 97.34,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/batteryonlybert-cased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batterybert-cased-abstract
|
batterydata
| 2022-03-05T14:54:39Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryBERT-cased for Battery Abstract Classification
**Language model:** batterybert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 11
base_LM_model = "batterybert-cased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 97.29,
"Test accuracy": 96.85,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/batterybert-cased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batteryscibert-cased-abstract
|
batterydata
| 2022-03-05T14:54:32Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatterySciBERT-cased for Battery Abstract Classification
**Language model:** batteryscibert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 11
base_LM_model = "batteryscibert-cased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 97.06,
"Test accuracy": 97.19,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/batteryscibert-cased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batterybert-uncased-abstract
|
batterydata
| 2022-03-05T14:52:59Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryBERT-uncased for Battery Abstract Classification
**Language model:** batterybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 11
base_LM_model = "batterybert-uncased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 97.10,
"Test accuracy": 96.94,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/batterybert-uncased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/bert-base-uncased-abstract
|
batterydata
| 2022-03-05T14:44:13Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BERT-base-uncased for Battery Abstract Classification
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 13
base_LM_model = "bert-base-uncased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 96.79,
"Test accuracy": 96.29,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/bert-base-uncased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/bert-base-cased-abstract
|
batterydata
| 2022-03-05T14:42:16Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BERT-base-cased for Battery Abstract Classification
**Language model:** bert-base-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 15
base_LM_model = "bert-base-cased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 96.84,
"Test accuracy": 96.83,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/bert-base-cased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batterybert-cased-squad-v1
|
batterydata
| 2022-03-05T13:50:54Z | 5,999 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BatteryBERT-cased for QA
**Language model:** batterybert-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 16
n_epochs = 4
base_LM_model = "batterybert-cased"
max_seq_len = 386
learning_rate = 2e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 81.54,
"f1": 89.16,
```
Evaluated on the battery device dataset.
```
"precision": 70.74,
"recall": 84.19,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/batterybert-cased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
naam/xlm-roberta-base-finetuned-panx-de
|
naam
| 2022-03-05T13:48:33Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-05T13:36:41Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8594910162670748
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1348
- F1: 0.8595
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2556 | 1.0 | 525 | 0.1629 | 0.8218 |
| 0.1309 | 2.0 | 1050 | 0.1378 | 0.8522 |
| 0.0812 | 3.0 | 1575 | 0.1348 | 0.8595 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/ernie_ernie_summarization_cnn_dailymail
|
Ayham
| 2022-03-04T20:54:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-04T14:48:41Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: ernie_ernie_summarization_cnn_dailymail
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. -->
# ernie_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Kuray107/wsj0-5percent-supervised
|
Kuray107
| 2022-03-04T20:16:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-03T14:31:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wsj0-5percent-supervised
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. -->
# wsj0-5percent-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3883
- Wer: 0.1555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 12
- 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: 1000
- num_epochs: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.0248 | 16.67 | 500 | 2.9406 | 1.0 |
| 2.0466 | 33.33 | 1000 | 0.3935 | 0.3300 |
| 0.1486 | 50.0 | 1500 | 0.3091 | 0.1931 |
| 0.052 | 66.67 | 2000 | 0.3562 | 0.2052 |
| 0.0309 | 83.33 | 2500 | 0.3252 | 0.1773 |
| 0.0228 | 100.0 | 3000 | 0.3360 | 0.1652 |
| 0.0177 | 116.67 | 3500 | 0.3423 | 0.1603 |
| 0.0142 | 133.33 | 4000 | 0.3416 | 0.1611 |
| 0.0119 | 150.0 | 4500 | 0.3663 | 0.1583 |
| 0.0094 | 166.67 | 5000 | 0.3617 | 0.1567 |
| 0.0093 | 183.33 | 5500 | 0.3738 | 0.1668 |
| 0.0079 | 200.0 | 6000 | 0.3881 | 0.1652 |
| 0.0065 | 216.67 | 6500 | 0.3752 | 0.1611 |
| 0.0056 | 233.33 | 7000 | 0.3798 | 0.1603 |
| 0.0057 | 250.0 | 7500 | 0.3944 | 0.1624 |
| 0.0047 | 266.67 | 8000 | 0.4038 | 0.1583 |
| 0.0041 | 283.33 | 8500 | 0.3928 | 0.1547 |
| 0.0036 | 300.0 | 9000 | 0.3883 | 0.1555 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
nimrah/wav2vec2-large-xls-r-300m-turkish-colab-9
|
nimrah
| 2022-03-04T18:24:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-04T17:28:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab-9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab-9
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.
