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language: de |
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# German BERT + LER (Legal Entity Recognition) ⚖️ |
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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. |
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## Details of the downstream task (NER) - Dataset |
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[Legal-Entity-Recognition](https://github.com/elenanereiss/Legal-Entity-Recognition): Fine-grained Named Entity Recognition in Legal Documents. |
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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). |
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| Split | # Samples | |
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| ---------------------- | ----- | |
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| Train | 1657048 | |
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| Eval | 500000 | |
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- Training script: [Fine-tuning script for NER provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py) |
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Colab: [How to fine-tune a model for NER using HF scripts](https://colab.research.google.com/drive/156Qrd7NsUHwA3nmQ6gXdZY0NzOvqk9AT?usp=sharing) |
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- Labels covered (and its distribution): |
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``` |
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107 B-AN |
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918 B-EUN |
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2238 B-GRT |
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13282 B-GS |
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1113 B-INN |
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704 B-LD |
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151 B-LDS |
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2490 B-LIT |
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282 B-MRK |
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890 B-ORG |
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1374 B-PER |
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1480 B-RR |
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10046 B-RS |
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401 B-ST |
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68 B-STR |
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1011 B-UN |
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282 B-VO |
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391 B-VS |
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2648 B-VT |
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46 I-AN |
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6925 I-EUN |
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1957 I-GRT |
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70257 I-GS |
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2931 I-INN |
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153 I-LD |
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26 I-LDS |
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28881 I-LIT |
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383 I-MRK |
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1185 I-ORG |
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330 I-PER |
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106 I-RR |
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138938 I-RS |
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34 I-ST |
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55 I-STR |
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1259 I-UN |
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1572 I-VO |
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2488 I-VS |
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11121 I-VT |
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1348525 O |
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``` |
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- [Annotation Guidelines (German)](https://github.com/elenanereiss/Legal-Entity-Recognition/blob/master/docs/Annotationsrichtlinien.pdf) |
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## Metrics on evaluation set |
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| Metric | # score | |
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| :------------------------------------------------------------------------------------: | :-------: | |
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| F1 | **85.67** |
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| Precision | **84.35** | |
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| Recall | **87.04** | |
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| Accuracy | **98.46** | |
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## Model in action |
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Fast usage with **pipelines**: |
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```python |
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from transformers import pipeline |
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nlp_ler = pipeline( |
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"ner", |
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model="mrm8488/bert-base-german-finetuned-ler", |
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tokenizer="mrm8488/bert-base-german-finetuned-ler" |
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) |
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text = "Your German legal text here" |
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nlp_ler(text) |
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``` |
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) |
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> Made with <span style="color: #e25555;">♥</span> in Spain |
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