bert-german-ler

模型描述

这个模型是在 German LER Dataset 数据集上，基于 bert-base-german-cased 进行微调得到的。

数据集中各类的分布情况：

Fine-grained classes	#	%
1	PER	Person	1,747	3.26
2	RR	Judge	1,519	2.83
3	AN	Lawyer	111	0.21
4	LD	Country	1,429	2.66
5	ST	City	705	1.31
6	STR	Street	136	0.25
7	LDS	Landscape	198	0.37
8	ORG	Organization	1,166	2.17
9	UN	Company	1,058	1.97
10	INN	Institution	2,196	4.09
11	GRT	Court	3,212	5.99
12	MRK	Brand	283	0.53
13	GS	Law	18,52	34.53
14	VO	Ordinance	797	1.49
15	EUN	European legal norm	1,499	2.79
16	VS	Regulation	607	1.13
17	VT	Contract	2,863	5.34
18	RS	Court decision	12,58	23.46
19	LIT	Legal literature	3,006	5.60
Total	53,632	100

如何在德语LER数据集上进行另一个模型的微调，请参考 GitHub 。

训练过程

训练超参数

训练过程中使用了以下超参数：

learning_rate: 1e-05
train_batch_size: 12
eval_batch_size: 16
max_seq_length: 512
num_epochs: 3

结果

开发集上的结果：

              precision    recall  f1-score   support

          AN       0.75      0.50      0.60        12
         EUN       0.92      0.93      0.92       116
         GRT       0.95      0.99      0.97       331
          GS       0.98      0.98      0.98      1720
         INN       0.84      0.91      0.88       199
          LD       0.95      0.95      0.95       109
         LDS       0.82      0.43      0.56        21
         LIT       0.88      0.92      0.90       231
         MRK       0.50      0.70      0.58        23
         ORG       0.64      0.71      0.67       103
         PER       0.86      0.93      0.90       186
          RR       0.97      0.98      0.97       144
          RS       0.94      0.95      0.94      1126
          ST       0.91      0.88      0.89        58
         STR       0.29      0.29      0.29         7
          UN       0.81      0.85      0.83       143
          VO       0.76      0.95      0.84        37
          VS       0.62      0.80      0.70        56
          VT       0.87      0.92      0.90       275

   micro avg       0.92      0.94      0.93      4897
   macro avg       0.80      0.82      0.80      4897
weighted avg       0.92      0.94      0.93      4897

测试集上的结果：

              precision    recall  f1-score   support

          AN       1.00      0.89      0.94         9
         EUN       0.90      0.97      0.93       150
         GRT       0.98      0.98      0.98       321
          GS       0.98      0.99      0.98      1818
         INN       0.90      0.95      0.92       222
          LD       0.97      0.92      0.94       149
         LDS       0.91      0.45      0.61        22
         LIT       0.92      0.96      0.94       314
         MRK       0.78      0.88      0.82        32
         ORG       0.82      0.88      0.85       113
         PER       0.92      0.88      0.90       173
          RR       0.95      0.99      0.97       142
          RS       0.97      0.98      0.97      1245
          ST       0.79      0.86      0.82        64
         STR       0.75      0.80      0.77        15
          UN       0.90      0.95      0.93       108
          VO       0.80      0.83      0.81        71
          VS       0.73      0.84      0.78        64
          VT       0.93      0.97      0.95       290

   micro avg       0.94      0.96      0.95      5322
   macro avg       0.89      0.89      0.89      5322
weighted avg       0.95      0.96      0.95      5322

参考资料

@misc{https://doi.org/10.48550/arxiv.2003.13016,
  doi = {10.48550/ARXIV.2003.13016},
  url = {https://arxiv.org/abs/2003.13016},  
  author = {Leitner, Elena and Rehm, Georg and Moreno-Schneider, Julián},  
  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},  
  title = {A Dataset of German Legal Documents for Named Entity Recognition},  
  publisher = {arXiv},  
  year = {2020},  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

作者:

Elena Leitner

数据集大小:

828.54 MB