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ner's Introduction

NER

中文命名实体识别算法，欢迎star!

1、目录结构

  • data: 训练数据集
  • models: 构造的模型
  • results_save: 存放结果
  • conlleval_rev: perl脚本，计算模型性能用
  • data_helper: 数据处理
  • eval: 调用perl脚本计算性能指标
  • run.py: 执行程序
  • train_val_test.py: 训练、验证和测试
  • utils.py: 包含一些用到的功能

2、数据

3、运行

python3 run.py --mode xxx
xxx: train/test/demo，默认为demo

4、效果

模型 指标 准确率 精确率 召回率 F1值
BiLSTM+CRF 平均 98.57% 90.04% 87.39% 88.83%
LOC - 92.73% 90.44 91.57%
ORG - 87.54% 86.03% 86.78%
PER - 87.82% 84.68% 86.22%

BiLSTM+CRF测试:

5、参考

[1] https://github.com/Determined22/zh-NER-TF

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