❗ NOTE: I no longer maintain this repository. I recommend using AllenNLP instead.
A PyTorch implementation of a Bi-LSTM CRF with character-level features.
pytorch-crf is a flexible framework that makes it easy to reproduce several state-of-the-art sequence labelling deep neural networks that have proven to excel at the tasks of named entity recognition (NER) and part-of-speech (POS) tagging, among others. Some examples of the models you can reproduce with pytorch-crf are:
- the LSTM-CRF with LSTM-generated character-level features from Lample et. al., 2016.
- the CNN-LSTM-CRF with CNN-generated character-level features from Ma & Hovy, 2016.
First and foremost, you will need Python >= 3.6. The remaining requirements are listed in requirements.txt and can be installed with pip install -r requirements.txt.
In order to train a model, you will need a dataset and pretrained word embeddings. The dataset should be formatted like this:
EU B-ORG
rejects O
German B-MISC
call O
to O
boycott O
British B-MISC
lamb O
...
Each token is followed by a tab character and then the corresponding label. The end of a sentence is indicated by a blank line.
The pretrained word embeddings should be stored in text file like this:
the -0.038194 -0.24487 0.72812 -0.39961 ...
, -0.10767 0.11053 0.59812 -0.54361 ...
. -0.33979 0.20941 0.46348 -0.64792 ...
of -0.1529 -0.24279 0.89837 0.16996 ...
Each line contains a term in the vocabulary, followed by a space, and then the embedding coordinates (separated by spaces).
A model is trained using the pytorch-crf command-line interface, which is invoked with python -m pycrf.train. You can see all of the
available options with python -m pycrf.train --help.
- Chiu, J. P. C., and Nichols, E. 2016. Named entity recognition with bidirectional lstm-cnns. TACL
- Huang, Z., Xu, W., and Yu, K. 2015. Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991.
- Lample, G., Ballesteros, M., Kawakami, K., Subramanian, S., and Dyer, C. 2016. Neural architectures for named entity recognition. In NAACL-HLT.
- Ma, X., and Hovy, E. 2016. End-to-end sequence labeling via bi-directional lstm-cnns-crf. In ACL.