ivnle/bi-lstm-crf

A PyTorch implementation of the BI-LSTM-CRF model.

A PyTorch implementation of the BI-LSTM-CRF model.

Features:

• Compared with PyTorch BI-LSTM-CRF tutorial, following improvements are performed:
  • Full support for mini-batch computation
  • Full vectorized implementation. Specially, removing all loops in "score sentence" algorithm, which dramatically improve training performance
  • CUDA supported
  • Very simple APIs for CRF module
    • START/STOP tags are automatically added in CRF
    • A inner Linear Layer is included which transform from feature space to tag space
• Specialized for NLP sequence tagging tasks
• Easy to train your own sequence tagging models
• MIT License

Installation

• dependencies
  • Python 3
  • PyTorch
• install

  $ pip install bi-lstm-crf

Training

corpus

• prepare your corpus in the specified structure and format
• there is also a sample corpus in bi_lstm_crf/app/sample_corpus

training

$ python -m bi_lstm_crf corpus_dir --model_dir "model_xxx"

• more options
• detail of model_dir

training curve

import pandas as pd
import matplotlib.pyplot as plt

# the training losses are saved in the model_dir
df = pd.read_csv(".../model_dir/loss.csv")
df[["train_loss", "val_loss"]].ffill().plot(grid=True)
plt.show()

Prediction

from bi_lstm_crf.app import WordsTagger

model = WordsTagger(model_dir="xxx")
tags, sequences = model(["市领导到成都..."])  # CHAR-based model
print(tags)  
# [["B", "B", "I", "B", "B-LOC", "I-LOC", "I-LOC", "I-LOC", "I-LOC", "B", "I", "B", "I"]]
print(sequences)
# [['市', '领导', '到', ('成都', 'LOC'), ...]]

# model([["市", "领导", "到", "成都", ...]])  # WORD-based model

CRF Module

The CRF module can be easily embeded into other models:

from bi_lstm_crf import CRF

# a BERT-CRF model for sequence tagging
class BertCrf(nn.Module):
    def __init__(self, ...):
        ...
        self.bert = BERT(...)
        self.crf = CRF(in_features, num_tags)

    def loss(self, xs, tags):
        features, = self.bert(xs)
        masks = xs.gt(0)
        loss = self.crf.loss(features, tags, masks)
        return loss

    def forward(self, xs):
        features, = self.bert(xs)
        masks = xs.gt(0)
        scores, tag_seq = self.crf(features, masks)
        return scores, tag_seq

Recommendation

• Neural Network: A very cool App for Deep Learning & PyTorch

References

1. Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv:1508.01991.
2. PyTorch tutorial ADVANCED: MAKING DYNAMIC DECISIONS AND THE BI-LSTM CRF