readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: en
  datasets:
  - ontonotes
  widget:
  - text: "On September 1st George Washington won 1 dollar."
  ---
  
  ## English NER in Flair (Ontonotes default model)
  
  This is the 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **89.27** (Ontonotes)
  
  Predicts 18 tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  | CARDINAL    | cardinal value | 
  | DATE         | date value | 
  | EVENT         | event name | 
  | FAC         | building name | 
  | GPE         | geo-political entity | 
  | LANGUAGE         | language name | 
  | LAW         | law name | 
  | LOC         | location name | 
  | MONEY         | money name | 
  | NORP         | affiliation | 
  | ORDINAL         | ordinal value | 
  | ORG         | organization name | 
  | PERCENT         | percent value | 
  | PERSON         | person name | 
  | PRODUCT         | product name | 
  | QUANTITY         | quantity value | 
  | TIME         | time value | 
  | WORK_OF_ART         | name of work of art | 
  
  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-ontonotes")
  
  # make example sentence
  sentence = Sentence("On September 1st George Washington won 1 dollar.")
  
  # 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 [2,3]: "September 1st"   [− Labels: DATE (0.8824)]
  Span [4,5]: "George Washington"   [− Labels: PERSON (0.9604)]
  Span [7,8]: "1 dollar"   [− Labels: MONEY (0.9837)]
  ```
  
  So, the entities "*September 1st*" (labeled as a **date**), "*George Washington*" (labeled as a **person**) and "*1 dollar*" (labeled as a **money**) are found in the sentence "*On September 1st George Washington won 1 dollar*". 
  
  
  ---
  
  ### 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 ColumnCorpus
  from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
  
  # 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
  corpus: Corpus = ColumnCorpus(
                  "resources/tasks/onto-ner",
                  column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
                  tag_to_bioes="ner",
              )
  
  # 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('en-crawl'),
  
      # contextual string embeddings, forward
      FlairEmbeddings('news-forward'),
  
      # contextual string embeddings, backward
      FlairEmbeddings('news-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-english-ontonotes',
                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/).
  