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null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-electra-small-ex-discriminator
| null |
[
"transformers",
"pytorch",
"tf",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
hfl/chinese-electra-small-ex-generator
| null |
[
"transformers",
"pytorch",
"tf",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
hfl/chinese-electra-small-generator
| null |
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-legal-electra-base-discriminator
| null |
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-legal-electra-base-generator
| null |
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-legal-electra-large-discriminator
| null |
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-legal-electra-large-generator
| null |
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-legal-electra-small-discriminator
| null |
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-legal-electra-small-generator
| null |
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
<p align="center">
<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&style=flat-square">
</a>
</p>
# Please use 'Bert' related functions to load this model!
This repository contains the resources in our paper **"Revisiting Pre-trained Models for Chinese Natural Language Processing"**, which will be published in "[Findings of EMNLP](https://2020.emnlp.org)". You can read our camera-ready paper through [ACL Anthology](#) or [arXiv pre-print](https://arxiv.org/abs/2004.13922).
**[Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)**
*Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu*
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Introduction
**MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.
Instead of masking with [MASK] token, which never appears in the fine-tuning stage, **we propose to use similar words for the masking purpose**. A similar word is obtained by using [Synonyms toolkit (Wang and Hu, 2017)](https://github.com/chatopera/Synonyms), which is based on word2vec (Mikolov et al., 2013) similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement.
Here is an example of our pre-training task.
| | Example |
| -------------- | ----------------- |
| **Original Sentence** | we use a language model to predict the probability of the next word. |
| **MLM** | we use a language [M] to [M] ##di ##ct the pro [M] ##bility of the next word . |
| **Whole word masking** | we use a language [M] to [M] [M] [M] the [M] [M] [M] of the next word . |
| **N-gram masking** | we use a [M] [M] to [M] [M] [M] the [M] [M] [M] [M] [M] next word . |
| **MLM as correction** | we use a text system to ca ##lc ##ulate the po ##si ##bility of the next word . |
Except for the new pre-training task, we also incorporate the following techniques.
- Whole Word Masking (WWM)
- N-gram masking
- Sentence-Order Prediction (SOP)
**Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.**
For more technical details, please check our paper: [Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
hfl/chinese-macbert-base
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
<p align="center">
<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&style=flat-square">
</a>
</p>
# Please use 'Bert' related functions to load this model!
This repository contains the resources in our paper **"Revisiting Pre-trained Models for Chinese Natural Language Processing"**, which will be published in "[Findings of EMNLP](https://2020.emnlp.org)". You can read our camera-ready paper through [ACL Anthology](#) or [arXiv pre-print](https://arxiv.org/abs/2004.13922).
**[Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)**
*Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu*
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Introduction
**MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.
Instead of masking with [MASK] token, which never appears in the fine-tuning stage, **we propose to use similar words for the masking purpose**. A similar word is obtained by using [Synonyms toolkit (Wang and Hu, 2017)](https://github.com/chatopera/Synonyms), which is based on word2vec (Mikolov et al., 2013) similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement.
Here is an example of our pre-training task.
| | Example |
| -------------- | ----------------- |
| **Original Sentence** | we use a language model to predict the probability of the next word. |
| **MLM** | we use a language [M] to [M] ##di ##ct the pro [M] ##bility of the next word . |
| **Whole word masking** | we use a language [M] to [M] [M] [M] the [M] [M] [M] of the next word . |
| **N-gram masking** | we use a [M] [M] to [M] [M] [M] the [M] [M] [M] [M] [M] next word . |
| **MLM as correction** | we use a text system to ca ##lc ##ulate the po ##si ##bility of the next word . |
Except for the new pre-training task, we also incorporate the following techniques.
