本项目采用Keras和Keras-bert实现文本多标签分类任务。
- jclian91
本项目以 2020语言与智能技术竞赛：事件抽取任务 中的数据作为多分类标签的样例数据，借助多标签分类模型来解决。
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├── chinese_L-12_H-768_A-12（BERT中文预训练模型）
│ ├── bert_config.json
│ ├── bert_model.ckpt.data-00000-of-00001
│ ├── bert_model.ckpt.index
│ ├── bert_model.ckpt.meta
│ └── vocab.txt
├── data（数据集）
├── label.json（类别词典，生成文件）
├── model_evaluate.py（模型评估脚本）
├── model_predict.py（模型预测脚本）
├── model_train.py（模型训练脚本）
└── requirements.txt
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, None) 0
__________________________________________________________________________________________________
input_2 (InputLayer) (None, None) 0
__________________________________________________________________________________________________
model_2 (Model) (None, None, 768) 101677056 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
lambda_1 (Lambda) (None, 768) 0 model_2[1][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 65) 49985 lambda_1[0][0]
==================================================================================================
Total params: 101,727,041
Trainable params: 101,727,041
Non-trainable params: 0
__________________________________________________________________________________________________
- chinese_L-12_H-768_A-12
模型参数: batch_size = 8, maxlen = 256, epoch=10
使用chinese_L-12_H-768_A-12预训练模型，评估结果如下:
precision recall f1-score support
0 1.0000 0.8947 0.9444 19
1 0.8750 1.0000 0.9333 21
2 0.9091 1.0000 0.9524 10
3 1.0000 0.8537 0.9211 41
4 0.9462 1.0000 0.9724 88
5 1.0000 1.0000 1.0000 8
6 0.9726 1.0000 0.9861 71
7 1.0000 1.0000 1.0000 7
8 0.8761 0.9340 0.9041 106
9 1.0000 0.9000 0.9474 10
10 0.9333 1.0000 0.9655 14
11 1.0000 1.0000 1.0000 8
12 0.8333 1.0000 0.9091 5
13 0.8636 1.0000 0.9268 19
14 1.0000 1.0000 1.0000 36
15 0.8438 1.0000 0.9153 27
16 0.9697 1.0000 0.9846 32
17 1.0000 1.0000 1.0000 12
18 1.0000 1.0000 1.0000 29
19 0.8824 0.8571 0.8696 35
20 0.9091 0.9091 0.9091 11
21 1.0000 1.0000 1.0000 4
22 0.6667 0.8000 0.7273 5
23 1.0000 0.8889 0.9412 9
24 0.8571 0.6667 0.7500 9
25 0.9737 0.9867 0.9801 150
26 1.0000 1.0000 1.0000 12
27 0.9394 0.9688 0.9538 32
28 0.8462 1.0000 0.9167 11
29 0.8387 0.9286 0.8814 56
30 0.9487 0.8605 0.9024 43
31 0.9231 0.8000 0.8571 15
32 1.0000 1.0000 1.0000 24
33 1.0000 0.6250 0.7692 8
34 1.0000 0.9375 0.9677 16
35 0.8421 0.9697 0.9014 33
36 0.8500 0.7727 0.8095 22
37 0.9231 1.0000 0.9600 24
38 0.9591 0.9906 0.9746 213
39 0.9118 0.8857 0.8986 35
40 1.0000 1.0000 1.0000 7
41 1.0000 0.9000 0.9474 10
42 0.8333 0.7692 0.8000 13
43 0.8947 0.9444 0.9189 18
44 0.9167 1.0000 0.9565 11
45 1.0000 1.0000 1.0000 14
46 0.9000 1.0000 0.9474 9
47 1.0000 1.0000 1.0000 9
48 0.7895 0.8333 0.8108 18
49 0.9706 1.0000 0.9851 33
50 1.0000 1.0000 1.0000 9
51 1.0000 1.0000 1.0000 3
52 0.9286 1.0000 0.9630 13
53 1.0000 1.0000 1.0000 14
54 0.8571 1.0000 0.9231 6
55 1.0000 1.0000 1.0000 15
56 0.8421 1.0000 0.9143 16
57 1.0000 1.0000 1.0000 14
58 0.9000 1.0000 0.9474 9
59 1.0000 1.0000 1.0000 27
60 0.8824 0.9375 0.9091 16
61 0.7778 1.0000 0.8750 14
62 1.0000 0.7500 0.8571 4
63 1.0000 0.9375 0.9677 16
64 0.9000 1.0000 0.9474 9
micro avg 0.9341 0.9578 0.9458 1657
macro avg 0.9336 0.9462 0.9370 1657
weighted avg 0.9367 0.9578 0.9456 1657
samples avg 0.9520 0.9672 0.9531 1657
accuracy: 0.8985313751668892
hamming loss: 0.001869158878504673
- chinese_wwm_ext_L-12_H-768_A-12
模型参数: batch_size = 8, maxlen = 256, epoch=10
使用chinese_wwm_ext_L-12_H-768_A-12预训练模型，评估结果如下：
micro avg 0.9403 0.9608 0.9504 1657
macro avg 0.9322 0.9542 0.9404 1657
weighted avg 0.9446 0.9608 0.9510 1657
samples avg 0.9598 0.9713 0.9595 1657
accuracy: 0.9092122830440588
hamming loss: 0.0017048372188559105
- chinese_roberta_wwm_large_ext_L-24_H-1024_A-16
模型参数: batch_size = 2, maxlen = 256, epoch=10
使用chinese_roberta_wwm_large_ext_L-24_H-1024_A-16预训练模型，评估结果如下：
micro avg 0.9385 0.9584 0.9483 1657
macro avg 0.9372 0.9482 0.9383 1657
weighted avg 0.9428 0.9584 0.9488 1657
samples avg 0.9581 0.9700 0.9579 1657
accuracy: 0.9065420560747663
hamming loss: 0.001776727944952244
- chinese_L-12_H-768_A-12
模型参数: batch_size = 8, maxlen = 256, epoch=10
micro avg 0.9516 0.9608 0.9562 1657
macro avg 0.9427 0.9491 0.9440 1657
weighted avg 0.9533 0.9608 0.9562 1657
samples avg 0.9654 0.9725 0.9632 1657
accuracy: 0.9158878504672897
hamming loss: 0.0014994351442949574
micro avg 0.9518 0.9644 0.9580 1657
macro avg 0.9448 0.9512 0.9463 1657
weighted avg 0.9539 0.9644 0.9582 1657
samples avg 0.9622 0.9727 0.9623 1657
accuracy: 0.9192256341789052
hamming loss: 0.0014378145219266715
micro avg 0.9482 0.9614 0.9547 1657
macro avg 0.9419 0.9422 0.9384 1657
weighted avg 0.9506 0.9614 0.9546 1657
samples avg 0.9626 0.9713 0.9617 1657
accuracy: 0.9185580774365821
hamming loss: 0.0015507856629351957
- 将BERT中文预训练模型chinese_L-12_H-768_A-12放在chinese_L-12_H-768_A-12文件夹下
- 所需Python第三方模块参考requirements.txt文档
- 自己需要分类的数据按照data/train.py的格式准备好
- 调整模型参数，运行model_train.py进行模型训练
- 运行model_evaluate.py进行模型评估
- 运行model_predict.py对新文本进行评估
执行的情况： 模型训练：一天多 执行预测：3秒1条