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fill-mask
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transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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-180g-base-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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-180g-large-discriminator
| null |
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
fill-mask
|
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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-180g-large-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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-180g-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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-180g-small-ex-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
fill-mask
|
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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-180g-small-ex-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
fill-mask
|
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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-180g-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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-base-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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-large-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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-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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle 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.\nFor 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.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<a href="URL
<img alt="GitHub" src="URL
</a>
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". You can read our camera-ready paper through ACL Anthology or arXiv pre-print.
Revisiting Pre-trained Models for Chinese Natural Language Processing
You may also interested in,
* Chinese BERT series: URL
* Chinese ELECTRA: URL
* Chinese XLNet: URL
* Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
Introduction
------------
MacBERT is an improved BERT with novel MLM as correction 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), 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.
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
If you find our resource or paper is useful, please consider including the following citation in your paper.
* URL
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<a href="URL
<img alt="GitHub" src="URL
</a>
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". You can read our camera-ready paper through ACL Anthology or arXiv pre-print.
Revisiting Pre-trained Models for Chinese Natural Language Processing
You may also interested in,
* Chinese BERT series: URL
* Chinese ELECTRA: URL
* Chinese XLNet: URL
* Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
Introduction
------------
MacBERT is an improved BERT with novel MLM as correction 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), 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.
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
If you find our resource or paper is useful, please consider including the following citation in your paper.
* URL
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: URL
|
[
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
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
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: URL
|
[
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
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
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#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
|
# 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
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#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 \n",
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#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
|
# 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
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#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 \n",
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese Pre-Trained XLNet\nThis 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.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Chinese Pre-Trained XLNet\nThis 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.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## 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: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese Pre-Trained XLNet\nThis 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.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Chinese Pre-Trained XLNet\nThis 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.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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
|
[] |
[
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] |
TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## 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): URL
You may also interested in,
Chinese MacBERT: URL
Chinese BERT series: URL
Chinese ELECTRA: URL
Chinese XLNet: URL
Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
|
[
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo 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 \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo 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 \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
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
|
[] |
[
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] |
TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## 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): URL
You may also interested in,
Chinese MacBERT: URL
Chinese BERT series: URL
Chinese ELECTRA: URL
Chinese XLNet: URL
Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
|
[
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo 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 \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo 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 \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
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
|
[] |
[
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] |
TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## 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): URL
You may also interested in,
Chinese MacBERT: URL
Chinese BERT series: URL
Chinese ELECTRA: URL
Chinese XLNet: URL
Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
|
[
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo 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 \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo 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 \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
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
|
[] |
[
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] |
TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## 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): URL
You may also interested in,
Chinese MacBERT: URL
Chinese BERT series: URL
Chinese ELECTRA: URL
Chinese XLNet: URL
Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
|
[
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo 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 \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo 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 \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
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
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# 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: URL
|
[
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
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
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# 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: URL
|
[
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
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
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 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
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# This is a re-trained 3-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 3-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 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
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# This is a re-trained 4-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 4-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 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
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# This is a re-trained 6-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 6-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 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
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# fruits
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### apple
!apple
#### banana
!banana
#### mango
!mango
#### orange
!orange
#### tomato
!tomato
|
[
"# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### apple\n\n!apple",
"#### banana\n\n!banana",
"#### mango\n\n!mango",
"#### orange\n\n!orange",
"#### tomato\n\n!tomato"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### apple\n\n!apple",
"#### banana\n\n!banana",
"#### mango\n\n!mango",
"#### orange\n\n!orange",
"#### tomato\n\n!tomato"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# indian-snacks
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### dosa
!dosa
#### idli
!idli
#### naan
!naan
#### samosa
!samosa
#### vada
!vada
|
[
"# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### dosa\n\n!dosa",
"#### idli\n\n!idli",
"#### naan\n\n!naan",
"#### samosa\n\n!samosa",
"#### vada\n\n!vada"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### dosa\n\n!dosa",
"#### idli\n\n!idli",
"#### naan\n\n!naan",
"#### samosa\n\n!samosa",
"#### vada\n\n!vada"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-xls-r-300m-fa-colab
============================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# 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*:
## Training procedure
I trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased.
|
[
"# BETO(cased)\nThis model was built using pytorch.",
"## Model description\nInput for the model: Any spanish text\nOutput for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))",
"#### How to use\nHere is how to use this model to get the features of a given text in *PyTorch*:",
"## Training procedure\nI trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased."
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BETO(cased)\nThis model was built using pytorch.",
"## Model description\nInput for the model: Any spanish text\nOutput for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))",
"#### How to use\nHere is how to use this model to get the features of a given text in *PyTorch*:",
"## Training procedure\nI trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased."
] |
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
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 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:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684327\n- CO2 Emissions (in grams): 437.2441955971972",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 3.7729\n- Rouge2: 0.4152\n- RougeL: 3.5066\n- RougeLsum: 3.5167\n- Gen Len: 5.0577",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684327\n- CO2 Emissions (in grams): 437.2441955971972",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 3.7729\n- Rouge2: 0.4152\n- RougeL: 3.5066\n- RougeLsum: 3.5167\n- Gen Len: 5.0577",
"## Usage\n\nYou can use cURL to access this model:"
] |
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
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 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:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684328\n- CO2 Emissions (in grams): 1133.9679082840014",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 9.4193\n- Rouge2: 0.91\n- RougeL: 7.9376\n- RougeLsum: 8.0076\n- Gen Len: 10.65",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684328\n- CO2 Emissions (in grams): 1133.9679082840014",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 9.4193\n- Rouge2: 0.91\n- RougeL: 7.9376\n- RougeLsum: 8.0076\n- Gen Len: 10.65",
"## Usage\n\nYou can use cURL to access this model:"
] |
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
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #spanish #question generation #qg #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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.