## 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: 0.03
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Kevincp560/distilbart-cnn-6-6-finetuned-pubmed
|
Kevincp560
| 2022-03-04T17:56:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-04T12:49:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: distilbart-cnn-6-6-finetuned-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 39.2769
---
<!-- 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. -->
# distilbart-cnn-6-6-finetuned-pubmed
This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0648
- Rouge1: 39.2769
- Rouge2: 15.876
- Rougel: 24.2306
- Rougelsum: 35.267
- Gen Len: 141.8565
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.2215 | 1.0 | 4000 | 2.0781 | 37.2476 | 14.2852 | 22.6875 | 33.1607 | 141.97 |
| 2.0105 | 2.0 | 8000 | 2.0217 | 37.8038 | 14.7869 | 23.2025 | 33.7069 | 141.918 |
| 1.8331 | 3.0 | 12000 | 2.0243 | 39.0497 | 15.8077 | 24.2237 | 34.9371 | 141.822 |
| 1.6936 | 4.0 | 16000 | 2.0487 | 38.7059 | 15.4364 | 23.8514 | 34.7771 | 141.878 |
| 1.5817 | 5.0 | 20000 | 2.0648 | 39.2769 | 15.876 | 24.2306 | 35.267 | 141.8565 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
azaninello/distilbert-base-uncased-finetuned-mushrooms
|
azaninello
| 2022-03-04T17:45:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-04T17:37:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-mushrooms
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. -->
# distilbert-base-uncased-finetuned-mushrooms
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4432
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.734 | 1.0 | 157 | 2.5275 |
| 2.5807 | 2.0 | 314 | 2.4169 |
| 2.5122 | 3.0 | 471 | 2.4352 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
daisyxie21/bert-base-uncased-8-10-0.01
|
daisyxie21
| 2022-03-04T16:27:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-04T14:27:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-8-10-0.01
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-8-10-0.01
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8324
- Matthews Correlation: 0.0
## 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: 0.01
- 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 400 | 0.8324 | 0.0 |
| 1.0904 | 2.0 | 800 | 1.3157 | 0.0 |
| 0.9461 | 3.0 | 1200 | 0.4407 | 0.0 |
| 0.9565 | 4.0 | 1600 | 2.1082 | 0.0 |
| 1.024 | 5.0 | 2000 | 0.7220 | 0.0 |
| 1.024 | 6.0 | 2400 | 0.7414 | 0.0 |
| 0.8362 | 7.0 | 2800 | 0.4442 | 0.0 |
| 0.6765 | 8.0 | 3200 | 0.5481 | 0.0 |
| 0.5902 | 9.0 | 3600 | 0.5642 | 0.0 |
| 0.5476 | 10.0 | 4000 | 0.4449 | 0.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
jiobiala24/wav2vec2-base-2
|
jiobiala24
| 2022-03-04T15:56:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-04T04:00:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-2
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-1](https://huggingface.co/jiobiala24/wav2vec2-base-1) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9415
- Wer: 0.3076
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4206 | 1.96 | 1000 | 0.6022 | 0.3435 |
| 0.3278 | 3.93 | 2000 | 0.6191 | 0.3344 |
| 0.2604 | 5.89 | 3000 | 0.6170 | 0.3288 |
| 0.2135 | 7.86 | 4000 | 0.6590 | 0.3239 |
| 0.1805 | 9.82 | 5000 | 0.7359 | 0.3289 |
| 0.1582 | 11.79 | 6000 | 0.7450 | 0.3276 |
| 0.1399 | 13.75 | 7000 | 0.7914 | 0.3218 |
| 0.1252 | 15.72 | 8000 | 0.8254 | 0.3185 |
| 0.1095 | 17.68 | 9000 | 0.8524 | 0.3184 |
| 0.1 | 19.65 | 10000 | 0.8340 | 0.3165 |
| 0.0905 | 21.61 | 11000 | 0.8846 | 0.3161 |
| 0.0819 | 23.58 | 12000 | 0.8994 | 0.3142 |
| 0.0763 | 25.54 | 13000 | 0.9018 | 0.3134 |
| 0.0726 | 27.5 | 14000 | 0.9552 | 0.3081 |
| 0.0668 | 29.47 | 15000 | 0.9415 | 0.3076 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
jish/distilgpt2-finetuned-wikitext2
|
jish
| 2022-03-04T15:14:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-04T14:44:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6423
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.633 | 2.0 | 4668 | 3.6455 |
| 3.6078 | 3.0 | 7002 | 3.6423 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
dragonSwing/wav2vec2-base-vn-270h
|
dragonSwing
| 2022-03-04T15:05:51Z | 81 | 8 |
speechbrain
|
[
"speechbrain",
"wav2vec2",
"audio",
"speech",
"Transformer",
"automatic-speech-recognition",
"vi",
"dataset:vivos",
"dataset:common_voice",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: vi
datasets:
- vivos
- common_voice
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- audio
- speech
- speechbrain
- Transformer
license: cc-by-nc-4.0
widget:
- example_title: Example 1
src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example.mp3
- example_title: Example 2
src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example2.mp3
model-index:
- name: Wav2vec2 Base Vietnamese 270h
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice vi
type: common_voice
args: vi
metrics:
- name: Test WER
type: wer
value: 9.66
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: vi
metrics:
- name: Test WER
type: wer
value: 5.57
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: vi
metrics:
- name: Test WER
type: wer
value: 5.76
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: VIVOS
type: vivos
args: vi
metrics:
- name: Test WER
type: wer
value: 3.70
---
# Wav2Vec2-Base-Vietnamese-270h
Fine-tuned Wav2Vec2 model on Vietnamese Speech Recognition task using about 270h labelled data combined from multiple datasets including [Common Voice](https://huggingface.co/datasets/common_voice), [VIVOS](https://huggingface.co/datasets/vivos), [VLSP2020](https://vlsp.org.vn/vlsp2020/eval/asr). The model was fine-tuned using SpeechBrain toolkit with a custom tokenizer. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io/).
When using this model, make sure that your speech input is sampled at 16kHz.
Please refer to [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) or [speechbrain](https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonVoice/ASR/CTC) on how to fine-tune Wav2Vec2 model on a specific language.
### Benchmark WER result:
| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |
|---|---|---|---|
|without LM| 8.23 | 12.15 | 12.15 |
|with 4-grams LM| 3.70 | 5.57 | 5.76 |
The language model was trained using [OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset on about 32GB of crawled text.
