Bilstm Crf Sequence Labeling Pytorch Save
Bi-LSTM+CRF sequence labeling model implemented in PyTorch
Project README
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.Dataloaderto 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
Open Source Agenda is not affiliated with "Bilstm Crf Sequence Labeling Pytorch" Project. README Source: SkyAndCloud/bilstm_crf_sequence_labeling_pytorch
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Last Commit
5 years ago
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