ADD ERNIE model (#5763)

* ERNIE model card

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Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

model_cards/nghuyong/ernie-1.0/README.md

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+---
+language: zh
+---
+
+# ERNIE-1.0
+
+## Introduction
+
+ERNIE (Enhanced Representation through kNowledge IntEgration) is proposed by Baidu in 2019,
+which is designed to learn language representation enhanced by knowledge masking strategies i.e. entity-level masking and phrase-level masking. 
+Experimental results show that ERNIE achieve state-of-the-art results on five Chinese natural language processing tasks including natural language inference, 
+semantic similarity, named entity recognition, sentiment analysis and question answering. 
+
+More detail: https://arxiv.org/abs/1904.09223
+
+## Released Model Info
+
+|Model Name|Language|Model Structure|
+|:---:|:---:|:---:|
+|ernie-1.0| Chinese |Layer:12, Hidden:768, Heads:12|
+
+This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and 
+a series of experiments have been conducted to check the accuracy of the conversion.
+
+- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
+- Pytorch Conversion repo:  https://github.com/nghuyong/ERNIE-Pytorch
+
+## How to use
+```Python
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0")
+model = AutoModel.from_pretrained("nghuyong/ernie-1.0")
+```
+
+## Citation
+
+```bibtex
+@article{sun2019ernie,
+  title={Ernie: Enhanced representation through knowledge integration},
+  author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Chen, Xuyi and Zhang, Han and Tian, Xin and Zhu, Danxiang and Tian, Hao and Wu, Hua},
+  journal={arXiv preprint arXiv:1904.09223},
+  year={2019}
+}
+```

model_cards/nghuyong/ernie-2.0-en/README.md

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+---
+language: en
+---
+
+# ERNIE-2.0
+
+## Introduction
+
+ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019, 
+which builds and learns incrementally pre-training tasks through constant multi-task learning. 
+Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese. 
+
+More detail: https://arxiv.org/abs/1907.12412
+
+## Released Model Info
+
+|Model Name|Language|Model Structure|
+|:---:|:---:|:---:|
+|ernie-2.0-en| English |Layer:12, Hidden:768, Heads:12|
+
+This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and 
+a series of experiments have been conducted to check the accuracy of the conversion.
+
+- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
+- Pytorch Conversion repo:  https://github.com/nghuyong/ERNIE-Pytorch
+
+## How to use
+```Python
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-2.0-en")
+model = AutoModel.from_pretrained("nghuyong/ernie-2.0-en")
+```
+
+## Citation
+
+```bibtex
+@article{sun2019ernie20,
+  title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
+  author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
+  journal={arXiv preprint arXiv:1907.12412},
+  year={2019} 
+}
+```

model_cards/nghuyong/ernie-2.0-large-en/README.md

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+# ERNIE-2.0-large
+
+## Introduction
+
+ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019, 
+which builds and learns incrementally pre-training tasks through constant multi-task learning. 
+Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese. 
+
+More detail: https://arxiv.org/abs/1907.12412
+
+## Released Model Info
+
+|Model Name|Language|Model Structure|
+|:---:|:---:|:---:|
+|ernie-2.0-large-en| English |Layer:24, Hidden:1024, Heads:16|
+
+This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and 
+a series of experiments have been conducted to check the accuracy of the conversion.
+
+- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
+- Pytorch Conversion repo:  https://github.com/nghuyong/ERNIE-Pytorch
+
+## How to use
+```Python
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-2.0-large-en")
+model = AutoModel.from_pretrained("nghuyong/ernie-2.0-large-en")
+```
+
+## Citation
+
+```bibtex
+@article{sun2019ernie20,
+  title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
+  author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
+  journal={arXiv preprint arXiv:1907.12412},
+  year={2019} 
+}
+```

model_cards/nghuyong/ernie-tiny/README.md

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+---
+language: en
+---
+
+# ERNIE-tiny
+
+## Introduction
+ERNIE-tiny is a compressed model from [ERNIE 2.0](../ernie-2.0-en) base model through model structure compression and model distillation.
+Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base, 
+but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times.
+
+More details: https://github.com/PaddlePaddle/ERNIE/blob/develop/distill/README.md
+
+## Released Model Info
+
+|Model Name|Language|Model Structure|
+|:---:|:---:|:---:|
+|ernie-tiny| English |Layer:3, Hidden:1024, Heads:16|
+
+This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and 
+a series of experiments have been conducted to check the accuracy of the conversion.
+
+- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
+- Pytorch Conversion repo:  https://github.com/nghuyong/ERNIE-Pytorch
+
+## How to use
+```Python
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-tiny")
+model = AutoModel.from_pretrained("nghuyong/ernie-tiny")
+```
+
+## Citation
+
+```bibtex
+@article{sun2019ernie20,
+  title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
+  author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
+  journal={arXiv preprint arXiv:1907.12412},
+  year={2019} 
+}
+```