### Install SpeechBrain
To use this model, you should install speechbrain > 0.5.10
### Usage
The model can be used directly (without a language model) as follows:
```python
from speechbrain.pretrained import EncoderASR
model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi")
model.transcribe_file('dragonSwing/wav2vec2-base-vn-270h/example.mp3')
# Output: được hồ chí minh coi là một động lực lớn của sự phát triển đất nước
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Evaluation
The model can be evaluated as follows on the Vietnamese test data of Common Voice 8.0.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric, Audio
from transformers import Wav2Vec2FeatureExtractor
from speechbrain.pretrained import EncoderASR
import re
test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token=True)
test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16_000))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
wer = load_metric("wer")
extractor = Wav2Vec2FeatureExtractor.from_pretrained("dragonSwing/wav2vec2-base-vn-270h")
model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi", run_opts={'device': device})
chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]'
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
audio = batch["audio"]
batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
batch['speech'] = audio['array']
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
def evaluate(batch):
# For padding inputs only
inputs = extractor(
batch['speech'],
sampling_rate=16000,
return_tensors="pt",
padding=True,
do_normalize=False
).input_values
input_lens = torch.ones(inputs.shape[0])
pred_str, pred_tokens = model.transcribe_batch(inputs, input_lens)
batch["pred_strings"] = pred_str
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=1)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["target_text"])))
```
**Test Result**: 12.155553%
#### Citation
```
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
```
#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: [https://speechbrain.github.io](https://speechbrain.github.io/)
GitHub: [https://github.com/speechbrain/speechbrain](https://github.com/speechbrain/speechbrain)
|
augustoortiz/bert-finetuned-squad2
|
augustoortiz
| 2022-03-04T12:53:53Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: augustoortiz/bert-finetuned-squad2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# augustoortiz/bert-finetuned-squad2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2223
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11091, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2223 | 0 |
### Framework versions
- Transformers 4.17.0.dev0
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
NbAiLab/roberta_jan_512_ncc
|
NbAiLab
| 2022-03-04T11:44:03Z | 60 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
license: cc-by-sa-4.0
---
|
jkhan447/sentiment-model-sample
|
jkhan447
| 2022-03-04T11:13:39Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: sentiment-model-sample
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93948
---
<!-- 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. -->
# sentiment-model-sample
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5280
- Accuracy: 0.9395
## 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: 10
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu
|
kabelomalapane
| 2022-03-04T08:53:37Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-03T17:46:12Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5907
- Validation Loss: 1.6321
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
This model is to be used to translate English into Zulu. But there are still some problems in running this model, so it's still to be modified.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 783, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.1622 | 1.7379 | 0 |
| 1.7292 | 1.6529 | 1 |
| 1.5907 | 1.6321 | 2 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Yulinfeng/wsj0_2mix_enh_train_enh_mdc_raw_valid.si_snr.ave
|
Yulinfeng
| 2022-03-04T07:19:47Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
audio-to-audio
| 2022-03-04T07:19:31Z |
---
tags:
- espnet
- audio
- audio-to-audio
language: en
datasets:
- wsj0_2mix
license: cc-by-4.0
---
## ESPnet2 ENH model
### `Yulinfeng/wsj0_2mix_enh_train_enh_mdc_raw_valid.si_snr.ave`
This model was trained by earthmanylf using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout ec1acec03d109f06d829b80862e0388f7234d0d1
pip install -e .
cd egs2/wsj0_2mix/enh1
./run.sh --skip_data_prep false --skip_train true --download_model Yulinfeng/wsj0_2mix_enh_train_enh_mdc_raw_valid.si_snr.ave
```
<!-- Generated by ./scripts/utils/show_enh_score.sh -->
# RESULTS
## Environments
- date: `Thu Mar 3 17:10:03 CST 2022`
- python version: `3.8.10 (default, May 19 2021, 18:05:58) [GCC 7.3.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.5.1+cu101`
- Git hash: `ec1acec03d109f06d829b80862e0388f7234d0d1`
- Commit date: `Fri Feb 25 14:12:45 2022 +0800`
## ..
config: conf/tuning/train_enh_mdc.yaml
|dataset|PESQ|STOI|SAR|SDR|SIR|SI_SNR|
|---|---|---|---|---|---|---|
|enhanced_cv_min_8k|2.20|0.84|9.62|8.57|17.27|8.03|
|enhanced_tt_min_8k|2.18|0.85|9.56|8.50|17.28|7.97|
## ENH config
<details><summary>expand</summary>
```
config: conf/tuning/train_enh_mdc.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/enh_train_enh_mdc_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 10
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- si_snr
- max
- - valid
- loss
- min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 8
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_8k/train/speech_mix_shape
- exp/enh_stats_8k/train/speech_ref1_shape
- exp/enh_stats_8k/train/speech_ref2_shape
valid_shape_file:
- exp/enh_stats_8k/valid/speech_mix_shape
- exp/enh_stats_8k/valid/speech_ref1_shape
- exp/enh_stats_8k/valid/speech_ref2_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_min_8k/wav.scp
- speech_mix
- sound
- - dump/raw/tr_min_8k/spk1.scp
- speech_ref1
- sound
- - dump/raw/tr_min_8k/spk2.scp
- speech_ref2
- sound
valid_data_path_and_name_and_type:
- - dump/raw/cv_min_8k/wav.scp
- speech_mix
- sound
- - dump/raw/cv_min_8k/spk1.scp
- speech_ref1
- sound
- - dump/raw/cv_min_8k/spk2.scp
- speech_ref2
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-08
weight_decay: 1.0e-07
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.7
patience: 1
init: xavier_uniform
model_conf:
stft_consistency: false
loss_type: mask_mse
mask_type: PSM
ref_channel: 0
criterions:
- name: dpcl
conf:
loss_type: mdc
wrapper: dpcl
wrapper_conf:
weight: 1.0
use_preprocessor: false
encoder: stft
encoder_conf:
n_fft: 256
hop_length: 128
separator: dpcl
separator_conf:
rnn_type: blstm
num_spk: 2
nonlinear: relu
layer: 2
unit: 500
dropout: 0.1
emb_D: 40
decoder: stft
decoder_conf:
n_fft: 256
hop_length: 128
required:
- output_dir
version: 0.10.7a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{ESPnet-SE,
author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and
Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe},
title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021},
pages = {785--792},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SLT48900.2021.9383615},
doi = {10.1109/SLT48900.2021.9383615},
timestamp = {Mon, 12 Apr 2021 17:08:59 +0200},
biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Yulinfeng/wsj0_2mix_enh_train_enh_dan_tf_raw_valid.si_snr.ave
|
Yulinfeng
| 2022-03-04T07:17:15Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
audio-to-audio
| 2022-03-04T07:16:14Z |
---
tags:
- espnet
- audio
- audio-to-audio
language: en
datasets:
- wsj0_2mix
license: cc-by-4.0
---
## ESPnet2 ENH model
### `Yulinfeng/wsj0_2mix_enh_train_enh_dan_tf_raw_valid.si_snr.ave`
This model was trained by earthmanylf using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout ec1acec03d109f06d829b80862e0388f7234d0d1
pip install -e .
