readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: de
  datasets:
  - legal
  widget:
  - text: "Herr W. verstieß gegen § 36 Abs. 7 IfSG."
  ---
  
  ## NER for German Legal Text in Flair (default model)
  
  This is the legal NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **96,35** (LER German dataset)
  
  Predicts 19 tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  | AN   |  Anwalt   |
  | EUN   |  Europäische Norm   |
  | GS   |  Gesetz   |
  | GRT   | Gericht    |
  | INN   |  Institution   |
  | LD   |  Land   |
  | LDS   | Landschaft    |
  | LIT   | Literatur    |
  | MRK   |  Marke   |
  | ORG   |  Organisation   |
  | PER   |  Person   |
  | RR   |  Richter   |
  | RS   |  Rechtssprechung   |
  | ST   |  Stadt   |
  | STR   |  Straße   |
  | UN   |  Unternehmen   |
  | VO   |  Verordnung   |
  | VS   |  Vorschrift   |
  | VT   | Vertrag    |
  
  Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
  
  More details on the Legal NER dataset [here](https://github.com/elenanereiss/Legal-Entity-Recognition)
  
  ---
  
  ### Demo: How to use in Flair
  
  Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
  
  ```python
  from flair.data import Sentence
  from flair.models import SequenceTagger
  
  # load tagger
  tagger = SequenceTagger.load("flair/ner-german-legal")
  
  # make example sentence (don't use tokenizer since Rechtstexte are badly handled)
  sentence = Sentence("Herr W. verstieß gegen § 36 Abs. 7 IfSG.", use_tokenizer=False)
  
  
  # predict NER tags
  tagger.predict(sentence)
  
  # print sentence
  print(sentence)
  
  # print predicted NER spans
  print('The following NER tags are found:')
  # iterate over entities and print
  for entity in sentence.get_spans('ner'):
      print(entity)
  
  ```
  
  This yields the following output:
  ```
  Span [2]: "W."   [− Labels: PER (0.9911)]
  Span [5,6,7,8,9]: "§ 36 Abs. 7 IfSG."   [− Labels: GS (0.5353)]
  
  ```
  
  So, the entities "*W.*" (labeled as a **person**) and "*§ 36 Abs. 7 IfSG*" (labeled as a **Gesetz**) are found in the sentence "*Herr W. verstieß gegen § 36 Abs. 7 IfSG.*". 
  
  
  ---
  
  ### Training: Script to train this model
  
  The following Flair script was used to train this model: 
  
  ```python
  from flair.data import Corpus
  from flair.datasets import LER_GERMAN
  from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
  
  # 1. get the corpus
  corpus: Corpus = LER_GERMAN()
  
  # 2. what tag do we want to predict?
  tag_type = 'ner'
  
  # 3. make the tag dictionary from the corpus
  tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
  
  # 4. initialize each embedding we use
  embedding_types = [
  
      # GloVe embeddings
      WordEmbeddings('de'),
  
      # contextual string embeddings, forward
      FlairEmbeddings('de-forward'),
  
      # contextual string embeddings, backward
      FlairEmbeddings('de-backward'),
  ]
  
  # embedding stack consists of Flair and GloVe embeddings
  embeddings = StackedEmbeddings(embeddings=embedding_types)
  
  # 5. initialize sequence tagger
  from flair.models import SequenceTagger
  
  tagger = SequenceTagger(hidden_size=256,
                          embeddings=embeddings,
                          tag_dictionary=tag_dictionary,
                          tag_type=tag_type)
  
  # 6. initialize trainer
  from flair.trainers import ModelTrainer
  
  trainer = ModelTrainer(tagger, corpus)
  
  # 7. run training
  trainer.train('resources/taggers/ner-german-legal',
                train_with_dev=True,
                max_epochs=150)
  ```
  
  
  
  ---
  
  ### Cite
  
  Please cite the following papers when using this model.
  
  ```
  @inproceedings{leitner2019fine,
    author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
    title = {{Fine-grained Named Entity Recognition in Legal Documents}},
    booktitle = {Semantic Systems. The Power of AI and Knowledge
                    Graphs. Proceedings of the 15th International Conference
                    (SEMANTiCS 2019)},
    year = 2019,
    pages = {272--287},
    pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
  ```
  
  ```
  @inproceedings{akbik2018coling,
    title={Contextual String Embeddings for Sequence Labeling},
    author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
    booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
    pages     = {1638--1649},
    year      = {2018}
  }
  ```
  
  ---
  
  ### Issues?
  
  The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
  