## Citing & Authors
- Datasets : squad_es
- Model : hiiamsid/est5-base
|
[
"## Citing & Authors\n- Datasets : squad_es\n- Model : hiiamsid/est5-base"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #spanish #question generation #qg #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Datasets : squad_es\n- Model : hiiamsid/est5-base"
] |
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
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #spanish #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a smaller version of the 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
- Model : google/mt5-base
- Reference: cointegrated/rut5-base
|
[
"## Citing & Authors\n- Datasets : cleaned corpora\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #spanish #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Datasets : cleaned corpora\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
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
|
[] |
[
"hi"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #hindi #hi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a smaller version of the 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
- Reference: cointegrated/rut5-base
|
[
"## Citing & Authors\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #hindi #hi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
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
|
[] |
[
"hi"
] |
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #hi #endpoints_compatible #region-us
|
# hiiamsid/sentence_similarity_hindi
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, 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.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 341 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
- Model: [setu4993/LaBSE]
(URL
- Sentence Transformers [Semantic Textual Similarity]
(URL
|
[
"# hiiamsid/sentence_similarity_hindi\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, 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.",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 341 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n\n\n- Model: [setu4993/LaBSE]\n(URL\n- Sentence Transformers [Semantic Textual Similarity]\n(URL"
] |
[
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #hi #endpoints_compatible #region-us \n",
"# hiiamsid/sentence_similarity_hindi\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, 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.",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 341 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n\n\n- Model: [setu4993/LaBSE]\n(URL\n- Sentence Transformers [Semantic Textual Similarity]\n(URL"
] |
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
|
[] |
[
"es"
] |
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #es #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# hiiamsid/sentence_similarity_spanish_es
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, 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.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 360 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
- Datasets : stsb_multi_mt
- Model : dccuchile/bert-base-spanish-wwm-cased
- Sentence Transformers Semantic Textual Similarity
|
[
"# hiiamsid/sentence_similarity_spanish_es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, 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.",
"## Evaluation Results\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 360 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n- Datasets : stsb_multi_mt\n- Model : dccuchile/bert-base-spanish-wwm-cased\n- Sentence Transformers Semantic Textual Similarity"
] |
[
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #es #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# hiiamsid/sentence_similarity_spanish_es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, 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.",
"## Evaluation Results\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 360 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n- Datasets : stsb_multi_mt\n- Model : dccuchile/bert-base-spanish-wwm-cased\n- Sentence Transformers Semantic Textual Similarity"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of 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
|
[
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<br />
<div align="center">
<img src="URL
<br/>
<img alt="Made With Python" src="URL height=28 style="display:inline; height:28px;" />
<img alt="Medium" src="URL height=28 style="display:inline; height:28px;"/>
<a href="URL
<img alt="WandB Dashboard" src="URL height=28 style="display:inline; height:28px;" />
</a>
![PyPI version URL](URL
</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:
After this the package could be verified and accessed as either a native CLI tool or a python package with:
On installation the CLI can be used directly, such as:
The API endpoint is deployed using FastAPI. Once all the requirements have been installed for the project, the API can be accessed with:
</div>
## Tech Stack
<div align="center">
<img alt="Python" src="URL style="display:inline;" />
<img alt="PyTorch" src="URL style="display:inline;" />
<img alt="Transformers" src="URL height=28 width=120 style="display:inline; background-color:white; height:28px; width:120px"/>
<img alt="Docker" src="URL style="display:inline;" />
<img src="URL alt="FastAPI" height=28 style="display:inline; background-color:black; height:28px;" />
<img src="URL height=28 style="display:inline; background-color:teal; height:28px;" />
</div>
## Tested Platforms
<div align="center">
<img alt="Linux" src="URL style="display:inline;" />
<img alt="Windows 10" src="URL style="display:inline;" />
</div>
## BibTeX
If you want to cite the framework feel free to use this:
<hr />
<div align="center">
<img alt="MIT License" src="URL style="display:inline;" />
<img src="URL style="display:inline;" />
</div>
|
[
"## Tech Stack\n<div align=\"center\">\n<img alt=\"Python\" src=\"URL style=\"display:inline;\" />\n<img alt=\"PyTorch\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Transformers\" src=\"URL height=28 width=120 style=\"display:inline; background-color:white; height:28px; width:120px\"/>\n<img alt=\"Docker\" src=\"URL style=\"display:inline;\" />\n<img src=\"URL alt=\"FastAPI\" height=28 style=\"display:inline; background-color:black; height:28px;\" /> \n<img src=\"URL height=28 style=\"display:inline; background-color:teal; height:28px;\" />\n</div>",
"## Tested Platforms\n<div align=\"center\">\n<img alt=\"Linux\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Windows 10\" src=\"URL style=\"display:inline;\" />\n</div>",
"## BibTeX\nIf you want to cite the framework feel free to use this:\n\n\n<hr />\n\n<div align=\"center\">\n<img alt=\"MIT License\" src=\"URL style=\"display:inline;\" /> \n<img src=\"URL style=\"display:inline;\" />\n</div>"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Tech Stack\n<div align=\"center\">\n<img alt=\"Python\" src=\"URL style=\"display:inline;\" />\n<img alt=\"PyTorch\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Transformers\" src=\"URL height=28 width=120 style=\"display:inline; background-color:white; height:28px; width:120px\"/>\n<img alt=\"Docker\" src=\"URL style=\"display:inline;\" />\n<img src=\"URL alt=\"FastAPI\" height=28 style=\"display:inline; background-color:black; height:28px;\" /> \n<img src=\"URL height=28 style=\"display:inline; background-color:teal; height:28px;\" />\n</div>",
"## Tested Platforms\n<div align=\"center\">\n<img alt=\"Linux\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Windows 10\" src=\"URL style=\"display:inline;\" />\n</div>",
"## BibTeX\nIf you want to cite the framework feel free to use this:\n\n\n<hr />\n\n<div align=\"center\">\n<img alt=\"MIT License\" src=\"URL style=\"display:inline;\" /> \n<img src=\"URL style=\"display:inline;\" />\n</div>"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
GPT-2 chatbot - talk to Ray Smuckles
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#GPT-2 model trained on Dana Scully's dialog.
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
GPT-2 chatbot - talk to Fox Mulder
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
GPT-2 model trained on Phil from Eastenders
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
GPT-2 chatbot - talk to Sonic
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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
|
[] |
[
"ja"
] |
TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 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 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'.
|
[
"# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.\nThe only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'."
] |
[
"TAGS\n#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.\nThe only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'."
] |
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
|
[] |
[
"ja"
] |
TAGS
#spacy #token-classification #ja #model-index #region-us
|
Japanese transformer pipeline (bert-base). Components: transformer, parser, ner.
### Label Scheme
View label scheme (45 labels for 2 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (45 labels for 2 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #ja #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (45 labels for 2 components)",
"### Accuracy"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.12.5
* Pytorch 1.5.1
* Datasets 1.18.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.5.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.5.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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
|
[
"2106.09685",
"2110.02861"
] |
[
"en"
] |
TAGS
#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
|
Note: this model was superceded by the 'load_in_8bit=True' feature in transformers
by Younes Belkada and Tim Dettmers. Please see this usage example.
This legacy model was built for transformers 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: .
!img
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. This notebook measures wikitext test perplexity 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.
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, aws sagemaker or paperspace. For intance, this is the same notebook running in kaggle using a more powerful P100 instance.
### Can I use this technique with other models?
The model was converted using this notebook. 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.
|
[
"### Quantized EleutherAI/gpt-j-6b with 8-bit weights\n\nThis 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).\n\nHere's how to run it: .\n\n!img\n\n\n__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. This notebook measures wikitext test perplexity and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.\n\nOur 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.\n\n\n__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?\n\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.",
"### Where can I train for free?\n\nYou can train fine in colab, but if you get a K80, it's probably best to switch to other free gpu providers: kaggle, aws sagemaker or paperspace. For intance, this is the same notebook running in kaggle using a more powerful P100 instance.",
"### Can I use this technique with other models?\n\nThe model was converted using this notebook. 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."
] |
[
"TAGS\n#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 \n",
"### Quantized EleutherAI/gpt-j-6b with 8-bit weights\n\nThis 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).\n\nHere's how to run it: .\n\n!img\n\n\n__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. This notebook measures wikitext test perplexity and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.\n\nOur 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.\n\n\n__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?\n\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.",
"### Where can I train for free?\n\nYou can train fine in colab, but if you get a K80, it's probably best to switch to other free gpu providers: kaggle, aws sagemaker or paperspace. For intance, this is the same notebook running in kaggle using a more powerful P100 instance.",
"### Can I use this technique with other models?\n\nThe model was converted using this notebook. 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."
