mrm8488/bert-base-german-finetuned-ler

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

• Training script: Fine-tuning script for NER provided by Huggingface Colab: How to fine-tune a model for NER using HF scripts

• 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

• Annotation Guidelines (German)

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