LSTM-CRF in PyTorch

A PyTorch implementation of bidirectional LSTM-CRF for sequence tagging, adapted from the PyTorch tutorial.

Supported features:

• A PyTorch implementation of conditional random field (CRF)
• Vectorized computation of CRF loss
• Vectorized Viterbi decoding
• Mini-batch training with CUDA

Usage

Training data should be formatted as below:

token/tag token/tag token/tag ...
token/tag token/tag token/tag ...
...

To prepare data:

python prepare.py training_data

To train:

python train.py model word_to_idx tag_to_idx training_data.csv num_epoch

To predict:

python predict.py model.epochN word_to_idx tag_to_idx test_data

References

Zhiheng Huang, Wei Xu, Kai Yu. 2015. Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv:1508.01991.

Xuezhe Ma, Eduard Hovy. 2016. End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF. arXiv:1603.01354.