] |
null |
transformers
|
This is the ckpt of prefix-tuning model we trained on 21 tasks using a upsampling temp of 2.
Note: The prefix module is large due to the fact we keep the re-param weight and didn't compress it to make it more original and extendable for researchers.
|
{}
|
hkunlp/T5_large_prefix_all_tasks_2upsample2
| null |
[
"transformers",
"pytorch",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us
|
This is the ckpt of prefix-tuning model we trained on 21 tasks using a upsampling temp of 2.
Note: The prefix module is large due to the fact we keep the re-param weight and didn't compress it to make it more original and extendable for researchers.
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition
|
transformers
|
Convert from model .pt to transformer
Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h
Bash:
```bash
pip install transformers[sentencepiece]
pip install fairseq -U
git clone https://github.com/huggingface/transformers.git
cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py .
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt
mkdir dict
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt
mkdir outputs
python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py
--pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt
--dict_path ./dict/dict.ltr.txt --not_finetuned
```
# install and upload model
```
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
git lfs install
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo
ls
cd wav2vec-demo/
git status
git add .
git commit -m "First model version"
git config --global user.email [yourname]
git config --global user.name [yourpass]
git commit -m "First model version"
git push
```
|
{}
|
hoangbinhmta99/wav2vec-demo
| null |
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
Convert from model .pt to transformer
Link: URL
Bash:
# install and upload model
|
[
"# install and upload model"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# install and upload model"
] |
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. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0604
- Precision: 0.9247
- Recall: 0.9343
- F1: 0.9295
- Accuracy: 0.9854
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2082 | 1.0 | 753 | 0.0657 | 0.8996 | 0.9256 | 0.9125 | 0.9821 |
| 0.0428 | 2.0 | 1506 | 0.0595 | 0.9268 | 0.9343 | 0.9305 | 0.9848 |
| 0.0268 | 3.0 | 2259 | 0.0604 | 0.9247 | 0.9343 | 0.9295 | 0.9854 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9853695435592783}}]}]}
|
hoanhkhoa/bert-base-uncased-finetuned-ner
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0604
* Precision: 0.9247
* Recall: 0.9343
* F1: 0.9295
* Accuracy: 0.9854
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
### Framework versions
* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0381
- Precision: 0.9469
- Recall: 0.9530
- F1: 0.9500
- Accuracy: 0.9915
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1328 | 1.0 | 753 | 0.0492 | 0.9143 | 0.9308 | 0.9225 | 0.9884 |
| 0.0301 | 2.0 | 1506 | 0.0378 | 0.9421 | 0.9474 | 0.9448 | 0.9910 |
| 0.0185 | 3.0 | 2259 | 0.0381 | 0.9469 | 0.9530 | 0.9500 | 0.9915 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "roberta-base-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9914674251177673}}]}]}
|
hoanhkhoa/roberta-base-finetuned-ner
| null |
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
roberta-base-finetuned-ner
==========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0381
* Precision: 0.9469
* Recall: 0.9530
* F1: 0.9500
* Accuracy: 0.9915
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
### Framework versions
* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7004
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.316 | 1.0 | 2363 | 2.0234 |
| 2.0437 | 2.0 | 4726 | 1.7881 |
| 1.9058 | 3.0 | 7089 | 1.7004 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
hogger32/distilbert-base-uncased-finetuned-squad
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7004
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# xlmRoberta-for-VietnameseQA
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the UIT-Viquad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8315
## Model description
Fine-tuned by Honganh Nguyen (FPTU AI Club).
## Intended uses & limitations
More information needed
## Training and evaluation data
Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data.
Training data: https://github.com/vietnguyen012/QA_viuit/blob/main/train.json
Evaluation data: https://github.com/vietnguyen012/QA_viuit/blob/main/trial/trial.json
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5701 | 1.0 | 2534 | 1.2220 |
| 1.2942 | 2.0 | 5068 | 0.9698 |
| 1.0693 | 3.0 | 7602 | 0.8315 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "xlmRoberta-for-VietnameseQA", "results": []}]}
|
hogger32/xlmRoberta-for-VietnameseQA
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
|
xlmRoberta-for-VietnameseQA
===========================
This model is a fine-tuned version of xlm-roberta-base on the UIT-Viquad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8315
Model description
-----------------
Fine-tuned by Honganh Nguyen (FPTU AI Club).
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data.
Training data: URL
Evaluation data: URL
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
text-generation
|
transformers
|
# Zhongli, but not Zhongli
|
{"tags": ["conversational"]}
|
honguyenminh/old-zhongli
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Zhongli, but not Zhongli
|
[
"# Zhongli, but not Zhongli"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Zhongli, but not Zhongli"
] |
null | null |
dd
|
{}
|
hooni/bert-fine-tuned-cola
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
dd
|
[] |
[
"TAGS\n#region-us \n"
] |
text-generation
|
transformers
|
#Joey DialoGPT Model
|
{"tags": ["conversational"]}
|
houssaineamzil/DialoGPT-small-joey
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Joey DialoGPT Model
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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: 0.4241
- Wer: 0.3381
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7749 | 4.0 | 500 | 2.0639 | 1.0018 |
| 0.9252 | 8.0 | 1000 | 0.4853 | 0.4821 |
| 0.3076 | 12.0 | 1500 | 0.4507 | 0.4044 |
| 0.1732 | 16.0 | 2000 | 0.4315 | 0.3688 |
| 0.1269 | 20.0 | 2500 | 0.4481 | 0.3559 |
| 0.1087 | 24.0 | 3000 | 0.4354 | 0.3464 |
| 0.0832 | 28.0 | 3500 | 0.4241 | 0.3381 |
### 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": []}]}
|
hrdipto/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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4241
* Wer: 0.3381
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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-bangla-command-generated-data-finetune
This model is a fine-tuned version of [hrdipto/wav2vec2-xls-r-300m-bangla-command-data](https://huggingface.co/hrdipto/wav2vec2-xls-r-300m-bangla-command-data) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0099
- eval_wer: 0.0208
- eval_runtime: 2.5526
- eval_samples_per_second: 75.217
- eval_steps_per_second: 9.402
- epoch: 71.43
- step: 2000
## 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: 100
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-300m-bangla-command-generated-data-finetune", "results": []}]}
|
hrdipto/wav2vec2-xls-r-300m-bangla-command-generated-data-finetune
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-bangla-command-generated-data-finetune
This model is a fine-tuned version of hrdipto/wav2vec2-xls-r-300m-bangla-command-data on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0099
- eval_wer: 0.0208
- eval_runtime: 2.5526
- eval_samples_per_second: 75.217
- eval_steps_per_second: 9.402
- epoch: 71.43
- step: 2000
## 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: 100
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# wav2vec2-xls-r-300m-bangla-command-generated-data-finetune\n\nThis model is a fine-tuned version of hrdipto/wav2vec2-xls-r-300m-bangla-command-data on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0099\n- eval_wer: 0.0208\n- eval_runtime: 2.5526\n- eval_samples_per_second: 75.217\n- eval_steps_per_second: 9.402\n- epoch: 71.43\n- step: 2000",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 100\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-bangla-command-generated-data-finetune\n\nThis model is a fine-tuned version of hrdipto/wav2vec2-xls-r-300m-bangla-command-data on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0099\n- eval_wer: 0.0208\n- eval_runtime: 2.5526\n- eval_samples_per_second: 75.217\n- eval_steps_per_second: 9.402\n- epoch: 71.43\n- step: 2000",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 100\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
