readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: sl
  widget:
  - text: "Danes je lep dan."
  ---
  
  ## Slovene Part-of-speech (PoS) Tagging for Flair
  
  This is a Slovene part-of-speech (PoS) tagger trained on the [Slovenian UD Treebank](https://github.com/UniversalDependencies/UD_Slovenian-SSJ) using Flair NLP framework.
  
  The tagger is trained using a combination of forward Slovene contextual string embeddings, backward Slovene contextual string embeddings and classic Slovene FastText embeddings.
  
  F-score (micro): **94,96**
  
  The model is trained on a large (500+) number of different tags that described at [https://universaldependencies.org/tagset-conversion/sl-multext-uposf.html](https://universaldependencies.org/tagset-conversion/sl-multext-uposf.html).
  
  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("tadejmagajna/flair-sl-pos")
  
  # make example sentence
  sentence = Sentence("Danes je lep dan.")
  
  # predict PoS tags
  tagger.predict(sentence)
  
  # print sentence
  print(sentence)
  
  # print predicted PoS spans
  print('The following PoS tags are found:')
  # iterate over parts of speech and print
  for tag in sentence.get_spans('pos'):
      print(tag)
  ```
  
  This prints out the following output:
  ```
  Sentence: "Danes je lep dan ."   [− Tokens: 5  − Token-Labels: "Danes <Rgp> je <Va-r3s-n> lep <Agpmsnn> dan <Ncmsn> . <Z>"]
  The following PoS tags are found:
  Span [1]: "Danes"   [− Labels: Rgp (1.0)]
  Span [2]: "je"   [− Labels: Va-r3s-n (1.0)]
  Span [3]: "lep"   [− Labels: Agpmsnn (0.9999)]
  Span [4]: "dan"   [− Labels: Ncmsn (1.0)]
  Span [5]: "."   [− Labels: Z (1.0)]
  ```
  
  ---
  ### Training: Script to train this model
  The following standard Flair script was used to train this model: 
  
  ```python
  from flair.data import Corpus
  from flair.datasets import UD_SLOVENIAN
  from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
  
  # 1. get the corpus
  corpus: Corpus = UD_SLOVENIAN()
  
  # 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 embeddings
  embedding_types = [
      WordEmbeddings('sl'),
      FlairEmbeddings('sl-forward'),
      FlairEmbeddings('sl-backward'),
  ]
  embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_types)
  
  # 5. initialize sequence tagger
  from flair.models import SequenceTagger
  
  tagger: SequenceTagger = SequenceTagger(hidden_size=256,
                                          embeddings=embeddings,
                                          tag_dictionary=tag_dictionary,
                                          tag_type=tag_type)
  
  # 6. initialize trainer
  from flair.trainers import ModelTrainer
  
  trainer: ModelTrainer = ModelTrainer(tagger, corpus)
  
  # 7. start training
  trainer.train('resources/taggers/pos-slovene',
                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/).