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DAP project, PyTorch, LSTM model

This is for Yuan's Data Analysis Project. I want to implment LSTM-CRF autoencoder in PyTorch.

The data pre-processing part is done.

LSTM in PyTorch training part is done.

Evaluation part is done.

The CRF-LSTM model part is done.

Test this model on CONLL2000, the result shown in below:

Pre-Training part is done. The pre-training dataset can be downloaded from https://nlp.stanford.edu/projects/glove/

The accuracy after pre-training is 92.88%, before pre-training is 90.96%

The marginal decode is done.

The maximum labelwise accuracy part is done.

Split LSTMCRF into two models. Add CRF module.

Add some hand engineer.

The none-pretrain SGD F1 score ~ 88.5

The pretrain SGD F1 score ~ 91.2