Chinese named entity recognization (Bilstm with Keras)
Project Structure
./
├── README.md
├── data
│ ├── README.md
│ ├── data 数据集
│ │ ├── test.txt
│ │ └── train.txt
│ ├── plain_text.txt
│ └── vocab.txt 词表
├── evaluate
│ ├── __init__.py
│ └── f1_score.py 计算实体F1得分
├── keras_contrib keras_contrib包，也可以pip装
├── log 训练nohup日志
│ ├── __init__.py
│ └── nohup.out
├── model 模型
│ ├── BiLSTMCRF.py
│ ├── __init__.py
│ └── __pycache__
├── predict 输出预测
│ ├── __init__.py
│ ├── __pycache__
│ ├── predict.py
│ └── predict_process.py
├── preprocess 数据预处理
│ ├── README.md
│ ├── __pycache__
│ ├── convert_jsonl.py
│ ├── data_add_line.py
│ ├── generate_vocab.py 生成词表
│ ├── process_data.py 数据处理转换
│ ├── splite.py
│ └── vocab.py 词表对应工具
├── public
│ ├── __init__.py
│ ├── __pycache__
│ ├── config.py 训练设置
│ ├── generate_label_id.py 生成label2id文件
│ ├── label2id.json 标签dict
│ ├── path.py 所有路径
│ └── utils.py 小工具
├── report
│ └── report.out F1评估报告
├── train.py
└── weight 保存的权重
└── bilstm_ner.h5
52 directories, 214 files
Dataset
三甲医院肺结节数据集，20000+字，BIO格式，形如：
中 B-ORG
共 I-ORG
中 I-ORG
央 I-ORG
致 O
中 B-ORG
国 I-ORG
致 I-ORG
公 I-ORG
党 I-ORG
十 I-ORG
一 I-ORG
大 I-ORG
的 O
贺 O
词 O
ATTENTION: 在处理自己数据集的时候需要注意：
- 字与标签之间用tab（"\t"）隔开
- 其中句子与句子之间使用空行隔开
Steps
- 替换数据集
- 修改public/path.py中的地址
- 使用public/generate_label_id.py生成label2id.txt文件，将其中的内容填到preprocess/vocab.py的get_tag2index中。注意：序号必须从0开始
- 修改public/config.py中的MAX_LEN（超过截断，少于填充，最好设置训练集、测试集中最长句子作为MAX_LEN）
- 运行preprocess/generate_vocab.py生成词表，词表按词频生成
- 根据需要修改BiLSTMCRF.py模型结构
- 修改public/config.py的参数
- 训练前debug看下train_data,train_label对不对
- 训练
Model
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, None) 0
_________________________________________________________________
embedding_1 (Embedding) (None, None, 128) 81408
_________________________________________________________________
bidirectional_1 (Bidirection (None, None, 256) 263168
_________________________________________________________________
dropout_1 (Dropout) (None, None, 256) 0
_________________________________________________________________
bidirectional_2 (Bidirection (None, None, 128) 164352
_________________________________________________________________
dropout_2 (Dropout) (None, None, 128) 0
_________________________________________________________________
time_distributed_1 (TimeDist (None, None, 29) 3741
_________________________________________________________________
dropout_3 (Dropout) (None, None, 29) 0
_________________________________________________________________
crf_1 (CRF) (None, None, 29) 1769
=================================================================
Total params: 514,438
Trainable params: 514,438
Non-trainable params: 0
_________________________________________________________________
Train
运行train.py
Epoch 1/500
806/806 [==============================] - 15s 18ms/step - loss: 2.4178 - crf_viterbi_accuracy: 0.9106
Epoch 00001: loss improved from inf to 2.41777, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 2/500
806/806 [==============================] - 10s 13ms/step - loss: 0.6370 - crf_viterbi_accuracy: 0.9106
Epoch 00002: loss improved from 2.41777 to 0.63703, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 3/500
806/806 [==============================] - 11s 14ms/step - loss: 0.5295 - crf_viterbi_accuracy: 0.9106
Epoch 00003: loss improved from 0.63703 to 0.52950, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 4/500
806/806 [==============================] - 11s 13ms/step - loss: 0.4184 - crf_viterbi_accuracy: 0.9064
Epoch 00004: loss improved from 0.52950 to 0.41838, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 5/500
806/806 [==============================] - 12s 14ms/step - loss: 0.3422 - crf_viterbi_accuracy: 0.9104
Epoch 00005: loss improved from 0.41838 to 0.34217, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 6/500
806/806 [==============================] - 10s 13ms/step - loss: 0.3164 - crf_viterbi_accuracy: 0.9106
Epoch 00006: loss improved from 0.34217 to 0.31637, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 7/500
806/806 [==============================] - 10s 12ms/step - loss: 0.3003 - crf_viterbi_accuracy: 0.9111
Epoch 00007: loss improved from 0.31637 to 0.30032, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 8/500
806/806 [==============================] - 10s 12ms/step - loss: 0.2906 - crf_viterbi_accuracy: 0.9117
Epoch 00008: loss improved from 0.30032 to 0.29058, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 9/500
806/806 [==============================] - 9s 12ms/step - loss: 0.2837 - crf_viterbi_accuracy: 0.9118
Epoch 00009: loss improved from 0.29058 to 0.28366, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 10/500
806/806 [==============================] - 9s 11ms/step - loss: 0.2770 - crf_viterbi_accuracy: 0.9142
Epoch 00010: loss improved from 0.28366 to 0.27696, saving model to /home/bureaux/Projects/BiLSTMCRF_TimeDistribute/weight/bilstm_ner.h5
Epoch 11/500
806/806 [==============================] - 10s 12ms/step - loss: 0.2713 - crf_viterbi_accuracy: 0.9160
Evaluate
运行evaluate/f1_score.py
100%|█████████████████████████████████████████| 118/118 [00:38<00:00, 3.06it/s]
TP: 441
TP+FP: 621
precision: 0.7101449275362319
TP+FN: 604
recall: 0.7301324503311258
f1: 0.72
classification report:
precision recall f1-score support
ANATOMY 0.74 0.75 0.74 220
BOUNDARY 1.00 0.75 0.86 8
DENSITY 0.78 0.88 0.82 8
DIAMETER 0.82 0.88 0.85 16
DISEASE 0.54 0.72 0.62 43
LUNGFIELD 0.83 0.83 0.83 6
MARGIN 0.57 0.67 0.62 6
NATURE 0.00 0.00 0.00 6
ORGAN 0.62 0.62 0.62 13
QUANTITY 0.88 0.87 0.87 83
SHAPE 1.00 0.43 0.60 7
SIGN 0.66 0.65 0.65 189
TEXTURE 0.75 0.43 0.55 7
TREATMENT 0.25 0.33 0.29 9
micro avg 0.71 0.71 0.71 621
macro avg 0.67 0.63 0.64 621
weighted avg 0.71 0.71 0.71 621
Predict
运行predict/predict_bio.py
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