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BiLSTM-CRF

A pytorch implementation of BiLSTM-CRF model for Chinese/English NER task.

Requirements

  • python = 3.6
  • pytorch = 1.0.0
  • pytorch-crf = 0.7.2
  • seqeval = 0.0.12

Dataset

First prepare the following directories for each dataset,

  • data/[dataset-name]/raw
  • data/[dataset-name]/processed

then place train.txt, dev.txt and test.txt into the raw folder, note data is supposed to be organized as that one word/tag one line, sentences are sepreated by blankline.

For Chinese NER dataset, you can access MSRA from https://github.com/GeneZC/Chinese-NER/tree/master/data
For English NER dataset, you can access CoNLL03 from https://github.com/davidsbatista/NER-datasets/tree/master/CONLL2003

Preprocess

Run preprosess.py to transform the raw data into processed one, which includes transformed dataset and vocabularies for words and tags, and the processed data will be placed in data/[dataset-name]/processed folder, for example:

python preprocess.py --dataset="dataset-name"

Train

Run python train.py --help to get some training settings, during training, model performances on dev and test dataset is printed every epoch, including Precision, Recall and F1. Besides a model checkpoint file will be saved at the end of every epoch.

Training process will run on GPU by default.

Example:

python train.py --name="name-of-train" --dataset="dataset-name"

Tagging

Tagging using trained model.

Example:

python tagging.py --sentence="中国同加利福尼亚州的友好交往源远流长" \
    --model="checkpoints/name-of-train/model-epochX.pt"

Output:

B-LOC I-LOC O B-LOC I-LOC I-LOC I-LOC I-LOC I-LOC O O O O O O O O O