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GitHub - Kyubyong/bert_ner: Feature Based Ner with Bert

 6 years ago
source link: https://github.com/Kyubyong/bert_ner
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README.md

PyTorch Implementation of Feature Based NER with pretrained Bert

I know that you know BERT. In the great paper, the authors claim that the pretrained models do great in NER without fine-tuning. It's even impressive, allowing for the fact that they don't use any prediction-conditioned algorithms like CRF. We try to reproduce the result in a simple manner.

Requirements

  • python>=3.6 (Let's move on to python 3 if you still use python 2)
  • pytorch==1.0
  • pytorch_pretrained_bert==0.6.1
  • numpy>=1.15.4

Training & Evaluating

  • STEP 1. Run the command below to download conll 2003 NER dataset.
bash download.sh

It should be extracted to conll2003/ folder automatically.

  • STEP 2. Run the command below to train and evaluate.
python train.py

Results in the paper

bert_ner.png

Results

  • You can check the classification outputs in checkpoints.
epoch F1 score on conll2003 valid 1 0.2 2 0.75 3 0.84 4 0.88 5 0.89 6 0.90 7 0.90 8 0.91 9 0.91 10 0.92 11 0.92 12 0.93 13 0.93 14 0.93 15 0.93 16 0.92 17 0.93 18 0.93 19 0.93 20 0.93 21 0.94 22 0.94 23 0.93 24 0.93 25 0.93 26 0.93 27 0.93 28 0.93 29 0.94 30 0.93

report

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