cd egs2/wsj0_2mix/enh1
./run.sh --skip_data_prep false --skip_train true --download_model Yulinfeng/wsj0_2mix_enh_train_enh_dan_tf_raw_valid.si_snr.ave
```
<!-- Generated by ./scripts/utils/show_enh_score.sh -->
# RESULTS
## Environments
- date: `Thu Mar 3 14:33:32 CST 2022`
- python version: `3.8.10 (default, May 19 2021, 18:05:58) [GCC 7.3.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.5.1+cu101`
- Git hash: `ec1acec03d109f06d829b80862e0388f7234d0d1`
- Commit date: `Fri Feb 25 14:12:45 2022 +0800`
## ..
config: conf/tuning/train_enh_dan_tf.yaml
|dataset|PESQ|STOI|SAR|SDR|SIR|SI_SNR|
|---|---|---|---|---|---|---|
|enhanced_cv_min_8k|2.68|0.88|12.28|11.01|18.03|10.48|
|enhanced_tt_min_8k|2.68|0.89|12.10|10.84|17.98|10.30|
## ENH config
<details><summary>expand</summary>
```
config: conf/tuning/train_enh_dan_tf.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_dan_tf_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 10
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- si_snr
- max
- - valid
- loss
- min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 8
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_8k/train/speech_mix_shape
- exp/enh_stats_8k/train/speech_ref1_shape
- exp/enh_stats_8k/train/speech_ref2_shape
valid_shape_file:
- exp/enh_stats_8k/valid/speech_mix_shape
- exp/enh_stats_8k/valid/speech_ref1_shape
- exp/enh_stats_8k/valid/speech_ref2_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 32000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_min_8k/wav.scp
- speech_mix
- sound
- - dump/raw/tr_min_8k/spk1.scp
- speech_ref1
- sound
- - dump/raw/tr_min_8k/spk2.scp
- speech_ref2
- sound
valid_data_path_and_name_and_type:
- - dump/raw/cv_min_8k/wav.scp
- speech_mix
- sound
- - dump/raw/cv_min_8k/spk1.scp
- speech_ref1
- sound
- - dump/raw/cv_min_8k/spk2.scp
- speech_ref2
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0001
eps: 1.0e-08
weight_decay: 1.0e-07
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.7
patience: 1
init: xavier_uniform
model_conf:
stft_consistency: false
loss_type: mask_mse
mask_type: PSM
ref_channel: 0
criterions:
- name: mse
conf:
compute_on_mask: false
mask_type: PSM
wrapper: pit
wrapper_conf:
weight: 1.0
use_preprocessor: false
encoder: stft
encoder_conf:
n_fft: 256
hop_length: 64
separator: dan
separator_conf:
rnn_type: blstm
num_spk: 2
nonlinear: tanh
layer: 4
unit: 600
dropout: 0.1
emb_D: 20
decoder: stft
decoder_conf:
n_fft: 256
hop_length: 64
required:
- output_dir
version: 0.10.7a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{ESPnet-SE,
author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and
Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe},
title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021},
pages = {785--792},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SLT48900.2021.9383615},
doi = {10.1109/SLT48900.2021.9383615},
timestamp = {Mon, 12 Apr 2021 17:08:59 +0200},
biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Ayham/ernie_roberta_summarization_cnn_dailymail
|
Ayham
| 2022-03-04T01:47:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-03T18:05:21Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: ernie_roberta_summarization_cnn_dailymail
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. -->
# ernie_roberta_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
abdelhalim/Shower_Sound_Recognition
|
abdelhalim
| 2022-03-03T22:09:48Z | 20 | 3 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"audio",
"audio-classificaiton",
"shower detection",
"dataset:SHD-2",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:05Z |
---
datasets:
- SHD-2
tags:
- audio
- audio-classificaiton
- shower detection
metrics:
- Accuracy
---
**Context**
Most of our great brilliant ideas happen in periods of relaxation, like taking a
shower, however, once we leave the shower, we forget the brilliant idea. What if
we do not forget, and collect your ideas in the shower?
**What is the Shower Ideas concept?**
This is an app that detects when someone is taking a shower (douche) and asks
“do you have any idea?”, and the person will speak while taking the shower telling
the idea. And also will ask questions after taking a shower.
**Abstract about the model**
This model was trained based on *facebook/wav2vec2-base-960h* (which is a pretrained model on 960 hours of Librispeech on 16kHz sampled speech audio.) in order to classify the audio input into shower or no_shower.
**Dataset**
The SHD-2 dataset is a labeled collection of 2260 audio recordings of shower and no shower sounds.
The dataset consists of 6-second-long recordings organized into 2 classes (with 1130 examples per class).
# Usage
In order to use the model in your Python script just copy the following code:
```python
from transformers import pipeline
audio_input = 'example.wav'
classifier = pipeline("audio-classification", model="abdelhalim/Shower_Sound_Recognition")
labels = classifier(audio_input)
labels
```
|
batterydata/batteryscibert-cased-squad-v1
|
batterydata
| 2022-03-03T20:29:14Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BatterySciBERT-cased for QA
**Language model:** batteryscibert-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 3
base_LM_model = "batteryscibert-cased"
max_seq_len = 386
learning_rate = 2e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 79.66,
"f1": 87.43,
```
Evaluated on the battery device dataset.
```
"precision": 65.09,
"recall": 84.56,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/batteryscibert-cased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batteryonlybert-uncased-squad-v1
|
batterydata
| 2022-03-03T20:25:01Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BatteryOnlyBERT-uncased for QA
**Language model:** batteryonlybert-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 16
n_epochs = 2
base_LM_model = "batteryonlybert-uncased"
max_seq_len = 386
learning_rate = 2e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 79.53,
"f1": 87.22,
```
Evaluated on the battery device dataset.
```
"precision": 67.20,
"recall": 83.82,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/batteryonlybert-uncased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
repro-rights-amicus-briefs/legal-bert-base-uncased-finetuned-RRamicus
|
repro-rights-amicus-briefs
| 2022-03-03T20:21:45Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: legal-bert-base-uncased-finetuned-RRamicus
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. -->
# legal-bert-base-uncased-finetuned-RRamicus
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1520
## 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: 928
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.021 | 1.0 | 1118 | 1.3393 |
| 1.2272 | 2.0 | 2236 | 1.2612 |
| 1.2467 | 3.0 | 3354 | 1.2403 |
| 1.2149 | 4.0 | 4472 | 1.2276 |
| 1.1855 | 5.0 | 5590 | 1.2101 |
| 1.1674 | 6.0 | 6708 | 1.2020 |
| 1.1508 | 7.0 | 7826 | 1.1893 |
| 1.1386 | 8.0 | 8944 | 1.1870 |
| 1.129 | 9.0 | 10062 | 1.1794 |
| 1.1193 | 10.0 | 11180 | 1.1759 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
mcdzwil/bert-base-NER-finetuned-ner-ISU
|
mcdzwil
| 2022-03-03T20:21:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-03T20:12:34Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-NER-finetuned-ner-ISU
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-NER-finetuned-ner-ISU
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1090
- Precision: 0.9408
- Recall: 0.8223
- F1: 0.8776
- Accuracy: 0.9644
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 48 | 0.1411 | 0.8970 | 0.7840 | 0.8367 | 0.9473 |
| No log | 2.0 | 96 | 0.1231 | 0.9453 | 0.7964 | 0.8645 | 0.9589 |
| No log | 3.0 | 144 | 0.1090 | 0.9408 | 0.8223 | 0.8776 | 0.9644 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
batterydata/bert-base-cased-squad-v1
|
batterydata
| 2022-03-03T19:54:26Z | 71 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BERT-base-cased for QA
**Language model:** bert-base-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 2
base_LM_model = "bert-base-cased"
max_seq_len = 386
learning_rate = 5e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 81.30,
"f1": 88.58,
```
Evaluated on the battery device dataset.
