readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: de
  datasets:
  - conll2003
  widget:
  - text: "George Washington ging nach Washington"
  ---
  
  ## German NER in Flair (large model)
  
  This is the large 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **92,31** (CoNLL-03 German revised)
  
  Predicts 4 tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  | PER         | person name | 
  | LOC         | location name | 
  | ORG         | organization name | 
  | MISC         | other name | 
  
  Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf).
  
  ---
  
  ### 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-large")
  
  # make example sentence
  sentence = Sentence("George Washington ging nach Washington")
  
  # 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 [1,2]: "George Washington"   [− Labels: PER (1.0)]
  Span [5]: "Washington"   [− Labels: LOC (1.0)]
  ```
  
  So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*". 
  
  
  ---
  
  ### Training: Script to train this model
  
  The following Flair script was used to train this model: 
  
  ```python
  import torch
  
  # 1. get the corpus
  from flair.datasets import CONLL_03_GERMAN
  
  corpus = CONLL_03_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 fine-tuneable transformer embeddings WITH document context
  from flair.embeddings import TransformerWordEmbeddings
  
  embeddings = TransformerWordEmbeddings(
      model='xlm-roberta-large',
      layers="-1",
      subtoken_pooling="first",
      fine_tune=True,
      use_context=True,
  )
  
  # 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
  from flair.models import SequenceTagger
  
  tagger = SequenceTagger(
      hidden_size=256,
      embeddings=embeddings,
      tag_dictionary=tag_dictionary,
      tag_type='ner',
      use_crf=False,
      use_rnn=False,
      reproject_embeddings=False,
  )
  
  # 6. initialize trainer with AdamW optimizer
  from flair.trainers import ModelTrainer
  
  trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
  
  # 7. run training with XLM parameters (20 epochs, small LR)
  from torch.optim.lr_scheduler import OneCycleLR
  
  trainer.train('resources/taggers/ner-german-large',
                learning_rate=5.0e-6,
                mini_batch_size=4,
                mini_batch_chunk_size=1,
                max_epochs=20,
                scheduler=OneCycleLR,
                embeddings_storage_mode='none',
                weight_decay=0.,
                )
  
  )
  ```
  
  
  
  ---
  
  ### Cite
  
  Please cite the following paper when using this model.
  
  ```
  @misc{schweter2020flert,
      title={FLERT: Document-Level Features for Named Entity Recognition},
      author={Stefan Schweter and Alan Akbik},
      year={2020},
      eprint={2011.06993},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
  }
  ```
  
  ---
  
  ### Issues?
  
  The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
  