readme: ---
  language: de
  ---
  
  # German BERT + LER (Legal Entity Recognition) ⚖️
  
  German BERT ([BERT-base-german-cased](https://huggingface.co/bert-base-german-cased)) fine-tuned on [Legal-Entity-Recognition](https://github.com/elenanereiss/Legal-Entity-Recognition) dataset for **LER** (NER) downstream task.
  
  ## Details of the downstream task (NER) - Dataset
  
  [Legal-Entity-Recognition](https://github.com/elenanereiss/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](http://www.rechtsprechung-im-internet.de). 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](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py)
  Colab: [How to fine-tune a model for NER using HF scripts](https://colab.research.google.com/drive/156Qrd7NsUHwA3nmQ6gXdZY0NzOvqk9AT?usp=sharing)
  
  - 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)](https://github.com/elenanereiss/Legal-Entity-Recognition/blob/master/docs/Annotationsrichtlinien.pdf)
  
  
  ## 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**:
  
  ```python
  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](https://twitter.com/mrm8488)
  
  > Made with <span style="color: #e25555;">&hearts;</span> in Spain
  