```
"precision": 67.02,
"recall": 80.15,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/bert-base-cased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
kaixinwang/NLP
|
kaixinwang
| 2022-03-03T19:06:29Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"sentiment analysis",
"STEM",
"text classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- "Python"
thumbnail: "url to a thumbnail used in social sharing"
tags:
- "sentiment analysis"
- "STEM"
- "text classification"
---
Welcome! This is the model built for the sentiment analysis on the STEM course reviews at UCLA.
- Author: Kaixin Wang
- Email: kaixinwang@g.ucla.edu
- Time Updated: March 2022
|
Kevincp560/t5-small-finetuned-pubmed
|
Kevincp560
| 2022-03-03T17:22:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-03T16:24:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: t5-small-finetuned-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 8.8295
---
<!-- 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. -->
# t5-small-finetuned-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2635
- Rouge1: 8.8295
- Rouge2: 3.2594
- Rougel: 7.9975
- Rougelsum: 8.4483
- Gen Len: 19.0
## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 2.5892 | 1.0 | 4000 | 2.3616 | 10.1169 | 3.9666 | 8.8854 | 9.5836 | 19.0 |
| 2.559 | 2.0 | 8000 | 2.3045 | 9.4321 | 3.5398 | 8.424 | 8.984 | 19.0 |
| 2.5029 | 3.0 | 12000 | 2.2820 | 9.1658 | 3.3686 | 8.2222 | 8.7311 | 19.0 |
| 2.4673 | 4.0 | 16000 | 2.2692 | 8.8973 | 3.2617 | 8.0395 | 8.5046 | 19.0 |
| 2.4331 | 5.0 | 20000 | 2.2635 | 8.8295 | 3.2594 | 7.9975 | 8.4483 | 19.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
nateraw/keras-dummy-model-mixin-demo-w-card
|
nateraw
| 2022-03-03T15:55:09Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
nateraw/autoencoder-keras-mnist-demo-with-card-2
|
nateraw
| 2022-03-03T15:53:24Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-03-03T15:53:14Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
nateraw/keras-dummy-sequential-demo-with-card-2
|
nateraw
| 2022-03-03T15:51:04Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-03-03T15:50:54Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
sanchit-gandhi/wav2vec2-2-rnd-grid-search
|
sanchit-gandhi
| 2022-03-03T14:51:05Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 6.9475
- Wer: 2.0097
## 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: 4
- 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: 1000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9006 | 1.68 | 1500 | 6.9507 | 2.0097 |
| 6.9503 | 3.36 | 3000 | 6.9475 | 2.0097 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
amtam0/timer-ner-fr
|
amtam0
| 2022-03-03T14:12:18Z | 10 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fr",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: fr
widget:
- text: 'génère 27 séries de 54 seconde '
- text: ' 9 cycles de 17 minute '
- text: 'initie 17 sets de 44 secondes 297 minutes entre séries'
- text: ' 13 sets de 88 secondes 225 minutes 49 entre chaque série'
- text: 'génère 39 séries de 19 minute 21 minute 45 entre séries'
- text: 'débute 47 sets de 6 heures '
- text: 'débute 1 cycle de 25 minutes 48 23 minute 32 entre chaque série'
- text: 'commence 23 séries de 18 heure et demi 25 minutes 41 entre séries'
- text: ' 13 cycles de 52 secondes '
- text: 'crée 31 série de 60 secondes '
- text: ' 7 set de 36 secondes 139 minutes 34 entre séries'
- text: 'commence 37 sets de 51 minute 25 295 minute entre chaque série'
- text: 'crée 11 cycles de 72 seconde 169 minute 15 entre chaque série'
- text: 'initie 5 série de 33 minutes 48 '
- text: 'crée 23 set de 1 minute 46 279 minutes 50 entre chaque série'
- text: 'génère 41 série de 35 minutes 55 '
- text: 'lance 11 cycles de 4 heures '
- text: 'crée 47 cycle de 28 heure moins quart 243 minutes 45 entre chaque série'
- text: 'initie 23 set de 36 secondes '
- text: 'commence 37 sets de 24 heures et quart '
---
#### This model is used in the [Speech Interval Timer app](https://medium.com/@amtam0/speech-interval-timer-app-using-transformers-1df8fa3821d5)
7-class NER French model using [Flair TransformerWordEmbeddings - camembert-base](https://github.com/flairNLP/flair/).
| **tag** | **meaning** |
|---------------------------------|-----------|
| nb_rounds | Number of rounds |
| duration_br_sd | Duration btwn rounds in seconds |
| duration_br_min | Duration btwn rounds in minutes |
| duration_br_hr | Duration btwn rounds in hours |
| duration_wt_sd | workout duration in seconds |
| duration_wt_min | workout duration in minutes |
| duration_wt_hr | workout duration in hours |
---
Synthetic dataset has been used (perfectible). Sentences example in the widget.