- Whole Word Masking (WWM)
- N-gram masking
- Sentence-Order Prediction (SOP)
**Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.**
For more technical details, please check our paper: [Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
hfl/chinese-macbert-large
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
feature-extraction
|
transformers
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
{"language": ["zh"], "license": "cc-by-nc-sa-4.0"}
|
hfl/chinese-pert-base
| null |
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
feature-extraction
|
transformers
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
{"language": ["zh"], "license": "cc-by-nc-sa-4.0"}
|
hfl/chinese-pert-large
| null |
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
hfl/chinese-roberta-wwm-ext-large
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
hfl/chinese-roberta-wwm-ext
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/Google official XLNet: https://github.com/zihangdai/xlnet
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-xlnet-base
| null |
[
"transformers",
"pytorch",
"tf",
"xlnet",
"text-generation",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/Google official XLNet: https://github.com/zihangdai/xlnet
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
hfl/chinese-xlnet-mid
| null |
[
"transformers",
"pytorch",
"tf",
"xlnet",
"text-generation",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM
You may also interested in,
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
{"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"}
|
hfl/cino-base-v2
| null |
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM
You may also interested in,
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
{"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"}
|
hfl/cino-large-v2
| null |
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM
You may also interested in,
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
{"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"}
|
hfl/cino-large
| null |
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM
You may also interested in,
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
{"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"}
|
hfl/cino-small-v2
| null |
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
feature-extraction
|
transformers
|
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0"}
|
hfl/english-pert-base
| null |
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
feature-extraction
|
transformers
|
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0"}
|
hfl/english-pert-large
| null |
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# This is a re-trained 3-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"], "pipeline_tag": "fill-mask"}
|
hfl/rbt3
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# This is a re-trained 4-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
hfl/rbt4
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# This is a re-trained 6-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
hfl/rbt6
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
hfl/rbtl3
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
image-classification
|
transformers
|
# fruits
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### apple
#### banana
#### mango
#### orange
#### tomato
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
hgarg/fruits
| null |
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
image-classification
|
transformers
|
# indian-snacks
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### dosa
#### idli
#### naan
#### samosa
#### vada
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
hgarg/indian-snacks
| null |
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-fa-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: 0.4404
- Wer: 0.4402
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.083 | 0.75 | 300 | 3.0037 | 1.0 |
| 1.5795 | 1.5 | 600 | 0.9167 | 0.7638 |
| 0.658 | 2.25 | 900 | 0.5737 | 0.5595 |
| 0.4213 | 3.0 | 1200 | 0.4404 | 0.4402 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-fa-colab", "results": []}]}
|
hgharibi/wav2vec2-xls-r-300m-fa-colab
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
{}
|
hgharibi/wav2vec2-xls-r-300m-fa
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ar-mbertmodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ar-mbertmodel-monotok-adapter
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ar-mbertmodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ar-monomodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ar-monomodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/fi-mbertmodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/fi-mbertmodel-monotok-adapter
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/fi-mbertmodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/fi-monomodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/fi-monomodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/id-mbertmodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/id-mbertmodel-monotok-adapter
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/id-mbertmodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/id-monomodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/id-monomodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ko-mbertmodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ko-mbertmodel-monotok-adapter
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ko-mbertmodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ko-monomodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/ko-monomodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/tr-mbertmodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/tr-mbertmodel-monotok-adapter
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/tr-mbertmodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/tr-monomodel-mberttok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hgiyt/tr-monomodel-monotok
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
{}
|
hgw3lss/gpt-j-6B-Buckland
| null |
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
hhou435/chinese_roberta_L-2_H-128
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
hide-JP/distilbert-base-uncased-finetuned-imdb-accelerate
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
hide-JP/distilbert-base-uncased-finetuned-imdb
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
higgzy/paraphrase-distilroberta-base-v1-finetuned-journal-conference
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
highnoon/longformer-base-4096-finetuned-ner-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
highnoon/longformer-base-4096-finetuned-squadv2-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
highnoon/longformerwikiannen-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
higopires/roB3rta
| null |
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
# BETO(cased)
This model was built using pytorch.
## Model description
Input for the model: Any spanish text
Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
```python
# You can include sample code which will be formatted
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification")
model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training procedure
I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).
|
{"language": ["es"], "license": "apache-2.0", "tags": ["es", "ticket classification"], "datasets": ["self made to classify whether text is related to technology or not."], "metrics": ["fscore", "accuracy", "precision", "recall"]}
|
hiiamsid/BETO_es_binary_classification
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"ticket classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684327
- CO2 Emissions (in grams): 437.2441955971972
## Validation Metrics
- Loss: nan
- Rouge1: 3.7729
- Rouge2: 0.4152
- RougeL: 3.5066
- RougeLsum: 3.5167
- Gen Len: 5.0577
## 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/hiiamsid/autonlp-Summarization-20684327
```
|
{"language": "es", "tags": "autonlp", "datasets": ["hiiamsid/autonlp-data-Summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 437.2441955971972}
|
hiiamsid/autonlp-Summarization-20684327
| null |
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"es",
"dataset:hiiamsid/autonlp-data-Summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684328
- CO2 Emissions (in grams): 1133.9679082840014
## Validation Metrics
- Loss: nan
- Rouge1: 9.4193
- Rouge2: 0.91
- RougeL: 7.9376
- RougeLsum: 8.0076
- Gen Len: 10.65
## 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/hiiamsid/autonlp-Summarization-20684328
```
|
{"language": "es", "tags": "autonlp", "datasets": ["hiiamsid/autonlp-data-Summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1133.9679082840014}
|
hiiamsid/autonlp-Summarization-20684328
| null |
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"es",
"dataset:hiiamsid/autonlp-data-Summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
This is the finetuned model of hiiamsid/est5-base for Question Generation task.
