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All Projects → threelittlemonkeys → Lstm Crf Pytorch

threelittlemonkeys / Lstm Crf Pytorch

LSTM-CRF in PyTorch

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python
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pytorchcrfsequence-labeling

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LSTM-CRF in PyTorch

A minimal PyTorch (1.7.1) implementation of bidirectional LSTM-CRF for sequence labelling.

Supported features:

  • Mini-batch training with CUDA
  • Lookup, CNNs, RNNs and/or self-attention in the embedding layer
  • Hierarchical recurrent encoding (HRE)
  • A PyTorch implementation of conditional random field (CRF)
  • Vectorized computation of CRF loss
  • Vectorized Viterbi decoding

Usage

Training data should be formatted as below:

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

For more detail, see README.md in each subdirectory.

To prepare data:

python3 prepare.py training_data

To train:

python3 train.py model char_to_idx word_to_idx tag_to_idx training_data.csv (validation_data) num_epoch

To predict:

python3 predict.py model.epochN word_to_idx tag_to_idx test_data

To evaluate:

python3 evaluate.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.

Harshit Kumar, Arvind Agarwal, Riddhiman Dasgupta, Sachindra Joshi. 2018. Dialogue Act Sequence Labeling Using Hierarchical Encoder with CRF. In AAAI.

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

Shotaro Misawa, Motoki Taniguchi, Yasuhide Miura, Tomoko Ohkuma. 2017. Character-based Bidirectional LSTM-CRF with Words and Characters for Japanese Named Entity Recognition. In Proceedings of the 1st Workshop on Subword and Character Level Models in NLP.

Yan Shao, Christian Hardmeier, Jörg Tiedemann, Joakim Nivre. 2017. Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF. arXiv:1704.01314.

Slav Petrov, Dipanjan Das, Ryan McDonald. 2011. A Universal Part-of-Speech Tagset. arXiv:1104.2086.

Nils Reimers, Iryna Gurevych. 2017. Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks. arXiv:1707.06799.

Feifei Zhai, Saloni Potdar, Bing Xiang, Bowen Zhou. 2017. Neural Models for Sequence Chunking. In AAAI.

Zenan Zhai, Dat Quoc Nguyen, Karin Verspoor. 2018. Comparing CNN and LSTM Character-level Embeddings in BiLSTM-CRF Models for Chemical and Disease Named Entity Recognition. arXiv:1808.08450.

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