readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: 
  - en 
  - de 
  - fr 
  - it
  - nl
  - pl
  - es
  - sv
  - da
  - no
  - fi
  - cs
  datasets:
  - ontonotes
  widget:
  - text: "Ich liebe Berlin, as they say."
  ---
  
  ## Multilingual Universal Part-of-Speech Tagging in Flair (fast model)
  
  This is the fast multilingual universal part-of-speech tagging model that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **92,88** (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Danish, Norwegian, Finnish and Czech)
  
  Predicts universal POS tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  |ADJ |  adjective |
   |   ADP |  adposition |
   |   ADV |  adverb |
   |   AUX |  auxiliary |
   |   CCONJ |  coordinating conjunction |
   |   DET |  determiner |
   |   INTJ |  interjection |
   |   NOUN |  noun |
   |   NUM |  numeral |
   |   PART |  particle |
   |   PRON |  pronoun |
   |   PROPN |  proper noun |
   |   PUNCT |  punctuation |
   |   SCONJ |  subordinating conjunction |
   |   SYM |  symbol |
   |   VERB |  verb |
   |   X |  other |
  
  
  
  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/upos-multi-fast")
  
  # make example sentence
  sentence = Sentence("Ich liebe Berlin, as they say. ")
  
  # 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]: "Ich"   [− Labels: PRON (0.9999)]
  Span [2]: "liebe"   [− Labels: VERB (0.9999)]
  Span [3]: "Berlin"   [− Labels: PROPN (0.9997)]
  Span [4]: ","   [− Labels: PUNCT (1.0)]
  Span [5]: "as"   [− Labels: SCONJ (0.9991)]
  Span [6]: "they"   [− Labels: PRON (0.9998)]
  Span [7]: "say"   [− Labels: VERB (0.9998)]
  Span [8]: "."   [− Labels: PUNCT (1.0)]
  ```
  
  So, the words "*Ich*" and "*they*" are labeled as **pronouns** (PRON), while "*liebe*" and "*say*" are labeled as **verbs** (VERB) in the multilingual sentence "*Ich liebe Berlin, as they say*". 
  
  
  ---
  
  ### Training: Script to train this model
  
  The following Flair script was used to train this model: 
  
  ```python
  from flair.data import MultiCorpus
  from flair.datasets import UD_ENGLISH, UD_GERMAN, UD_FRENCH, UD_ITALIAN, UD_POLISH, UD_DUTCH, UD_CZECH, \
      UD_DANISH, UD_SPANISH, UD_SWEDISH, UD_NORWEGIAN, UD_FINNISH
  from flair.embeddings import StackedEmbeddings, FlairEmbeddings
  
  # 1. make a multi corpus consisting of 12 UD treebanks (in_memory=False here because this corpus becomes large)
  corpus = MultiCorpus([
      UD_ENGLISH(in_memory=False),
      UD_GERMAN(in_memory=False),
      UD_DUTCH(in_memory=False),
      UD_FRENCH(in_memory=False),
      UD_ITALIAN(in_memory=False),
      UD_SPANISH(in_memory=False),
      UD_POLISH(in_memory=False),
      UD_CZECH(in_memory=False),
      UD_DANISH(in_memory=False),
      UD_SWEDISH(in_memory=False),
      UD_NORWEGIAN(in_memory=False),
      UD_FINNISH(in_memory=False),
  ])
  
  # 2. what tag do we want to predict?
  tag_type = 'upos'
  
  # 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('multi-forward-fast'),
  
      # contextual string embeddings, backward
      FlairEmbeddings('multi-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,
                          use_crf=False)
  
  # 6. initialize trainer
  from flair.trainers import ModelTrainer
  
  trainer = ModelTrainer(tagger, corpus)
  
  # 7. run training
  trainer.train('resources/taggers/upos-multi-fast',
                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/).
  