readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: en
  datasets:
  - conll2003
  widget:
  - text: "George Washington went to Washington"
  ---
  
  ## English NER in Flair (fast model)
  
  This is the fast 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **92,92** (corrected CoNLL-03)
  
  Predicts 4 tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  | PER         | person name | 
  | LOC         | location name | 
  | ORG         | organization name | 
  | MISC         | other name | 
  
  Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) 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-english-fast")
  
  # make example sentence
  sentence = Sentence("George Washington went to 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 (0.9515)]
  Span [5]: "Washington"   [− Labels: LOC (0.992)]
  ```
  
  So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to 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
  from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
  
  # 1. get the corpus
  corpus: Corpus = CONLL_03()
  
  # 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('glove'),
  
      # contextual string embeddings, forward
      FlairEmbeddings('news-forward-fast'),
  
      # contextual string embeddings, backward
      FlairEmbeddings('news-backward-fast'),
  ]
  
  # 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-english',
                train_with_dev=True,
                max_epochs=150)
  ```
  
  
  
  ---
  
  ### Cite
  
  Please cite the following paper when using this model.
  
  ```
  @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/).
  