|
Kuray107/wsj0-full-supervised
|
Kuray107
| 2022-03-03T11:16:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wsj0-full-supervised
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. -->
# wsj0-full-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0623
- Wer: 0.0343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 12
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.517 | 0.86 | 500 | 2.9475 | 1.0 |
| 2.2387 | 1.72 | 1000 | 0.4004 | 0.3498 |
| 0.3081 | 2.57 | 1500 | 0.1362 | 0.1159 |
| 0.1744 | 3.43 | 2000 | 0.1125 | 0.0929 |
| 0.1285 | 4.29 | 2500 | 0.0894 | 0.0727 |
| 0.1015 | 5.15 | 3000 | 0.0852 | 0.0642 |
| 0.0811 | 6.0 | 3500 | 0.0789 | 0.0614 |
| 0.0748 | 6.86 | 4000 | 0.0746 | 0.0529 |
| 0.0639 | 7.72 | 4500 | 0.0714 | 0.0481 |
| 0.0606 | 8.58 | 5000 | 0.0698 | 0.0489 |
| 0.0525 | 9.43 | 5500 | 0.0747 | 0.0464 |
| 0.0489 | 10.29 | 6000 | 0.0594 | 0.0396 |
| 0.0419 | 11.15 | 6500 | 0.0600 | 0.0359 |
| 0.0414 | 12.01 | 7000 | 0.0612 | 0.0412 |
| 0.0383 | 12.86 | 7500 | 0.0676 | 0.0392 |
| 0.0352 | 13.72 | 8000 | 0.0626 | 0.0388 |
| 0.034 | 14.58 | 8500 | 0.0699 | 0.0372 |
| 0.0309 | 15.44 | 9000 | 0.0807 | 0.0420 |
| 0.0295 | 16.3 | 9500 | 0.0796 | 0.0396 |
| 0.0273 | 17.15 | 10000 | 0.0716 | 0.0376 |
| 0.0271 | 18.01 | 10500 | 0.0657 | 0.0384 |
| 0.0251 | 18.87 | 11000 | 0.0585 | 0.0351 |
| 0.024 | 19.73 | 11500 | 0.0557 | 0.0347 |
| 0.0252 | 20.58 | 12000 | 0.0609 | 0.0327 |
| 0.0231 | 21.44 | 12500 | 0.0720 | 0.0368 |
| 0.0202 | 22.3 | 13000 | 0.0625 | 0.0343 |
| 0.0195 | 23.16 | 13500 | 0.0635 | 0.0372 |
| 0.0201 | 24.01 | 14000 | 0.0582 | 0.0335 |
| 0.0183 | 24.87 | 14500 | 0.0562 | 0.0343 |
| 0.0183 | 25.73 | 15000 | 0.0629 | 0.0335 |
| 0.0175 | 26.59 | 15500 | 0.0593 | 0.0323 |
| 0.017 | 27.44 | 16000 | 0.0631 | 0.0339 |
| 0.0162 | 28.3 | 16500 | 0.0597 | 0.0335 |
| 0.0169 | 29.16 | 17000 | 0.0623 | 0.0343 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
cammy/bart-large-cnn-finetuned-new-100-pad-early
|
cammy
| 2022-03-03T10:23:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-03T10:22:53Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-new-100-pad-early
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. -->
# bart-large-cnn-finetuned-new-100-pad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9543
- Rouge1: 21.8858
- Rouge2: 8.1444
- Rougel: 16.5751
- Rougelsum: 19.163
- Gen Len: 66.8
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 100 | 0.8692 | 20.2714 | 6.206 | 16.3362 | 18.7117 | 66.4 |
| No log | 2.0 | 200 | 0.9543 | 21.8858 | 8.1444 | 16.5751 | 19.163 | 66.8 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
carolEileen/distilbert-base-uncased-finetuned-imdb
|
carolEileen
| 2022-03-03T09:07:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-03T08:55:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4725
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5756 | 2.0 | 314 | 2.4230 |
| 2.5395 | 3.0 | 471 | 2.4358 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Akash7897/distilbert-base-uncased-finetuned-sst2
|
Akash7897
| 2022-03-03T08:57:39Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9036697247706422
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3010
- Accuracy: 0.9037
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1793 | 1.0 | 4210 | 0.3010 | 0.9037 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
cammy/bart-large-cnn-finetuned-weaksup-100-pad-early
|
cammy
| 2022-03-03T06:29:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-03T06:28:42Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-weaksup-100-pad-early
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. -->
# bart-large-cnn-finetuned-weaksup-100-pad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0714
- Rouge1: 26.6767
- Rouge2: 8.6321
- Rougel: 17.4235
- Rougelsum: 21.6089
- Gen Len: 66.1
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 100 | 1.0405 | 26.8313 | 10.4295 | 19.1329 | 23.8101 | 64.6 |
| No log | 2.0 | 200 | 1.0714 | 26.6767 | 8.6321 | 17.4235 | 21.6089 | 66.1 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
shahp7575/electricidad-base-muchocine-finetuned
|
shahp7575
| 2022-03-03T05:20:16Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"spanish",
"sentiment",
"es",
"dataset:muchocine",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-03T03:46:13Z |
---
language:
- es
tags:
- spanish
- sentiment
datasets:
- muchocine
widget:
- "Increíble pelicula. ¡Altamente recomendado!"
- "Extremadamente malo. Baja calidad"
---
<!-- 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. -->
# electricidad-base-muchocine-finetuned
This model fine-tunes [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on [muchocine](https://huggingface.co/datasets/muchocine) dataset for sentiment classification to predict *star_rating*.
### How to use
The model can be used directly with the HuggingFace `pipeline`.
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("shahp7575/gpt2-horoscopes")
model = AutoModelWithLMHead.from_pretrained("shahp7575/gpt2-horoscopes")
```
### Examples
```python
from transformers import pipeline
clf = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
clf('Esta película es una joya. Todo fue perfecto: historia, casting, dirección. Me encantó el clímax.')
>>> [{'label': '5', 'score': 0.9658033847808838}]
clf("La historia y el casting fueron geniales.")
>>> [{'label': '4', 'score': 0.6666394472122192}]
clf("Me gustó pero podría ser mejor.")
>>> [{'label': '3', 'score': 0.7013391852378845}]
clf("dinero tirado en esta pelicula")
>>> [{'label': '2', 'score': 0.7564149498939514}]
clf("esta película es una película absolutamente repugnante. odio todo al respecto. gastó tanto dinero.")
