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https://github.com/ShannonAI/mrc-for-flat-nested-ner

Code for ACL 2020 paper `A Unified MRC Framework for Named Entity Recognition`
https://github.com/ShannonAI/mrc-for-flat-nested-ner

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Code for ACL 2020 paper `A Unified MRC Framework for Named Entity Recognition`

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# A Unified MRC Framework for Named Entity Recognition 

The repository contains the code of the recent research advances in [Shannon.AI](http://www.shannonai.com). 



**A Unified MRC Framework for Named Entity Recognition** 


Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu and Jiwei Li


In ACL 2020. [paper](https://arxiv.org/abs/1910.11476)


If you find this repo helpful, please cite the following:

```latex

@article{li2019unified,

  title={A Unified MRC Framework for Named Entity Recognition},

  author={Li, Xiaoya and Feng, Jingrong and Meng, Yuxian and Han, Qinghong and Wu, Fei and Li, Jiwei},

  journal={arXiv preprint arXiv:1910.11476},

  year={2019}

}

```

For any question, please feel free to post Github issues. 



## Install Requirements



* The code requires Python 3.6+.



* If you are working on a GPU machine with CUDA 10.1, please run `pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch. If not, please see the [PyTorch Official Website](https://pytorch.org/) for instructions.



* Then run the following script to install the remaining dependenices: `pip install -r requirements.txt`



We build our project on [pytorch-lightning.](https://github.com/PyTorchLightning/pytorch-lightning)

If you want to know more about the arguments used in our training scripts, please 

refer to [pytorch-lightning documentation.](https://pytorch-lightning.readthedocs.io/en/latest/)



### Baseline: BERT-Tagger 



We release code, [scripts](./scripts/bert_tagger/reproduce) and [datafiles](./ner2mrc/download.md) for fine-tuning BERT and treating NER as a sequence labeling task. 



### MRC-NER: Prepare Datasets



You can [download](./ner2mrc/download.md) the preprocessed MRC-NER datasets used in our paper. 


For flat NER datasets, please use `ner2mrc/mrsa2mrc.py` to transform your BMES NER annotations to MRC-format. 


For nested NER datasets, please use `ner2mrc/genia2mrc.py` to transform your start-end NER annotations to MRC-format. 



### MRC-NER: Training



The main training procedure is in `train/mrc_ner_trainer.py`



Scripts for reproducing our experimental results can be found in the `./scripts/mrc_ner/reproduce/` folder. 

Note that you need to change `DATA_DIR`, `BERT_DIR`, `OUTPUT_DIR` to your own dataset path, bert model path and log path, respectively.  
 

For example, run `./scripts/mrc_ner/reproduce/ace04.sh` will start training MRC-NER models and save intermediate log to `$OUTPUT_DIR/train_log.txt`. 
 

During training, the model trainer will automatically evaluate on the dev set every `val_check_interval` epochs,

and save the topk checkpoints to `$OUTPUT_DIR`. 
 



### MRC-NER: Evaluation



After training, you can find the best checkpoint on the dev set according to the evaluation results in `$OUTPUT_DIR/train_log.txt`. 
 

Then run `python3 evaluate/mrc_ner_evaluate.py $OUTPUT_DIR/.ckpt  $OUTPUT_DIR/lightning_logs/` to evaluate on the test set with the best checkpoint chosen on dev. 



### MRC-NER: Inference 



Code for inference using the trained MRC-NER model can be found in `inference/mrc_ner_inference.py` file. 


For flat NER, we provide the inference script in [flat_inference.sh](./scripts/mrc_ner/flat_inference.sh) 


For nested NER, we provide the inference script in [nested_inference.sh](./scripts/mrc_ner/nested_inference.sh)