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-tf-left-right-shuru-word-level
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0504
- Wer: 0.6859
## 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 23.217 | 23.81 | 500 | 1.3437 | 0.6859 |
| 1.1742 | 47.62 | 1000 | 1.0397 | 0.6859 |
| 1.0339 | 71.43 | 1500 | 1.0155 | 0.6859 |
| 0.9909 | 95.24 | 2000 | 1.0504 | 0.6859 |
### 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-xls-r-tf-left-right-shuru-word-level", "results": []}]}
|
hrdipto/wav2vec2-xls-r-tf-left-right-shuru-word-level
| 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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-xls-r-tf-left-right-shuru-word-level
=============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0504
* Wer: 0.6859
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: 100
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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-tf-left-right-shuru
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0921
- Wer: 1.2628
## 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.5528 | 23.81 | 500 | 0.5509 | 1.9487 |
| 0.2926 | 47.62 | 1000 | 0.1306 | 1.2756 |
| 0.1171 | 71.43 | 1500 | 0.1189 | 1.2628 |
| 0.0681 | 95.24 | 2000 | 0.0921 | 1.2628 |
### 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-xls-r-tf-left-right-shuru", "results": []}]}
|
hrdipto/wav2vec2-xls-r-tf-left-right-shuru
| 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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-xls-r-tf-left-right-shuru
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0921
* Wer: 1.2628
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: 100
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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-tf-left-right-trainer
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0090
- eval_wer: 0.0037
- eval_runtime: 11.2686
- eval_samples_per_second: 71.703
- eval_steps_per_second: 8.963
- epoch: 21.05
- step: 4000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### 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-xls-r-tf-left-right-trainer", "results": []}]}
|
hrdipto/wav2vec2-xls-r-tf-left-right-trainer
| 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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xls-r-tf-left-right-trainer
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0090
- eval_wer: 0.0037
- eval_runtime: 11.2686
- eval_samples_per_second: 71.703
- eval_steps_per_second: 8.963
- epoch: 21.05
- step: 4000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
[
"# wav2vec2-xls-r-tf-left-right-trainer\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0090\n- eval_wer: 0.0037\n- eval_runtime: 11.2686\n- eval_samples_per_second: 71.703\n- eval_steps_per_second: 8.963\n- epoch: 21.05\n- step: 4000",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-tf-left-right-trainer\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0090\n- eval_wer: 0.0037\n- eval_runtime: 11.2686\n- eval_samples_per_second: 71.703\n- eval_steps_per_second: 8.963\n- epoch: 21.05\n- step: 4000",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
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-timit-tokenizer-base
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: 3.0828
- 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.3134 | 4.03 | 500 | 3.0814 | 1.0 |
| 2.9668 | 8.06 | 1000 | 3.0437 | 1.0 |
| 2.9604 | 12.1 | 1500 | 3.0337 | 1.0 |
| 2.9619 | 16.13 | 2000 | 3.0487 | 1.0 |
| 2.9588 | 20.16 | 2500 | 3.0859 | 1.0 |
| 2.957 | 24.19 | 3000 | 3.0921 | 1.0 |
| 2.9555 | 28.22 | 3500 | 3.0828 | 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-xls-r-timit-tokenizer-base", "results": []}]}
|
hrdipto/wav2vec2-xls-r-timit-tokenizer-base
| 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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-xls-r-timit-tokenizer-base
===================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0828
* 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: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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-timit-tokenizer
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4285
- Wer: 0.3662
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1571 | 4.03 | 500 | 0.5235 | 0.5098 |
| 0.2001 | 8.06 | 1000 | 0.4172 | 0.4375 |
| 0.0968 | 12.1 | 1500 | 0.4562 | 0.4016 |
| 0.0607 | 16.13 | 2000 | 0.4640 | 0.4050 |
| 0.0409 | 20.16 | 2500 | 0.4688 | 0.3914 |
| 0.0273 | 24.19 | 3000 | 0.4414 | 0.3763 |
| 0.0181 | 28.22 | 3500 | 0.4285 | 0.3662 |
### 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-xls-r-timit-tokenizer", "results": []}]}
|
hrdipto/wav2vec2-xls-r-timit-tokenizer
| 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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-xls-r-timit-tokenizer
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4285
* Wer: 0.3662
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`sdk`: _string_
Can be either `gradio` or `streamlit`
`sdk_version` : _string_
Only applicable for `streamlit` SDK.
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.
`pinned`: _boolean_
Whether the Space stays on top of your list.
|
{"title": "First Order Motion Model", "emoji": "\ud83d\udc22", "colorFrom": "blue", "colorTo": "yellow", "sdk": "gradio", "app_file": "app.py", "pinned": false}
|
hrushikute/DanceOnTune
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'sdk': _string_
Can be either 'gradio' or 'streamlit'
'sdk_version' : _string_
Only applicable for 'streamlit' SDK.
See doc for more info on supported versions.
'app_file': _string_
Path to your main application file (which contains either 'gradio' or 'streamlit' Python code).
Path is relative to the root of the repository.
'pinned': _boolean_
Whether the Space stays on top of your list.
|
[
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'sdk': _string_ \nCan be either 'gradio' or 'streamlit'\n\n'sdk_version' : _string_ \nOnly applicable for 'streamlit' SDK. \nSee doc for more info on supported versions.\n\n'app_file': _string_ \nPath to your main application file (which contains either 'gradio' or 'streamlit' Python code). \nPath is relative to the root of the repository.\n\n'pinned': _boolean_ \nWhether the Space stays on top of your list."
] |
[
"TAGS\n#region-us \n",
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'sdk': _string_ \nCan be either 'gradio' or 'streamlit'\n\n'sdk_version' : _string_ \nOnly applicable for 'streamlit' SDK. \nSee doc for more info on supported versions.\n\n'app_file': _string_ \nPath to your main application file (which contains either 'gradio' or 'streamlit' Python code). \nPath is relative to the root of the repository.\n\n'pinned': _boolean_ \nWhether the Space stays on top of your list."
] |
text-generation
|
transformers
|
# Rick and Morty DialoGPT Model
|
{"tags": ["conversational"]}
|
hrv/DialoGPT-small-rick-morty
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model
|
[
"# Rick and Morty DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
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: 0.4125
- Wer: 0.3607
## 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2018 | 7.94 | 500 | 1.3144 | 0.8508 |
| 0.4671 | 15.87 | 1000 | 0.4737 | 0.4160 |
| 0.1375 | 23.81 | 1500 | 0.4125 | 0.3607 |
### 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": []}]}
|
hs788/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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4125
* Wer: 0.3607
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: 64
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
null | null |
Hi, this is Taiwan_House_Prediction.
|
{}
|
huang0624/Taiwan_House_Prediction
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Hi, this is Taiwan_House_Prediction.
|
[] |
[
"TAGS\n#region-us \n"
] |
null |
transformers
|
## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive BERT and then
allowing both adaptive width and depth using knowledge distillation.
