Create README.md

README.md

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+# ElasticBERT-LARGE
+
+## Model description
+
+This is an implementation of the `large` version of ElasticBERT.
+
+[**Towards Efficient NLP: A Standard Evaluation and A Strong Baseline**](https://arxiv.org/pdf/2110.07038.pdf)
+
+Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu
+
+## Code link
+
+[**fastnlp/elasticbert**](https://github.com/fastnlp/ElasticBERT)
+
+## Usage
+```python
+>>> from transformers import BertTokenizer as ElasticBertTokenizer
+>>> from models.configuration_elasticbert import ElasticBertConfig
+>>> from models.modeling_elasticbert import ElasticBertForSequenceClassification
+>>> num_output_layers = 1
+>>> config = ElasticBertConfig.from_pretrained('fnlp/elasticbert-large', num_output_layers=num_output_layers )
+>>> tokenizer = ElasticBertTokenizer.from_pretrained('fnlp/elasticbert-large')
+>>> model = ElasticBertForSequenceClassification.from_pretrained('fnlp/elasticbert-large', config=config)
+>>> input_ids = tokenizer.encode('The actors are fantastic .', return_tensors='pt')
+>>> outputs = model(input_ids)
+```
+
+## Citation
+
+```bibtex
+@article{liu2021elasticbert,
+  author    = {Xiangyang Liu and
+               Tianxiang Sun and
+               Junliang He and
+               Lingling Wu and
+               Xinyu Zhang and
+               Hao Jiang and
+               Zhao Cao and
+               Xuanjing Huang and
+               Xipeng Qiu},
+  title     = {Towards Efficient {NLP:} {A} Standard Evaluation and {A} Strong Baseline},
+  journal   = {CoRR},
+  volume    = {abs/2110.07038},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2110.07038},
+  eprinttype = {arXiv},
+  eprint    = {2110.07038},
+  timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2110-07038.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```