This is a more than 1000X faster LSTM-CRF implementation modified from the slower version in the Pytorch official tutorial (URL:https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html).

I have modified the dynamic planning part, including Viterbi decoding and partition function calculation. In the experiment, it has achieved a speed increase of more than 50 times compared to the original version. Furthermore, the original version can only input one sample at a time when it is trained.

In the most recent updated module 'LSTM_CRF_faster_parallel.py', I modified the model to support parallel computing for batch, so that the training time was greatly improved again. When the batchsize is large, parallel computing can bring you hundreds of times faster.

The code defaults to training word embedding from scratch. If you need to use pre-training word embedding, or take another model's outputs as it's inputs, you need to make some changes to the code, directly take the embedding as inpus of method '_get_lstm_features_parallel'.

Moreover, the code runs on the CPU by default, and if you need to experience faster speed, you need to manually deploy the model to the GPU.

(In previous version, parallel version 'LSTM_CRF_faster_parallel.py' is not work, now the bug is rectified. For more questions please send email to [email protected])