本项目使用Keras实现R-BERT，在人物关系数据集上进行测试验证。
- 共3901条标注样本，训练集：测试集=8:2
- 标注样本：
亲戚 1837年6月20日，<e1>威廉四世</e1>辞世，他的侄女<e2>维多利亚</e2>即位。，其中亲戚为关系，威廉四世为实体1（entity_1），维多利亚为实体2（entity_2）。 - 每一种关系的标注数量如下图:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 200) 0
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 200) 0
__________________________________________________________________________________________________
model_2 (Model) multiple 101677056 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
input_3 (InputLayer) (None, 200) 0
__________________________________________________________________________________________________
input_4 (InputLayer) (None, 200) 0
__________________________________________________________________________________________________
lambda_1 (Lambda) (None, 768) 0 model_2[1][0]
__________________________________________________________________________________________________
dot_1 (Dot) (None, 768) 0 input_3[0][0]
model_2[1][0]
__________________________________________________________________________________________________
dot_2 (Dot) (None, 768) 0 input_4[0][0]
model_2[1][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 768) 0 lambda_1[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 768) 0 dot_1[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 768) 0 dot_2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 768) 590592 dropout_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 768) 590592 dropout_2[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 768) 590592 dropout_3[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 2304) 0 dense_1[0][0]
dense_2[0][0]
dense_3[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 14) 32270 concatenate_1[0][0]
==================================================================================================
Total params: 103,481,102
Trainable params: 103,481,102
Non-trainable params: 0
__________________________________________________________________________________________________
- python >= 3.6
- 第三方模块参考:
requiments.txt
$ python3 model_train.py$ python3 model_evalaute.py
# Model: chinese_L-12_H-768_A-12, weighted avgage F1 = 82.74%
# Model: chinese-RoBERTa-wwm-ext, weighted avgage F1 = 85.27%
# Model: chinese-RoBERTa-wwm-ext-large, weighted avgage F1 = 85.74%Model: chinese_L-12_H-768_A-12, 详细的评估结果如下：
precision recall f1-score support
unknown 0.8216 0.7273 0.7716 209
上下级 0.6047 0.8387 0.7027 31
亲戚 0.8889 0.6667 0.7619 24
兄弟姐妹 0.7692 0.8824 0.8219 34
合作 0.8276 0.8136 0.8205 59
同人 1.0000 0.9231 0.9600 39
同学 0.9524 0.8333 0.8889 24
同门 1.0000 0.8846 0.9388 26
夫妻 0.7816 0.8608 0.8193 79
好友 0.8276 0.8000 0.8136 30
师生 0.7561 0.8378 0.7949 37
情侣 0.7941 0.8710 0.8308 31
父母 0.8582 0.9453 0.8996 128
祖孙 0.9524 0.8000 0.8696 25
accuracy 0.8273 776
macro avg 0.8453 0.8346 0.8353 776
weighted avg 0.8344 0.8273 0.8274 776
Model: chinese-RoBERTa-wwm-ext, 详细的评估结果如下：
precision recall f1-score support
unknown 0.7930 0.8612 0.8257 209
上下级 0.7188 0.7419 0.7302 31
亲戚 0.8824 0.6250 0.7317 24
兄弟姐妹 0.8378 0.9118 0.8732 34
合作 0.8600 0.7288 0.7890 59
同人 1.0000 0.9487 0.9737 39
同学 0.8800 0.9167 0.8980 24
同门 0.9615 0.9615 0.9615 26
夫妻 0.8333 0.8861 0.8589 79
好友 0.8065 0.8333 0.8197 30
师生 0.8857 0.8378 0.8611 37
情侣 0.9231 0.7742 0.8421 31
父母 0.9062 0.9062 0.9062 128
祖孙 0.9524 0.8000 0.8696 25
accuracy 0.8531 776
macro avg 0.8743 0.8381 0.8529 776
weighted avg 0.8566 0.8531 0.8527 776
Model: chinese-RoBERTa-wwm-ext-large, 详细的评估结果如下：
precision recall f1-score support
unknown 0.8269 0.8230 0.8249 209
上下级 0.7500 0.6774 0.7119 31
亲戚 0.8824 0.6250 0.7317 24
兄弟姐妹 0.7805 0.9412 0.8533 34
合作 0.8545 0.7966 0.8246 59
同人 0.9487 0.9487 0.9487 39
同学 1.0000 0.9167 0.9565 24
同门 0.9615 0.9615 0.9615 26
夫妻 0.8182 0.9114 0.8623 79
好友 0.7812 0.8333 0.8065 30
师生 0.8286 0.7838 0.8056 37
情侣 0.8788 0.9355 0.9062 31
父母 0.9291 0.9219 0.9255 128
祖孙 0.8800 0.8800 0.8800 25
accuracy 0.8582 776
macro avg 0.8657 0.8540 0.8571 776
weighted avg 0.8597 0.8582 0.8574 776
$ python3 model_predict.py