* This code is modified based on the repository developed by Hugging Face: [Transformers v2.1.1](https://github.com/huggingface/transformers/tree/v2.1.1), and is released in [GitHub](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT).
### Reference
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.
[DynaBERT: Dynamic BERT with Adaptive Width and Depth](https://arxiv.org/abs/2004.04037).
```
@inproceedings{hou2020dynabert,
title = {DynaBERT: Dynamic BERT with Adaptive Width and Depth},
author = {Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu},
booktitle = {Advances in Neural Information Processing Systems},
year = {2020}
}
```
|
{}
|
huawei-noah/DynaBERT_MNLI
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:2004.04037",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"2004.04037"
] |
[] |
TAGS
#transformers #pytorch #jax #bert #arxiv-2004.04037 #endpoints_compatible #region-us
|
## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive BERT and then
allowing both adaptive width and depth using knowledge distillation.
* This code is modified based on the repository developed by Hugging Face: Transformers v2.1.1, and is released in GitHub.
### Reference
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.
DynaBERT: Dynamic BERT with Adaptive Width and Depth.
|
[
"## DynaBERT: Dynamic BERT with Adaptive Width and Depth\n\n* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and \nthe subnetworks of it have competitive performances as other similar-sized compressed models.\nThe training process of DynaBERT includes first training a width-adaptive BERT and then \nallowing both adaptive width and depth using knowledge distillation. \n\n* This code is modified based on the repository developed by Hugging Face: Transformers v2.1.1, and is released in GitHub.",
"### Reference\nLu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.\nDynaBERT: Dynamic BERT with Adaptive Width and Depth."
] |
[
"TAGS\n#transformers #pytorch #jax #bert #arxiv-2004.04037 #endpoints_compatible #region-us \n",
"## DynaBERT: Dynamic BERT with Adaptive Width and Depth\n\n* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and \nthe subnetworks of it have competitive performances as other similar-sized compressed models.\nThe training process of DynaBERT includes first training a width-adaptive BERT and then \nallowing both adaptive width and depth using knowledge distillation. \n\n* This code is modified based on the repository developed by Hugging Face: Transformers v2.1.1, and is released in GitHub.",
"### Reference\nLu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.\nDynaBERT: Dynamic BERT with Adaptive Width and Depth."
] |
null |
transformers
|
## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive BERT and then
allowing both adaptive width and depth using knowledge distillation.
* This code is modified based on the repository developed by Hugging Face: [Transformers v2.1.1](https://github.com/huggingface/transformers/tree/v2.1.1), and is released in [GitHub](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT).
### Reference
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.
[DynaBERT: Dynamic BERT with Adaptive Width and Depth](https://arxiv.org/abs/2004.04037).
```
@inproceedings{hou2020dynabert,
title = {DynaBERT: Dynamic BERT with Adaptive Width and Depth},
author = {Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu},
booktitle = {Advances in Neural Information Processing Systems},
year = {2020}
}
```
|
{}
|
huawei-noah/DynaBERT_SST-2
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:2004.04037",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"2004.04037"
] |
[] |
TAGS
#transformers #pytorch #jax #bert #arxiv-2004.04037 #endpoints_compatible #region-us
|
## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive BERT and then
allowing both adaptive width and depth using knowledge distillation.
* This code is modified based on the repository developed by Hugging Face: Transformers v2.1.1, and is released in GitHub.
### Reference
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.
DynaBERT: Dynamic BERT with Adaptive Width and Depth.
|
[
"## DynaBERT: Dynamic BERT with Adaptive Width and Depth\n\n* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and \nthe subnetworks of it have competitive performances as other similar-sized compressed models.\nThe training process of DynaBERT includes first training a width-adaptive BERT and then \nallowing both adaptive width and depth using knowledge distillation. \n\n* This code is modified based on the repository developed by Hugging Face: Transformers v2.1.1, and is released in GitHub.",
"### Reference\nLu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.\nDynaBERT: Dynamic BERT with Adaptive Width and Depth."
] |
[
"TAGS\n#transformers #pytorch #jax #bert #arxiv-2004.04037 #endpoints_compatible #region-us \n",
"## DynaBERT: Dynamic BERT with Adaptive Width and Depth\n\n* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and \nthe subnetworks of it have competitive performances as other similar-sized compressed models.\nThe training process of DynaBERT includes first training a width-adaptive BERT and then \nallowing both adaptive width and depth using knowledge distillation. \n\n* This code is modified based on the repository developed by Hugging Face: Transformers v2.1.1, and is released in GitHub.",
"### Reference\nLu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.\nDynaBERT: Dynamic BERT with Adaptive Width and Depth."
] |
null | null |
# Overview
<p align="center">
<img src="https://avatars.githubusercontent.com/u/12619994?s=200&v=4" width="150">
</p>
<!-- -------------------------------------------------------------------------------- -->
JABER (Junior Arabic BERt) is a 12-layer Arabic pretrained Language Model.
JABER obtained rank one on [ALUE leaderboard](https://www.alue.org/leaderboard) at `01/09/2021`.
This model is **only compatible** with the code in [this github repo](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/JABER-PyTorch) (not supported by the [Transformers](https://github.com/huggingface/transformers) library)
## Citation
Please cite the following [paper](https://arxiv.org/abs/2112.04329) when using our code and model:
``` bibtex
@misc{ghaddar2021jaber,
title={JABER: Junior Arabic BERt},
author={Abbas Ghaddar and Yimeng Wu and Ahmad Rashid and Khalil Bibi and Mehdi Rezagholizadeh and Chao Xing and Yasheng Wang and Duan Xinyu and Zhefeng Wang and Baoxing Huai and Xin Jiang and Qun Liu and Philippe Langlais},
year={2021},
eprint={2112.04329},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{}
|
huawei-noah/JABER
| null |
[
"pytorch",
"arxiv:2112.04329",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"2112.04329"
] |
[] |
TAGS
#pytorch #arxiv-2112.04329 #region-us
|
# Overview
<p align="center">
<img src="URL width="150">
</p>
JABER (Junior Arabic BERt) is a 12-layer Arabic pretrained Language Model.
JABER obtained rank one on ALUE leaderboard at '01/09/2021'.
This model is only compatible with the code in this github repo (not supported by the Transformers library)
Please cite the following paper when using our code and model:
|
[
"# Overview\n\n<p align=\"center\">\n <img src=\"URL width=\"150\">\n</p>\n\n\n\nJABER (Junior Arabic BERt) is a 12-layer Arabic pretrained Language Model. \nJABER obtained rank one on ALUE leaderboard at '01/09/2021'. \nThis model is only compatible with the code in this github repo (not supported by the Transformers library)\n \nPlease cite the following paper when using our code and model:"
] |
[
"TAGS\n#pytorch #arxiv-2112.04329 #region-us \n",
"# Overview\n\n<p align=\"center\">\n <img src=\"URL width=\"150\">\n</p>\n\n\n\nJABER (Junior Arabic BERt) is a 12-layer Arabic pretrained Language Model. \nJABER obtained rank one on ALUE leaderboard at '01/09/2021'. \nThis model is only compatible with the code in this github repo (not supported by the Transformers library)\n \nPlease cite the following paper when using our code and model:"
] |
null |
transformers
|
TinyBERT: Distilling BERT for Natural Language Understanding
========
TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand.
