initial model commit

README.md

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+---
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+language: de
+datasets:
+- legal
+inference: false
+---
+
+## 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** (CoNLL-03 German revised)
+
+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/).