readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: en
  datasets:
  - ontonotes
  widget:
  - text: "George returned to Berlin to return his hat."
  ---
  
  ## English Verb Disambiguation in Flair (fast model)
  
  This is the fast verb disambiguation model for English that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **88,27** (Ontonotes) - predicts [Proposition Bank verb frames](http://verbs.colorado.edu/propbank/framesets-english-aliases/).
  
  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/frame-english-fast")
  
  # make example sentence
  sentence = Sentence("George returned to Berlin to return his hat.")
  
  # predict NER tags
  tagger.predict(sentence)
  
  # print sentence
  print(sentence)
  
  # print predicted NER spans
  print('The following frame tags are found:')
  # iterate over entities and print
  for entity in sentence.get_spans('frame'):
      print(entity)
  
  ```
  
  This yields the following output:
  ```
  Span [2]: "returned"   [− Labels: return.01 (0.9867)]
  Span [6]: "return"   [− Labels: return.02 (0.4741)]
  ```
  
  So, the word "*returned*" is labeled as **return.01** (as in *go back somewhere*) while "*return*" is labeled as **return.02** (as in *give back something*) in the sentence "*George returned to Berlin to return his hat*". 
  
  
  ---
  
  ### 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 = ColumnCorpus(
      "resources/tasks/srl", column_format={1: "text", 11: "frame"}
  )
  
  # 2. what tag do we want to predict?
  tag_type = 'frame'
  
  # 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 = [
  
      BytePairEmbeddings("en"),
  
      FlairEmbeddings("news-forward-fast"),
  
      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/frame-english-fast',
                train_with_dev=True,
                max_epochs=150)
  ```
  
  
  
  ---
  
  ### Cite
  
  Please cite the following paper when using this model.
  
  ```
  @inproceedings{akbik2019flair,
    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={{NAACL} 2019, 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)},
    pages={54--59},
    year={2019}
  }
  
  ```
  
  ---
  
  ### Issues?
  
  The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
  