For more details about the techniques of TinyBERT, refer to our paper:
[TinyBERT: Distilling BERT for Natural Language Understanding](https://arxiv.org/abs/1909.10351)
Citation
========
If you find TinyBERT useful in your research, please cite the following paper:
```
@article{jiao2019tinybert,
title={Tinybert: Distilling bert for natural language understanding},
author={Jiao, Xiaoqi and Yin, Yichun and Shang, Lifeng and Jiang, Xin and Chen, Xiao and Li, Linlin and Wang, Fang and Liu, Qun},
journal={arXiv preprint arXiv:1909.10351},
year={2019}
}
```
|
{}
|
huawei-noah/TinyBERT_General_4L_312D
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"1909.10351"
] |
[] |
TAGS
#transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #has_space #region-us
|
TinyBERT: Distilling BERT for Natural Language Understanding
========
TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand.
For more details about the techniques of TinyBERT, refer to our paper:
TinyBERT: Distilling BERT for Natural Language Understanding
Citation
========
If you find TinyBERT useful in your research, please cite the following paper:
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #has_space #region-us \n"
] |
null | null |
This is an Audacity wrapper for the model, forked from the repository `groadabike/ConvTasNet_DAMP-VSEP_enhboth`,
This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied directly from `groadabike/ConvTasNet_DAMP-VSEP_enhboth`:
### Description:
This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.
### Training config:
```yaml
data:
channels: 1
n_src: 2
root_path: data
sample_rate: 16000
samples_per_track: 10
segment: 3.0
task: enh_both
filterbank:
kernel_size: 20
n_filters: 256
stride: 10
main_args:
exp_dir: exp/train_convtasnet
help: None
masknet:
bn_chan: 256
conv_kernel_size: 3
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 4
n_src: 2
norm_type: gLN
skip_chan: 256
optim:
lr: 0.0003
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 12
early_stop: True
epochs: 50
half_lr: True
num_workers: 12
```
### Results:
```yaml
si_sdr: 14.018196157142519
si_sdr_imp: 14.017103133809577
sdr: 14.498517291333885
sdr_imp: 14.463389151567865
sir: 24.149634529133372
sir_imp: 24.11450638936735
sar: 15.338597389045935
sar_imp: -137.30634122401517
stoi: 0.7639416744417206
stoi_imp: 0.1843383526963759
```
### License notice:
This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike.
|
{"tags": ["audacity"], "inference": false, "sample_rate": 8000}
|
hugggof/ConvTasNet-DAMP-Vocals
| null |
[
"audacity",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#audacity #region-us
|
This is an Audacity wrapper for the model, forked from the repository 'groadabike/ConvTasNet_DAMP-VSEP_enhboth',
This model was trained using the Asteroid library: URL
The following info was copied directly from 'groadabike/ConvTasNet_DAMP-VSEP_enhboth':
### Description:
This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.
### Training config:
### Results:
### License notice:
This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike.
|
[
"### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_DAMP-VSEP_enhboth\" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). \"ConvTasNet_DAMP-VSEP_enhboth\" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike."
] |
[
"TAGS\n#audacity #region-us \n",
"### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_DAMP-VSEP_enhboth\" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). \"ConvTasNet_DAMP-VSEP_enhboth\" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike."
] |
null | null |
This is an Audacity wrapper for the model, forked from the repository `JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k`,
This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied directly from `JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k`:
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_noisy` task of the Libri3Mix dataset.
Training config:
```yml
data:
n_src: 3
sample_rate: 16000
segment: 3
task: sep_noisy
train_dir: data/wav16k/min/train-360
valid_dir: data/wav16k/min/dev
filterbank:
kernel_size: 32
n_filters: 512
stride: 16
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 3
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
training:
batch_size: 8
early_stop: true
epochs: 200
half_lr: true
num_workers: 4
```
Results:
On Libri3Mix min test set :
```yml
si_sdr: 5.926151147554517
si_sdr_imp: 10.282912158535625
sdr: 6.700975236867358
sdr_imp: 10.882972447337504
sir: 15.364110064569388
sir_imp: 18.574476587171688
sar: 7.918866830474568
sar_imp: -0.9638973409971135
stoi: 0.7713777027310713
stoi_imp: 0.2078696167973911
```
License notice:
This work "ConvTasNet_Libri3Mix_sepnoisy_16k" is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by Vassil Panayotov,
used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); of The WSJ0 Hipster Ambient Mixtures
dataset by [Whisper.ai](http://wham.whisper.ai/), used under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
"ConvTasNet_Libri3Mix_sepnoisy_16k" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Joris Cosentino
|
{"tags": ["audacity"], "inference": false}
|
hugggof/ConvTasNet_Libri3Mix_sepnoisy_16k
| null |
[
"audacity",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#audacity #region-us
|
This is an Audacity wrapper for the model, forked from the repository 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k',
This model was trained using the Asteroid library: URL
The following info was copied directly from 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k':
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the 'sep_noisy' task of the Libri3Mix dataset.
Training config:
Results:
On Libri3Mix min test set :
License notice:
This work "ConvTasNet_Libri3Mix_sepnoisy_16k" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,
used under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures
dataset by URL, used under CC BY-NC 4.0.
"ConvTasNet_Libri3Mix_sepnoisy_16k" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino
|
[] |
[
"TAGS\n#audacity #region-us \n"
] |
null | null |
This is an Audacity wrapper for the model, forked from the repository mpariente/ConvTasNet_WHAM_sepclean,
This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied from `mpariente/ConvTasNet_WHAM_sepclean`:
### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! dataset.
### Training config:
```yaml
data:
n_src: 2
mode: min
nondefault_nsrc: None
sample_rate: 8000
segment: 3
task: sep_clean
train_dir: data/wav8k/min/tr/
valid_dir: data/wav8k/min/cv/
filterbank:
kernel_size: 16
n_filters: 512
stride: 8
main_args:
exp_dir: exp/wham
gpus: -1
help: None
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 2
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 24
early_stop: True
epochs: 200
half_lr: True
num_workers: 4
```
### Results:
```yaml
si_sdr: 16.21326632846293
si_sdr_imp: 16.21441705664987
sdr: 16.615180021738933
sdr_imp: 16.464137807433435
sir: 26.860503975131923
sir_imp: 26.709461760826414
sar: 17.18312813480803
sar_imp: -131.99332048277296
stoi: 0.9619940905157323
stoi_imp: 0.2239480672473015
```
### License notice:
This work "ConvTasNet_WHAM!_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for
Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only).
"ConvTasNet_WHAM!_sepclean" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente.
|
{"tags": ["audacity"], "inference": false}
|
hugggof/ConvTasNet_WHAM_sepclean
| null |
[
"audacity",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#audacity #region-us
|
This is an Audacity wrapper for the model, forked from the repository mpariente/ConvTasNet_WHAM_sepclean,
This model was trained using the Asteroid library: URL
The following info was copied from 'mpariente/ConvTasNet_WHAM_sepclean':
### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasNet recipe in Asteroid.
It was trained on the 'sep_clean' task of the WHAM! dataset.
### Training config:
### Results:
### License notice:
This work "ConvTasNet_WHAM!_sepclean" is a derivative of CSR-I (WSJ0) Complete
by LDC, used under LDC User Agreement for
Non-Members (Research only).
