Create README.md (#7722)

model_cards/cooelf/limit-bert/README.md

+# LIMIT-BERT
+
+Code and model for the *EMNLP 2020 Findings* paper: 
+
+[LIMIT-BERT: Linguistic Informed Multi-task BERT](https://arxiv.org/abs/1910.14296)) 
+
+## Contents
+
+1. [Requirements](#Requirements)
+2. [Training](#Training)
+
+## Requirements
+
+* Python 3.6 or higher.
+* Cython 0.25.2 or any compatible version.
+* [PyTorch](http://pytorch.org/) 1.0.0+. 
+* [EVALB](http://nlp.cs.nyu.edu/evalb/). Before starting, run `make` inside the `EVALB/` directory to compile an `evalb` executable. This will be called from Python for evaluation. 
+* [pytorch-transformers](https://github.com/huggingface/pytorch-transformers) PyTorch 1.0.0+ or any compatible version.
+
+#### Pre-trained Models (PyTorch)
+The following pre-trained models are available for download from Google Drive:
+* [`LIMIT-BERT`](https://drive.google.com/open?id=1fm0cK2A91iLG3lCpwowCCQSALnWS2X4i): 
+  PyTorch version, same setting with BERT-Large-WWM，loading model with [pytorch-transformers](https://github.com/huggingface/pytorch-transformers).
+
+## How to use
+
+```
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("cooelf/limitbert")
+model = AutoModel.from_pretrained("cooelf/limitbert")
+```
+
+Please see our original repo for the training scripts.
+
+https://github.com/cooelf/LIMIT-BERT
+
+## Training
+
+To train LIMIT-BERT, simply run:
+```
+sh run_limitbert.sh
+```
+### Evaluation Instructions
+
+To test after setting model path:
+```
+sh test_bert.sh
+```
+
+## Citation
+
+```
+@article{zhou2019limit,
+  title={{LIMIT-BERT}: Linguistic informed multi-task {BERT}},
+  author={Zhou, Junru and Zhang, Zhuosheng and Zhao, Hai},
+  journal={arXiv preprint arXiv:1910.14296},
+  year={2019}
+}
+```
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