* Here input is the context only and output is questions. No information regarding answers were given to model.
* Unfortunately, due to lack of sufficient resources it is fine tuned with batch_size=10 and num_seq_len=256. So, if too large context is given model may not get information about last portions.
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
MODEL_NAME = 'hiiamsid/est5-base-qg'
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
model.cuda();
model.eval();
def generate_question(text, beams=10, grams=2, num_return_seq=10,max_size=256):
x = tokenizer(text, return_tensors='pt', padding=True).to(model.device)
out = model.generate(**x, no_repeat_ngram_size=grams, num_beams=beams, num_return_sequences=num_return_seq, max_length=max_size)
return tokenizer.decode(out[0], skip_special_tokens=True)
print(generate_question('Any context in spanish from which question is to be generated'))
```
## Citing & Authors
- Datasets : [squad_es](https://huggingface.co/datasets/squad_es)
- Model : [hiiamsid/est5-base](hiiamsid/est5-base)
|
{"language": ["es"], "license": "mit", "tags": ["spanish", "question generation", "qg"], "Datasets": ["SQUAD"]}
|
hiiamsid/est5-base-qg
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"spanish",
"question generation",
"qg",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Spanish embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Spanish tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
## Citing & Authors
- Datasets : [cleaned corpora](https://github.com/crscardellino/sbwce)
- Model : [google/mt5-base](https://huggingface.co/google/mt5-base)
- Reference: [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base)
|
{"language": ["es"], "license": "mit", "tags": ["spanish"]}
|
hiiamsid/est5-base
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"spanish",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only hindi embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Hindi tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
## Citing & Authors
- Model : [google/mt5-base](https://huggingface.co/google/mt5-base)
- Reference: [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base)
|
{"language": ["hi"], "license": "mit", "tags": ["hindi"]}
|
hiiamsid/hit5-base
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"hindi",
"hi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
sentence-similarity
|
sentence-transformers
|
# hiiamsid/sentence_similarity_hindi
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('hiiamsid/sentence_similarity_hindi')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
```
cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
0.825825032,0.8227195932,0.8127990959,0.8214681478,0.8111641963,0.8194870279,0.8096042841,0.8061808483
```
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 341 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": 4,
"evaluation_steps": 1000,
"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": 137,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
- Model: [setu4993/LaBSE]
(https://huggingface.co/setu4993/LaBSE)
- Sentence Transformers [Semantic Textual Similarity]
(https://www.sbert.net/examples/training/sts/README.html)
|
{"language": ["hi"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
|
hiiamsid/sentence_similarity_hindi
| null |
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"hi",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
sentence-similarity
|
sentence-transformers
|
# hiiamsid/sentence_similarity_spanish_es
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ['Mi nombre es Siddhartha', 'Mis amigos me llamaron por mi nombre Siddhartha']
model = SentenceTransformer('hiiamsid/sentence_similarity_spanish_es')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['Mi nombre es Siddhartha', 'Mis amigos me llamaron por mi nombre Siddhartha']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hiiamsid/sentence_similarity_spanish_es')
model = AutoModel.from_pretrained('hiiamsid/sentence_similarity_spanish_es')
# 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, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
```
cosine_pearson : 0.8280372842978689
cosine_spearman : 0.8232689765056079
euclidean_pearson : 0.81021993884437
euclidean_spearman : 0.8087904592393836
manhattan_pearson : 0.809645390126291
manhattan_spearman : 0.8077035464970413
dot_pearson : 0.7803662255836028
dot_spearman : 0.7699607641618339
```
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=hiiamsid/sentence_similarity_spanish_es)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 360 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:
```
{
"callback": null,
"epochs": 4,
"evaluation_steps": 1000,
"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": 144,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
- Datasets : [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt)
- Model : [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased)
- Sentence Transformers [Semantic Textual Similarity](https://www.sbert.net/examples/training/sts/README.html)
|
{"language": ["es"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
|
hiiamsid/sentence_similarity_spanish_es
| null |
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"es",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
hiiii23/Qtest
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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.