"ConvTasNet_WHAM!_sepclean" is licensed under Attribution-ShareAlike 3.0 Unported
by Manuel Pariente.
|
[
"### Description:\nThis model was trained by Manuel Pariente \nusing the wham/ConvTasNet recipe in Asteroid.\nIt was trained on the 'sep_clean' task of the WHAM! dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_WHAM!_sepclean\" is a derivative of CSR-I (WSJ0) Complete\nby LDC, used under LDC User Agreement for \nNon-Members (Research only). \n\"ConvTasNet_WHAM!_sepclean\" is licensed under Attribution-ShareAlike 3.0 Unported\nby Manuel Pariente."
] |
[
"TAGS\n#audacity #region-us \n",
"### Description:\nThis model was trained by Manuel Pariente \nusing the wham/ConvTasNet recipe in Asteroid.\nIt was trained on the 'sep_clean' task of the WHAM! dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_WHAM!_sepclean\" is a derivative of CSR-I (WSJ0) Complete\nby LDC, used under LDC User Agreement for \nNon-Members (Research only). \n\"ConvTasNet_WHAM!_sepclean\" is licensed under Attribution-ShareAlike 3.0 Unported\nby Manuel Pariente."
] |
null | null |
## Music Source Separation in the Waveform Domain
This is the Demucs model, serialized from Facebook Research's pretrained models.
From Facebook research:
Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initialization of weights and transposed convolutions in the decoder.
This is the `demucs_extra` version, meaning that is was trained on the MusDB dataset, along with 150 extra songs of data.
See [facebookresearch's repository](https://github.com/facebookresearch/demucs) for more information on Demucs.
|
{"tags": "audacity"}
|
hugggof/demucs_extra
| null |
[
"audacity",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#audacity #region-us
|
## Music Source Separation in the Waveform Domain
This is the Demucs model, serialized from Facebook Research's pretrained models.
From Facebook research:
Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initialization of weights and transposed convolutions in the decoder.
This is the 'demucs_extra' version, meaning that is was trained on the MusDB dataset, along with 150 extra songs of data.
See facebookresearch's repository for more information on Demucs.
|
[
"## Music Source Separation in the Waveform Domain\n\nThis is the Demucs model, serialized from Facebook Research's pretrained models. \n\nFrom Facebook research:\n\n Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initialization of weights and transposed convolutions in the decoder.\n\n\nThis is the 'demucs_extra' version, meaning that is was trained on the MusDB dataset, along with 150 extra songs of data. \n\nSee facebookresearch's repository for more information on Demucs."
] |
[
"TAGS\n#audacity #region-us \n",
"## Music Source Separation in the Waveform Domain\n\nThis is the Demucs model, serialized from Facebook Research's pretrained models. \n\nFrom Facebook research:\n\n Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initialization of weights and transposed convolutions in the decoder.\n\n\nThis is the 'demucs_extra' version, meaning that is was trained on the MusDB dataset, along with 150 extra songs of data. \n\nSee facebookresearch's repository for more information on Demucs."
] |
null | null |
# Labeler With Timestamps
## Being used for the `Audio Labeler` effect in Audacity
This is a audio labeler model which is used in Audacity's labeler effect.
metadata:
```
{
"sample_rate": 48000,
"domain_tags": ["Music"],
"tags": ["Audio Labeler"],
"effect_type": "waveform-to-labels",
"multichannel": false,
"labels": ["Acoustic Guitar", "Auxiliary Percussion", "Brass", "Clean Electric Guitar", "Distorted Electric Guitar", "Double Bass", "Drum Set", "Electric Bass", "Flute", "piano", "Reeds", "Saxophone", "Strings", "Trumpet", "Voice"],
"short_description": "Use me to label some instruments!",
"long_description": "An audio labeler, which outputs label predictions and time ranges for the labels. This model can label various instruments listed in the labels section."
}
```
|
{"tags": ["audacity"], "inference": false}
|
hugggof/openl3-labeler-w-timestamps
| null |
[
"audacity",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#audacity #region-us
|
# Labeler With Timestamps
## Being used for the 'Audio Labeler' effect in Audacity
This is a audio labeler model which is used in Audacity's labeler effect.
metadata:
|
[
"# Labeler With Timestamps",
"## Being used for the 'Audio Labeler' effect in Audacity\n\nThis is a audio labeler model which is used in Audacity's labeler effect. \n\nmetadata:"
] |
[
"TAGS\n#audacity #region-us \n",
"# Labeler With Timestamps",
"## Being used for the 'Audio Labeler' effect in Audacity\n\nThis is a audio labeler model which is used in Audacity's labeler effect. \n\nmetadata:"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/9fd98af9a817af8cd78636f71895b6ad.500x500x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">100 gecs</div>
<a href="https://genius.com/artists/100-gecs">
<div style="text-align: center; font-size: 14px;">@100-gecs</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 100 gecs.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/100-gecs).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/100-gecs")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3c9j4tvq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 100 gecs's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1v0ffa4e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1v0ffa4e/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/100-gecs')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/100-gecs")
model = AutoModelWithLMHead.from_pretrained("huggingartists/100-gecs")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
{"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/100-gecs"], "widget": [{"text": "I am"}]}
|
huggingartists/100-gecs
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/100-gecs",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/100-gecs #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> HuggingArtists Model </div>
<div style="text-align: center; font-size: 16px; font-weight: 800">100 gecs</div>
<a href="URL
<div style="text-align: center; font-size: 14px;">@100-gecs</div>
</a>
</div>
I was made with huggingartists.
Create your own bot based on your favorite artist with the demo!
## How does it work?
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on lyrics from 100 gecs.
Dataset is available here.
And can be used with:
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on 100 gecs's lyrics.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
Or with Transformers library:
## Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
![GitHub stars](URL
|
[
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 100 gecs.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 100 gecs's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/100-gecs #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 100 gecs.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 100 gecs's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/aa32202cc20d1dde62e57940a8b278b2.770x770x1.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">21 Savage</div>
<a href="https://genius.com/artists/21-savage">
<div style="text-align: center; font-size: 14px;">@21-savage</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 21 Savage.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/21-savage).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/21-savage")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3lbkznnf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 21 Savage's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1fw9b6m4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1fw9b6m4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/21-savage')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/21-savage")
model = AutoModelWithLMHead.from_pretrained("huggingartists/21-savage")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
{"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/21-savage"], "widget": [{"text": "I am"}]}
|
huggingartists/21-savage
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/21-savage",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/21-savage #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> HuggingArtists Model </div>
<div style="text-align: center; font-size: 16px; font-weight: 800">21 Savage</div>
<a href="URL
<div style="text-align: center; font-size: 14px;">@21-savage</div>
</a>
</div>
I was made with huggingartists.
Create your own bot based on your favorite artist with the demo!
## How does it work?
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on lyrics from 21 Savage.
Dataset is available here.
