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
101 Dec 16, 2022
134 Dec 19, 2022
1.2k Dec 28, 2022
48 Dec 21, 2022
23 Oct 17, 2022
12 Nov 16, 2022
2.2k Dec 31, 2022
208 Dec 25, 2022
28 Jan 01, 2023
49 Dec 25, 2022
49 Oct 31, 2022
23 Aug 08, 2022
8 Mar 14, 2022
9 Nov 21, 2022
51 Nov 05, 2022
2.8k Dec 27, 2022
370 Dec 29, 2022
124 Dec 27, 2022
172 Dec 27, 2022
281 Dec 22, 2022