## 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
hiiii23/distilbert-base-uncased-finetuned-squad
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
hiking1/model1
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
<br />
<div align="center">
<img src="https://raw.githubusercontent.com/himanshu-dutta/pycoder/master/docs/pycoder-logo-p.png">
<br/>
<img alt="Made With Python" src="http://ForTheBadge.com/images/badges/made-with-python.svg" height=28 style="display:inline; height:28px;" />
<img alt="Medium" src="https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white" height=28 style="display:inline; height:28px;"/>
<a href="https://wandb.ai/himanshu-dutta/pycoder">
<img alt="WandB Dashboard" src="https://raw.githubusercontent.com/wandb/assets/04cfa58cc59fb7807e0423187a18db0c7430bab5/wandb-github-badge-28.svg" height=28 style="display:inline; height:28px;" />
</a>
[](https://pypi.org/project/pycoder/)
</div>
<div align="justify">
`PyCoder` is a tool to generate python code out of a few given topics and a description. It uses GPT-2 language model as its engine. Pycoder poses writing Python code as a conditional-Causal Language Modelling(c-CLM). It has been trained on millions of lines of Python code written by all of us. At the current stage and state of training, it produces sensible code with few lines of description, but the scope of improvement for the model is limitless.
Pycoder has been developed as a Command-Line tool (CLI), an API endpoint, as well as a python package (yet to be deployed to PyPI). This repository acts as a framework for anyone who either wants to try to build Pycoder from scratch or turn Pycoder into maybe a `CPPCoder` or `JSCoder` 😃. A blog post about the development of the project will be released soon.
To use `Pycoder` as a CLI utility, clone the repository as normal, and install the package with:
```console
foo@bar:❯ pip install pycoder
```
After this the package could be verified and accessed as either a native CLI tool or a python package with:
```console
foo@bar:❯ python -m pycoder --version
Or directly as:
foo@bar:❯ pycoder --version
```
On installation the CLI can be used directly, such as:
```console
foo@bar:❯ pycoder -t pytorch -t torch -d "a trainer class to train vision model" -ml 120
```
The API endpoint is deployed using FastAPI. Once all the requirements have been installed for the project, the API can be accessed with:
```console
foo@bar:❯ pycoder --endpoint PORT_NUMBER
Or
foo@bar:❯ pycoder -e PORT_NUMBER
```
</div>
## Tech Stack
<div align="center">
<img alt="Python" src="https://img.shields.io/badge/python-%2314354C.svg?style=for-the-badge&logo=python&logoColor=white" style="display:inline;" />
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white" style="display:inline;" />
<img alt="Transformers" src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" height=28 width=120 style="display:inline; background-color:white; height:28px; width:120px"/>
<img alt="Docker" src="https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white" style="display:inline;" />
<img src="https://fastapi.tiangolo.com/img/logo-margin/logo-teal.png" alt="FastAPI" height=28 style="display:inline; background-color:black; height:28px;" />
<img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" height=28 style="display:inline; background-color:teal; height:28px;" />
</div>
## Tested Platforms
<div align="center">
<img alt="Linux" src="https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=linux&logoColor=black" style="display:inline;" />
<img alt="Windows 10" src="https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white" style="display:inline;" />
</div>
## BibTeX
If you want to cite the framework feel free to use this:
```bibtex
@article{dutta2021pycoder,
title={Pycoder},
author={Dutta, H},
journal={GitHub. Note: https://github.com/himanshu-dutta/pycoder},
year={2021}
}
```
<hr />
<div align="center">
<img alt="MIT License" src="https://img.shields.io/github/license/himanshu-dutta/pycoder?style=for-the-badge&logo=appveyor" style="display:inline;" />
<img src="https://img.shields.io/badge/Copyright-Himanshu_Dutta-2ea44f?style=for-the-badge&logo=appveyor" style="display:inline;" />
</div>
|
{}
|
himanshu-dutta/pycoder-gpt2
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
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: 3.3780
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.08 | 10 | 14.0985 | 1.0 |
| No log | 0.16 | 20 | 13.8638 | 1.0004 |
| No log | 0.24 | 30 | 13.5135 | 1.0023 |
| No log | 0.32 | 40 | 12.8708 | 1.0002 |
| No log | 0.4 | 50 | 11.6927 | 1.0 |
| No log | 0.48 | 60 | 10.2733 | 1.0 |
| No log | 0.56 | 70 | 8.1396 | 1.0 |
| No log | 0.64 | 80 | 5.3503 | 1.0 |
| No log | 0.72 | 90 | 3.7975 | 1.0 |
| No log | 0.8 | 100 | 3.4275 | 1.0 |
| No log | 0.88 | 110 | 3.3596 | 1.0 |
| No log | 0.96 | 120 | 3.3780 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}
|
hiraki/wav2vec2-base-timit-demo-colab
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
GPT-2 chatbot - talk to Ray Smuckles
|
{"tags": ["conversational"]}
|
hireddivas/DialoGPT-small-ray
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
#GPT-2 model trained on Dana Scully's dialog.