>>> [{'label': '1', 'score': 0.3040296733379364}]
```
|
Kamuuung/autonlp-lessons_tagging-606217261
|
Kamuuung
| 2022-03-03T04:25:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:Kamuuung/autonlp-data-lessons_tagging",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-03T04:19:25Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Kamuuung/autonlp-data-lessons_tagging
co2_eq_emissions: 7.968891750522204
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 606217261
- CO2 Emissions (in grams): 7.968891750522204
## Validation Metrics
- Loss: 0.989620566368103
- Accuracy: 0.6777163904235728
- Macro F1: 0.6817448899563519
- Micro F1: 0.6777163904235728
- Weighted F1: 0.6590820060806175
- Macro Precision: 0.7028251935864661
- Micro Precision: 0.6777163904235728
- Weighted Precision: 0.6764567648776801
- Macro Recall: 0.6861061576846053
- Micro Recall: 0.6777163904235728
- Weighted Recall: 0.6777163904235728
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Kamuuung/autonlp-lessons_tagging-606217261
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Kamuuung/autonlp-lessons_tagging-606217261", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Kamuuung/autonlp-lessons_tagging-606217261", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
algolet/mt5-base-chinese-qg
|
algolet
| 2022-03-03T02:18:05Z | 45 | 17 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
<h3 align="center">
<p>MT5 Base Model for Chinese Question Generation</p>
</h3>
<h3 align="center">
<p>基于mt5的中文问题生成任务</p>
</h3>
#### 可以通过安装question-generation包开始用
```
pip install question-generation
```
使用方法请参考github项目：https://github.com/algolet/question_generation
#### 在线使用
可以直接在线使用我们的模型：https://www.algolet.com/applications/qg
#### 通过transformers调用
``` python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("algolet/mt5-base-chinese-qg")
model = AutoModelForSeq2SeqLM.from_pretrained("algolet/mt5-base-chinese-qg")
model.eval()
text = "在一个寒冷的冬天，赶集完回家的农夫在路边发现了一条冻僵了的蛇。他很可怜蛇，就把它放在怀里。当他身上的热气把蛇温暖以后，蛇很快苏醒了，露出了残忍的本性，给了农夫致命的伤害——咬了农夫一口。农夫临死之前说：“我竟然救了一条可怜的毒蛇，就应该受到这种报应啊！”"
text = "question generation: " + text
inputs = tokenizer(text,
return_tensors='pt',
truncation=True,
max_length=512)
with torch.no_grad():
outs = model.generate(input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_length=128,
no_repeat_ngram_size=4,
num_beams=4)
question = tokenizer.decode(outs[0], skip_special_tokens=True)
questions = [q.strip() for q in question.split("<sep>") if len(q.strip()) > 0]
print(questions)
['在寒冷的冬天,农夫在哪里发现了一条可怜的蛇?', '农夫是如何看待蛇的?', '当农夫遇到蛇时,他做了什么?']
```
#### 指标
rouge-1: 0.4041
rouge-2: 0.2104
rouge-l: 0.3843
---
language:
- zh
tags:
- mt5
- question generation
metrics:
- rouge
---
|
yoavgur/gpt2-bash-history-baseline
|
yoavgur
| 2022-03-02T23:02:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-bash-history-baseline
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-bash-history-baseline
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: 2.0349
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 158 | 2.1038 |
| No log | 2.0 | 316 | 2.0349 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
pjheslin/distilbert-base-uncased-finetuned-emotion
|
pjheslin
| 2022-03-02T22:49:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.9254862165828515
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2227
- Accuracy: 0.9255
- F1: 0.9255
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8417 | 1.0 | 250 | 0.3260 | 0.9045 | 0.9006 |
| 0.2569 | 2.0 | 500 | 0.2227 | 0.9255 | 0.9255 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
edugp/kenlm
|
edugp
| 2022-03-02T22:44:44Z | 0 | 51 | null |
[
"kenlm",
"perplexity",
"n-gram",
"kneser-ney",
"bigscience",
"es",
"af",
"ar",
"arz",
"as",
"bn",
"fr",
"sw",
"eu",
"ca",
"zh",
"en",
"hi",
"ur",
"id",
"pt",
"vi",
"gu",
"kn",
"ml",
"mr",
"ta",
"te",
"yo",
"dataset:wikipedia",
"dataset:oscar",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- es
- af
- ar
- arz
- as
- bn
- fr
- sw
- eu
- ca
- zh
- en
- hi
- ur
- id
- pt
- vi
- gu
- kn
- ml
- mr
- ta
- te
- yo
tags:
- kenlm
- perplexity
- n-gram
- kneser-ney
- bigscience
license: "mit"
datasets:
- wikipedia
- oscar
---
# KenLM models
This repo contains several KenLM models trained on different tokenized datasets and languages.
KenLM models are probabilistic n-gram languge models that models. One use case of these models consist on fast perplexity estimation for [filtering or sampling large datasets](https://huggingface.co/bertin-project/bertin-roberta-base-spanish). For example, one could use a KenLM model trained on French Wikipedia to run inference on a large dataset and filter out samples that are very unlike to appear on Wikipedia (high perplexity), or very simple non-informative sentences that could appear repeatedly (low perplexity).
At the root of this repo you will find different directories named after the dataset models were trained on (e.g. `wikipedia`, `oscar`). Within each directory, you will find several models trained on different language subsets of the dataset (e.g. `en (English)`, `es (Spanish)`, `fr (French)`). For each language you will find three different files
* `{language}.arpa.bin`: The trained KenLM model binary
* `{language}.sp.model`: The trained SentencePiece model used for tokenization
* `{language}.sp.vocab`: The vocabulary file for the SentencePiece model
The models have been trained using some of the preprocessing steps from [cc_net](https://github.com/facebookresearch/cc_net), in particular replacing numbers with zeros and normalizing punctuation. So, it is important to keep the default values for the parameters: `lower_case`, `remove_accents`, `normalize_numbers` and `punctuation` when using the pre-trained models in order to replicate the same pre-processing steps at inference time.
