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Bi-LSTM+CRF Sequence Labeling

This project applied bi-lstm+crf model into named entity recognition task, which belongs to sequence labeling.

Requirements

  • Python3.5+
  • PyTorch 0.4+
  • numpy
  • tensorboardX

Features

  • utilize torch.utils.data.Dataset, torch.utils.data.Dataloader to load corpus

  • conditional random field module inside

  • evaluate f1 on entity

  • support both cpu & gpu

  • user-friendly tensorboard visualization

  • abundant arguments for tuning parameters

  • nice code style & elaborate comments

Usage

Dataset format

for each line

token1 token2 token3 token4, tag1 tag2 tag3 tag4

please refer to test.csv for more detail.

Run on GPU X

export CUDA_VISIBLE_DEVICES=X
python main.py --model-name MODEL_NAME --vocab token2idx.json tag2idx.json

Run on CPU

python main.py --model-name MODEL_NAME --vocab token2idx.json tag2idx.json --no-cuda

Benchmark

config f1 precision recall
baseline (default config) 0.8974 0.9217 0.8743
lr=0.0005 0.8667 0.9 0.8357
embedding-size=256,hidden-size=512 0.8871 0.8929 0.8814
embedding-size=128,hidden-size=256 0.8895 0.9115 0.8686
dropout=0.1 0.9134 0.9396 0.8886
dropout=0.3 0.8975 0.9419 0.8571
dropout=0.5 0.9120 0.9367 0.8886
rnn-layer=2 0.9131 0.9342 0.8929
with-layer-norm=True 0.8943 0.9265 0.8643

TODO

  • support customized dataset
  • support multi-gpu training
  • refine viterbi decoding efficiency
  • extract evaluate mode

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Bi-LSTM+CRF sequence labeling model implemented in PyTorch

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