|
{"tags": ["conversational"]}
|
hireddivas/DialoGPT-small-scully
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
GPT-2 chatbot - talk to Fox Mulder
|
{"tags": ["conversational"]}
|
hireddivas/dialoGPT-small-mulder
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
GPT-2 model trained on Phil from Eastenders
|
{"tags": ["conversational"]}
|
hireddivas/dialoGPT-small-phil
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
GPT-2 chatbot - talk to Sonic
|
{"tags": ["conversational"]}
|
hireddivas/dialoGPT-small-sonic
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This pretrained model is almost the same as [cl-tohoku/bert-base-japanese-char-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-char-v2) but do not need `fugashi` or `unidic_lite`.
The only difference is in `word_tokenzer_type` property (specify `basic` instead of `mecab`) in `tokenizer_config.json`.
|
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"]}
|
hiroshi-matsuda-rit/bert-base-japanese-basic-char-v2
| null |
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
hiroshi-matsuda-rit/ja_gsd
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
token-classification
|
spacy
|
Japanese transformer pipeline (bert-base). Components: transformer, parser, ner.
| Feature | Description |
| --- | --- |
| **Name** | `ja_gsd_bert_wwm_unidic_lite` |
| **Version** | `3.1.1` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `transformer`, `parser`, `ner` |
| **Components** | `transformer`, `parser`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD_Japanese-GSD](https://github.com/UniversalDependencies/UD_Japanese-GSD)<br />[UD_Japanese-GSD r2.8+NE](https://github.com/megagonlabs/UD_Japanese-GSD/releases/tag/r2.8-NE)<br />[SudachiDict_core](https://github.com/WorksApplications/SudachiDict)<br />[cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking)<br />[unidic_lite](https://github.com/polm/unidic-lite) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Megagon Labs Tokyo.](https://github.com/megagonlabs/UD_japanese_GSD) |
### Label Scheme
<details>
<summary>View label scheme (45 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `aux`, `case`, `cc`, `ccomp`, `compound`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `MOVEMENT`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PET_NAME`, `PHONE`, `PRODUCT`, `QUANTITY`, `TIME`, `TITLE_AFFIX`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `DEP_UAS` | 93.68 |
| `DEP_LAS` | 92.61 |
| `SENTS_P` | 92.02 |
| `SENTS_R` | 95.46 |
| `SENTS_F` | 93.71 |
| `ENTS_F` | 84.04 |
| `ENTS_P` | 84.96 |
| `ENTS_R` | 83.14 |
| `TAG_ACC` | 0.00 |
| `TRANSFORMER_LOSS` | 28861.67 |
| `PARSER_LOSS` | 1306248.63 |
| `NER_LOSS` | 13993.36 |
|
{"language": ["ja"], "license": "CC-BY-SA-4.0", "tags": ["spacy", "token-classification"]}
|
hiroshi-matsuda-rit/ja_gsd_bert_wwm_unidic_lite
| null |
[
"spacy",
"token-classification",
"ja",
"model-index",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
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: 0.4600
- Matthews Correlation: 0.5291
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5227 | 1.0 | 535 | 0.4715 | 0.4678 |
| 0.3493 | 2.0 | 1070 | 0.4600 | 0.5291 |
| 0.2393 | 3.0 | 1605 | 0.6018 | 0.5219 |
| 0.1714 | 4.0 | 2140 | 0.7228 | 0.5245 |
| 0.1289 | 5.0 | 2675 | 0.8154 | 0.5279 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.5.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5290966132843783, "name": "Matthews Correlation"}]}]}]}
|
histinct7002/distilbert-base-uncased-finetuned-cola
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
token-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0727
- Precision: 0.9334
- Recall: 0.9398
- F1: 0.9366
- Accuracy: 0.9845
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0271 | 1.0 | 878 | 0.0656 | 0.9339 | 0.9339 | 0.9339 | 0.9840 |
| 0.0136 | 2.0 | 1756 | 0.0703 | 0.9268 | 0.9380 | 0.9324 | 0.9838 |
| 0.008 | 3.0 | 2634 | 0.0727 | 0.9334 | 0.9398 | 0.9366 | 0.9845 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9334444444444444, "name": "Precision"}, {"type": "recall", "value": 0.9398142969012194, "name": "Recall"}, {"type": "f1", "value": 0.9366185406098445, "name": "F1"}, {"type": "accuracy", "value": 0.9845425516704529, "name": "Accuracy"}]}]}]}
|
histinct7002/distilbert-base-uncased-finetuned-ner
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
histinct7002/distilroberta-base-finetuned-wikitext2
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
Note: this model was superceded by the [`load_in_8bit=True` feature in transformers](https://github.com/huggingface/transformers/pull/17901)
by Younes Belkada and Tim Dettmers. Please see [this usage example](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4#scrollTo=W8tQtyjp75O).
