
# 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

{"language": "de"}
transformers pytorch jax safetensors bert token-classification de autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us 

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).



* 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):
* Annotation Guidelines (German)


Metrics on evaluation set
-------------------------



Model in action
---------------


Fast usage with pipelines:



> 
> Created by Manuel Romero/@mrm8488
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
> 



TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us 