# Dependencies
* KenLM: `pip install https://github.com/kpu/kenlm/archive/master.zip`
* SentencePiece: `pip install sentencepiece`
# Example:
```
from model import KenlmModel
# Load model trained on English wikipedia
model = KenlmModel.from_pretrained("wikipedia", "en")
# Get perplexity
model.get_perplexity("I am very perplexed")
# 341.3 (low perplexity, since sentence style is formal and with no grammar mistakes)
model.get_perplexity("im hella trippin")
# 46793.5 (high perplexity, since the sentence is colloquial and contains grammar mistakes)
```
In the example above we see that, since Wikipedia is a collection of encyclopedic articles, a KenLM model trained on it will naturally give lower perplexity scores to sentences with formal language and no grammar mistakes than colloquial sentences with grammar mistakes.
|
Ayham/ernie_gpt2_summarization_cnn_dailymail
|
Ayham
| 2022-03-02T21:43:45Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: ernie_gpt2_summarization_cnn_dailymail
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. -->
# ernie_gpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hcy11/distilbert-base-uncased-finetuned-squad
|
hcy11
| 2022-03-02T20:32:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2131
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2672 | 1.0 | 5533 | 1.2131 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
facebook/convnext-large-384-22k-1k
|
facebook
| 2022-03-02T19:03:42Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-large-384-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-384-22k-1k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
datnth1709/Phobert-classifier
|
datnth1709
| 2022-03-02T18:29:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2003.00744",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance.
- PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.
The general architecture and experimental results of PhoBERT can be found in our EMNLP-2020 Findings [paper](https://arxiv.org/abs/2003.00744):
@article{phobert,
title = {{PhoBERT: Pre-trained language models for Vietnamese}},
author = {Dat Quoc Nguyen and Anh Tuan Nguyen},
journal = {Findings of EMNLP},
year = {2020}
}
**Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software.
For further information or requests, please go to [PhoBERT's homepage](https://github.com/VinAIResearch/PhoBERT)!
### Installation <a name="install2"></a>
- Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
- Install `transformers`:
- `git clone https://github.com/huggingface/transformers.git`
- `cd transformers`
- `pip3 install --upgrade .`
### Pre-trained models <a name="models2"></a>
Model | #params | Arch. | Pre-training data
---|---|---|---
`vinai/phobert-base` | 135M | base | 20GB of texts
`vinai/phobert-large` | 370M | large | 20GB of texts
### Example usage <a name="usage2"></a>
```python
import torch
from transformers import AutoModel, AutoTokenizer
phobert = AutoModel.from_pretrained("vinai/phobert-base")
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
input_ids = torch.tensor([tokenizer.encode(line)])
with torch.no_grad():
features = phobert(input_ids) # Models outputs are now tuples
## With TensorFlow 2.0+:
# from transformers import TFAutoModel
# phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
```
|
mcdzwil/bert-base-NER-finetuned-ner
|
mcdzwil
| 2022-03-02T16:53:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-NER-finetuned-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. -->
# bert-base-NER-finetuned-ner
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1670
- Precision: 0.8358
- Recall: 0.7615
- F1: 0.7969
- Accuracy: 0.9437
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 48 | 0.1892 | 0.8240 | 0.7267 | 0.7723 | 0.9341 |
| No log | 2.0 | 96 | 0.1812 | 0.8667 | 0.7458 | 0.8017 | 0.9441 |
| No log | 3.0 | 144 | 0.1670 | 0.8358 | 0.7615 | 0.7969 | 0.9437 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
mcdzwil/distilbert-base-uncased-finetuned-ner
|
mcdzwil
| 2022-03-02T16:35:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1830
- Precision: 0.9171
- Recall: 0.7099
- F1: 0.8003
- Accuracy: 0.9316
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 48 | 0.2903 | 0.7952 | 0.7063 | 0.7481 | 0.9136 |
| No log | 2.0 | 96 | 0.2015 | 0.9154 | 0.7075 | 0.7981 | 0.9298 |
| No log | 3.0 | 144 | 0.1830 | 0.9171 | 0.7099 | 0.8003 | 0.9316 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
jiobiala24/wav2vec2-base-checkpoint-14
|
jiobiala24
| 2022-03-02T15:13:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-checkpoint-14
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-checkpoint-14
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-13](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-13) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2822
- Wer: 0.4068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1996 | 1.59 | 1000 | 0.7181 | 0.4079 |
| 0.1543 | 3.17 | 2000 | 0.7735 | 0.4113 |
| 0.1171 | 4.76 | 3000 | 0.8152 | 0.4045 |
| 0.0969 | 6.35 | 4000 | 0.8575 | 0.4142 |
| 0.082 | 7.94 | 5000 | 0.9005 | 0.4124 |
| 0.074 | 9.52 | 6000 | 0.9232 | 0.4151 |
| 0.0653 | 11.11 | 7000 | 0.9680 | 0.4223 |
| 0.0587 | 12.7 | 8000 | 1.0633 | 0.4232 |
| 0.0551 | 14.29 | 9000 | 1.0875 | 0.4171 |
| 0.0498 | 15.87 | 10000 | 1.0281 | 0.4105 |
| 0.0443 | 17.46 | 11000 | 1.2164 | 0.4274 |
| 0.0421 | 19.05 | 12000 | 1.1868 | 0.4191 |
| 0.0366 | 20.63 | 13000 | 1.1678 | 0.4173 |
| 0.0366 | 22.22 | 14000 | 1.2444 | 0.4187 |
| 0.0346 | 23.81 | 15000 | 1.2042 | 0.4169 |
| 0.0316 | 25.4 | 16000 | 1.3019 | 0.4127 |
| 0.0296 | 26.98 | 17000 | 1.2001 | 0.4081 |
| 0.0281 | 28.57 | 18000 | 1.2822 | 0.4068 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
jcai1/sentence_similarity_concierge
|
jcai1
| 2022-03-02T15:04:54Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sentence_similarity_concierge
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. -->
# sentence_similarity_concierge
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1165
- Accuracy: 0.9748
- F1: 0.9680
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 402 | 0.2334 | 0.9412 | 0.9263 |
| 0.2834 | 2.0 | 804 | 0.1656 | 0.9608 | 0.9493 |
| 0.1073 | 3.0 | 1206 | 0.1165 | 0.9748 | 0.9680 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
swcrazyfan/KingJamesify-T5-large
|
swcrazyfan
| 2022-03-02T10:53:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
This model was fine-tuned to “translate” any English text into 17th-century style English.
The name comes from the dataset used for fine-tuning. Namely, modern Bible text as input and and the famous King James Bible as the output.
To test, use “kingify: “ at the beginning of anything you want to translate.
Generally, it does a good job and phrases, concepts, and vocabulary that may appear in the Bible. If not, the will likely just modify the grammar and other words while leaving the word with an unknown 17th-century equivalent.
|
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