And can be used with:
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on 21 Savage's lyrics.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
Or with Transformers library:
## Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
![GitHub stars](URL
|
[
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 21 Savage.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 21 Savage's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/21-savage #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 21 Savage.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 21 Savage's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/4fedc5dd2830a874a5274bf1cac62002.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">25/17</div>
<a href="https://genius.com/artists/25-17">
<div style="text-align: center; font-size: 14px;">@25-17</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 25/17.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/25-17).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/25-17")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1iuytbjp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 25/17's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/knv4l4gw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/knv4l4gw/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/25-17')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/25-17")
model = AutoModelWithLMHead.from_pretrained("huggingartists/25-17")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
{"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/25-17"], "widget": [{"text": "I am"}]}
|
huggingartists/25-17
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/25-17",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/25-17 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> HuggingArtists Model </div>
<div style="text-align: center; font-size: 16px; font-weight: 800">25/17</div>
<a href="URL
<div style="text-align: center; font-size: 14px;">@25-17</div>
</a>
</div>
I was made with huggingartists.
Create your own bot based on your favorite artist with the demo!
## How does it work?
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on lyrics from 25/17.
Dataset is available here.
And can be used with:
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on 25/17's lyrics.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
Or with Transformers library:
## Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
![GitHub stars](URL
|
[
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 25/17.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 25/17's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/25-17 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 25/17.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 25/17's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/10f98dca7bcd1a31222e36374544cad5.1000x1000x1.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">50 Cent</div>
<a href="https://genius.com/artists/50-cent">
<div style="text-align: center; font-size: 14px;">@50-cent</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 50 Cent.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/50-cent).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/50-cent")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1291qx5n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 50 Cent's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1igwpphq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1igwpphq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/50-cent')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/50-cent")
model = AutoModelWithLMHead.from_pretrained("huggingartists/50-cent")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
{"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/50-cent"], "widget": [{"text": "I am"}]}
|
huggingartists/50-cent
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/50-cent",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/50-cent #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> HuggingArtists Model </div>
<div style="text-align: center; font-size: 16px; font-weight: 800">50 Cent</div>
<a href="URL
<div style="text-align: center; font-size: 14px;">@50-cent</div>
</a>
</div>
I was made with huggingartists.
Create your own bot based on your favorite artist with the demo!
## How does it work?
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on lyrics from 50 Cent.
Dataset is available here.
And can be used with:
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on 50 Cent's lyrics.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
Or with Transformers library:
## Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
![GitHub stars](URL
|
[
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 50 Cent.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 50 Cent's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/50-cent #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 50 Cent.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 50 Cent's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/289ded19d51d41798be99217d6059eb3.458x458x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">5’Nizza</div>
<a href="https://genius.com/artists/5nizza">
<div style="text-align: center; font-size: 14px;">@5nizza</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 5’Nizza.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/5nizza).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/5nizza")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1zcp1grf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 5’Nizza's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2zg6pzw7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2zg6pzw7/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/5nizza')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/5nizza")
model = AutoModelWithLMHead.from_pretrained("huggingartists/5nizza")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
{"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/5nizza"], "widget": [{"text": "I am"}]}
|
huggingartists/5nizza
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/5nizza",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/5nizza #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> HuggingArtists Model </div>
<div style="text-align: center; font-size: 16px; font-weight: 800">5’Nizza</div>
<a href="URL
<div style="text-align: center; font-size: 14px;">@5nizza</div>
</a>
</div>
I was made with huggingartists.
Create your own bot based on your favorite artist with the demo!
## How does it work?
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on lyrics from 5’Nizza.
Dataset is available here.
And can be used with:
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on 5’Nizza's lyrics.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
Or with Transformers library:
## Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
![GitHub stars](URL
|
[
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 5’Nizza.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 5’Nizza's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/5nizza #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 5’Nizza.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 5’Nizza's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/c56dce03a151e17a9626e55e6c295bb1.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">5opka</div>
<a href="https://genius.com/artists/5opka">
<div style="text-align: center; font-size: 14px;">@5opka</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 5opka.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/5opka).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/5opka")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1o2s4fw8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 5opka's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3vitposx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3vitposx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/5opka')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/5opka")
model = AutoModelWithLMHead.from_pretrained("huggingartists/5opka")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
{"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/5opka"], "widget": [{"text": "I am"}]}
|
huggingartists/5opka
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/5opka",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/5opka #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> HuggingArtists Model </div>
<div style="text-align: center; font-size: 16px; font-weight: 800">5opka</div>
<a href="URL
<div style="text-align: center; font-size: 14px;">@5opka</div>
</a>
</div>
I was made with huggingartists.
Create your own bot based on your favorite artist with the demo!
## How does it work?
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on lyrics from 5opka.
Dataset is available here.
And can be used with:
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on 5opka's lyrics.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
Or with Transformers library:
## Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
![GitHub stars](URL
|
[
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 5opka.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 5opka's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/5opka #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 5opka.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 5opka's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/b2b164a7c6c02dd0843ad597df5dbf4b.1000x1000x1.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">6ix9ine</div>
<a href="https://genius.com/artists/6ix9ine">
<div style="text-align: center; font-size: 14px;">@6ix9ine</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 6ix9ine.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/6ix9ine).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/6ix9ine")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/eqmcaj0r/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 6ix9ine's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/s5dpg3h2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/s5dpg3h2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/6ix9ine')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/6ix9ine")
model = AutoModelWithLMHead.from_pretrained("huggingartists/6ix9ine")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
{"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/6ix9ine"], "widget": [{"text": "I am"}]}
|
huggingartists/6ix9ine
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/6ix9ine",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/6ix9ine #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> HuggingArtists Model </div>
<div style="text-align: center; font-size: 16px; font-weight: 800">6ix9ine</div>
<a href="URL
<div style="text-align: center; font-size: 14px;">@6ix9ine</div>
</a>
</div>
I was made with huggingartists.
Create your own bot based on your favorite artist with the demo!
## How does it work?
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on lyrics from 6ix9ine.
Dataset is available here.
And can be used with:
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on 6ix9ine's lyrics.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
Or with Transformers library:
## Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
![GitHub stars](URL
|
[
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 6ix9ine.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 6ix9ine's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/6ix9ine #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 6ix9ine.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on 6ix9ine's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/894021d09a748eef8c6d63ad898b814b.650x430x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aaron Watson</div>
<a href="https://genius.com/artists/aaron-watson">
<div style="text-align: center; font-size: 14px;">@aaron-watson</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Aaron Watson.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/aaron-watson).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/aaron-watson")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/14ha1tnc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Aaron Watson's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34e4zb2v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34e4zb2v/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/aaron-watson')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/aaron-watson")
model = AutoModelWithLMHead.from_pretrained("huggingartists/aaron-watson")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
{"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/aaron-watson"], "widget": [{"text": "I am"}]}
|
huggingartists/aaron-watson
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/aaron-watson",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/aaron-watson #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> HuggingArtists Model </div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aaron Watson</div>
<a href="URL
<div style="text-align: center; font-size: 14px;">@aaron-watson</div>
</a>
</div>
I was made with huggingartists.
Create your own bot based on your favorite artist with the demo!
## How does it work?
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on lyrics from Aaron Watson.
Dataset is available here.
And can be used with:
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on Aaron Watson's lyrics.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
Or with Transformers library:
## Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
![GitHub stars](URL
|
[
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from Aaron Watson.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on Aaron Watson's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/aaron-watson #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from Aaron Watson.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on Aaron Watson's lyrics.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n\n\nOr with Transformers library:",
"## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
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