readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: da
  datasets:
  - DaNE
  widget:
  - text: "Jens Peter Hansen kommer fra Danmark"
  ---
  
  # Danish NER in Flair (default model)
  
  This is the standard 4-class NER model for Danish that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **81.78** (DaNER)
  
  Predicts 4 tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  | PER         | person name | 
  | LOC         | location name | 
  | ORG         | organization name | 
  | MISC         | other name | 
  
  Based on Transformer embeddings and LSTM-CRF.
  
  ---
  # 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-danish")
  
  # make example sentence
  sentence = Sentence("Jens Peter Hansen kommer fra Danmark")
  
  # 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 [1,2,3]: "Jens Peter Hansen"   [− Labels: PER (0.9961)]
  Span [6]: "Danmark"   [− Labels: LOC (0.9816)]
  ```
  
  So, the entities "*Jens Peter Hansen*" (labeled as a **person**) and "*Danmark*" (labeled as a **location**) are found in the sentence "*Jens Peter Hansen kommer fra Danmark*". 
  
  
  ---
  
  ### Training: Script to train this model
  
  The model was trained by the [DaNLP project](https://github.com/alexandrainst/danlp) using the [DaNE corpus](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#danish-dependency-treebank-dane-dane). Check their repo for more information.
  
  The following Flair script may be used to train such a model: 
  
  ```python
  from flair.data import Corpus
  from flair.datasets import DANE
  from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
  
  # 1. get the corpus
  corpus: Corpus = DANE()
  
  # 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('da'),
  
      # contextual string embeddings, forward
      FlairEmbeddings('da-forward'),
  
      # contextual string embeddings, backward
      FlairEmbeddings('da-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-danish',
                train_with_dev=True,
                max_epochs=150)
  ```
  
  
  ---
  
  ### Cite
  
  Please cite the following papers when using this model.
  
  ```
  @inproceedings{akbik-etal-2019-flair,
      title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}",
      author = "Akbik, Alan  and
        Bergmann, Tanja  and
        Blythe, Duncan  and
        Rasul, Kashif  and
        Schweter, Stefan  and
        Vollgraf, Roland",
      booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
      year = "2019",
      url = "https://www.aclweb.org/anthology/N19-4010",
      pages = "54--59",
  }
  ```
  
  And check the [DaNLP project](https://github.com/alexandrainst/danlp) for more information.
  
  ---
  
  ### Issues?
  
  The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
  