readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: en
  datasets:
  - ontonotes
  widget:
  - text: "I love Berlin."
  ---
  
  ## English Part-of-Speech Tagging in Flair (default model)
  
  This is the standard part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **98,19** (Ontonotes)
  
  Predicts fine-grained POS tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  |ADD        | Email |
  |AFX        | Affix |
  |CC         | Coordinating conjunction  |
  |CD         | Cardinal number |
  |DT         | Determiner |
  |EX         | Existential there |
  |FW         | Foreign word |
  |HYPH       | Hyphen |
  |IN        | Preposition or subordinating conjunction |
  |JJ         | Adjective |
  |JJR        |Adjective, comparative |
  |JJS        | Adjective, superlative |
  |LS         | List item marker  |
  |MD         | Modal |
  |NFP        | Superfluous punctuation |
  |NN        | Noun, singular or mass |
  |NNP        |Proper noun, singular |
  |NNPS       | Proper noun, plural |
  |NNS        |Noun, plural |
  |PDT        | Predeterminer |
  |POS        | Possessive ending |
  |PRP        | Personal pronoun |
  |PRP$       | Possessive pronoun |
  |RB         | Adverb |
  |RBR        | Adverb, comparative |
  |RBS        | Adverb, superlative |
  |RP         | Particle |
  |SYM        | Symbol |
  |TO         | to |
  |UH         | Interjection |
  |VB         | Verb, base form |
  |VBD       | Verb, past tense |
  |VBG        | Verb, gerund or present participle |
  |VBN        | Verb, past participle |
  |VBP        | Verb, non-3rd person singular present |
  |VBZ        | Verb, 3rd person singular present |
  |WDT        | Wh-determiner |
  |WP        | Wh-pronoun |
  |WP$        | Possessive wh-pronoun |
  |WRB        | Wh-adverb |
  |XX         | Unknown |
  
  
  
  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/pos-english")
  
  # make example sentence
  sentence = Sentence("I love Berlin.")
  
  # 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('pos'):
      print(entity)
  
  ```
  
  This yields the following output:
  ```
  Span [1]: "I"   [− Labels: PRP (1.0)]
  Span [2]: "love"   [− Labels: VBP (1.0)]
  Span [3]: "Berlin"   [− Labels: NNP (0.9999)]
  Span [4]: "."   [− Labels: . (1.0)]
  
  ```
  
  So, the word "*I*" is labeled as a **pronoun** (PRP),  "*love*" is labeled as a **verb** (VBP) and "*Berlin*" is labeled as a **proper noun** (NNP) in the sentence "*I love Berlin*". 
  
  
  ---
  
  ### 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 = 'pos'
  
  # 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 = [
  
      # 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/pos-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/).
  