readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: nl
  datasets:
  - conll2003
  widget:
  - text: "George Washington ging naar Washington."
  ---
  
  # Dutch NER in Flair (default model)
  
  This is the standard 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **92,58** (CoNLL-03)
  
  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-dutch")
  
  # make example sentence
  sentence = Sentence("George Washington ging naar 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 (0.997)]
  Span [5]: "Washington"   [− Labels: LOC (0.9996)]
  ```
  
  So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging naar Washington*". 
  
  
  ---
  
  ### 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 CONLL_03_DUTCH
  from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
  
  
  # 1. get the corpus
  corpus: Corpus = CONLL_03_DUTCH()
  
  # 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 embeddings
  embeddings = TransformerWordEmbeddings('wietsedv/bert-base-dutch-cased')
  
  # 5. initialize sequence tagger
  tagger: SequenceTagger = SequenceTagger(hidden_size=256,
                                          embeddings=embeddings,
                                          tag_dictionary=tag_dictionary,
                                          tag_type=tag_type)
  
  # 6. initialize trainer
  trainer: ModelTrainer = ModelTrainer(tagger, corpus)
  
  # 7. run training
  trainer.train('resources/taggers/ner-dutch',
                train_with_dev=True,
                max_epochs=150)
  ```
  
  
  ---
  
  ### Cite
  
  Please cite the following paper 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",
  }
  ```
  
  ---
  
  ### Issues?
  
  The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
  