This legacy model was built for [transformers v4.15.0](https://github.com/huggingface/transformers/releases/tag/v4.15.0) and pytorch 1.11. Newer versions could work, but are not supported.
### Quantized EleutherAI/gpt-j-6b with 8-bit weights
This is a version of EleutherAI's GPT-J with 6 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**.
Here's how to run it: [](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es)
__The [original GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)__ takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. Even if you cast everything to 16-bit, it will still not fit onto most single-GPU setups short of A6000 and A100. You can inference it [on TPU](https://colab.research.google.com/github/kingoflolz/mesh-transformer-jax/blob/master/colab_demo.ipynb) or CPUs, but fine-tuning is way more expensive.
Here, we apply several techniques to make GPT-J usable and fine-tunable on a single GPU with ~11 GB memory:
- large weight tensors are quantized using dynamic 8-bit quantization and de-quantized just-in-time for multiplication
- using gradient checkpoints to store one only activation per layer: using dramatically less memory at the cost of 30% slower training
- scalable fine-tuning with [LoRA](https://arxiv.org/abs/2106.09685) and [8-bit Adam](https://arxiv.org/abs/2110.02861)
In other words, all of the large weight-matrices are frozen in 8-bit, and you only train small adapters and optionally 1d tensors (layernorm scales, biases).
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. [This notebook measures wikitext test perplexity](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/check_perplexity.ipynb) and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use **8-bit only for storage, and all computations are performed in float16 or float32**. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from [the LoRA paper](https://arxiv.org/pdf/2106.09685.pdf).
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Where can I train for free?
You can train fine in colab, but if you get a K80, it's probably best to switch to other free gpu providers: [kaggle](https://towardsdatascience.com/amazon-sagemaker-studio-lab-a-great-alternative-to-google-colab-7194de6ef69a), [aws sagemaker](https://towardsdatascience.com/amazon-sagemaker-studio-lab-a-great-alternative-to-google-colab-7194de6ef69a) or [paperspace](https://docs.paperspace.com/gradient/more/instance-types/free-instances). For intance, this is the same notebook [running in kaggle](https://www.kaggle.com/justheuristic/dmazur-converted) using a more powerful P100 instance.
### Can I use this technique with other models?
The model was converted using [this notebook](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb). It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["The Pile"]}
|
hivemind/gpt-j-6B-8bit
| null |
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"en",
"arxiv:2106.09685",
"arxiv:2110.02861",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
hizella/aBERT
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
hizella/aBERT_test
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
hizella/aBERT_v2
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
hjgwak/ViBE
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
hjj/hh
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{"tags": ["converstional"]}
|
hksenpai/DialoGPT-small-rick
| null |
[
"transformers",
"pytorch",
"converstional",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
hkunlp/T5_3b_finetune_kvret_glmp2
| null |
[
"transformers",
"pytorch",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
hkunlp/T5_base_finetune_all_tasks_2upsample2
| null |
[
"transformers",
"pytorch",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
hkunlp/T5_base_prefix_all_tasks_2upsample2
| null |
[
"transformers",
"pytorch",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
hkunlp/T5_large_finetune_kvret_glmp2
| null |
[
"transformers",
"pytorch",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
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