German BERT + LER (Legal Entity Recognition) ⚖️
German BERT (BERT-base-german-cased) fine-tuned on Legal-Entity-Recognition dataset for LER (NER) downstream task.
Details of the downstream task (NER) - Dataset
Legal-Entity-Recognition: Fine-grained Named Entity Recognition in Legal Documents.
Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).
| Split | # Samples |
|---|---|
| Train | 1657048 |
| Eval | 500000 |
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Training script: Fine-tuning script for NER provided by Huggingface Colab: How to fine-tune a model for NER using HF scripts
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Labels covered (and its distribution):
107 B-AN
918 B-EUN
2238 B-GRT
13282 B-GS
1113 B-INN
704 B-LD
151 B-LDS
2490 B-LIT
282 B-MRK
890 B-ORG
1374 B-PER
1480 B-RR
10046 B-RS
401 B-ST
68 B-STR
1011 B-UN
282 B-VO
391 B-VS
2648 B-VT
46 I-AN
6925 I-EUN
1957 I-GRT
70257 I-GS
2931 I-INN
153 I-LD
26 I-LDS
28881 I-LIT
383 I-MRK
1185 I-ORG
330 I-PER
106 I-RR
138938 I-RS
34 I-ST
55 I-STR
1259 I-UN
1572 I-VO
2488 I-VS
11121 I-VT
1348525 O
Metrics on evaluation set
| Metric | # score |
|---|---|
| F1 | 85.67 |
| Precision | 84.35 |
| Recall | 87.04 |
| Accuracy | 98.46 |
Model in action
Fast usage with pipelines:
from transformers import pipeline
nlp_ler = pipeline(
"ner",
model="mrm8488/bert-base-german-finetuned-ler",
tokenizer="mrm8488/bert-base-german-finetuned-ler"
)
text = "Your German legal text here"
nlp_ler(text)
Created by Manuel Romero/@mrm8488
Made with ♥ in Spain