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main.py
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main.py
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from data import build_corpus
from utils import extend_maps, prepocess_data_for_lstmcrf
from evaluate import hmm_train_eval, crf_train_eval, \
bilstm_train_and_eval, ensemble_evaluate
def main():
"""训练模型,评估结果"""
# 读取数据
print("读取数据...")
train_word_lists, train_tag_lists, word2id, tag2id = \
build_corpus("train")
dev_word_lists, dev_tag_lists = build_corpus("dev", make_vocab=False)
test_word_lists, test_tag_lists = build_corpus("test", make_vocab=False)
# 训练评估hmm模型
print("正在训练评估HMM模型...")
hmm_pred = hmm_train_eval(
(train_word_lists, train_tag_lists),
(test_word_lists, test_tag_lists),
word2id,
tag2id
)
# 训练评估CRF模型
print("正在训练评估CRF模型...")
crf_pred = crf_train_eval(
(train_word_lists, train_tag_lists),
(test_word_lists, test_tag_lists)
)
# 训练评估BI-LSTM模型
print("正在训练评估双向LSTM模型...")
# LSTM模型训练的时候需要在word2id和tag2id加入PAD和UNK
bilstm_word2id, bilstm_tag2id = extend_maps(word2id, tag2id, for_crf=False)
lstm_pred = bilstm_train_and_eval(
(train_word_lists, train_tag_lists),
(dev_word_lists, dev_tag_lists),
(test_word_lists, test_tag_lists),
bilstm_word2id, bilstm_tag2id,
crf=False
)
print("正在训练评估Bi-LSTM+CRF模型...")
# 如果是加了CRF的lstm还要加入<start>和<end> (解码的时候需要用到)
crf_word2id, crf_tag2id = extend_maps(word2id, tag2id, for_crf=True)
# 还需要额外的一些数据处理
train_word_lists, train_tag_lists = prepocess_data_for_lstmcrf(
train_word_lists, train_tag_lists
)
dev_word_lists, dev_tag_lists = prepocess_data_for_lstmcrf(
dev_word_lists, dev_tag_lists
)
test_word_lists, test_tag_lists = prepocess_data_for_lstmcrf(
test_word_lists, test_tag_lists, test=True
)
lstmcrf_pred = bilstm_train_and_eval(
(train_word_lists, train_tag_lists),
(dev_word_lists, dev_tag_lists),
(test_word_lists, test_tag_lists),
crf_word2id, crf_tag2id
)
ensemble_evaluate(
[hmm_pred, crf_pred, lstm_pred, lstmcrf_pred],
test_tag_lists
)
if __name__ == "__main__":
main()