readme: ---
  language:
  - "be"
  - "bg"
  - "mk"
  - "ru"
  - "sr"
  - "uk"
  tags:
  - "belarusian"
  - "bulgarian"
  - "macedonian"
  - "russian"
  - "serbian"
  - "ukrainian"
  - "token-classification"
  - "pos"
  - "dependency-parsing"
  datasets:
  - "universal_dependencies"
  license: "cc-by-sa-4.0"
  pipeline_tag: "token-classification"
  ---
  
  # bert-large-slavic-cyrillic-upos
  
  ## Model Description
  
  This is a BERT model pre-trained with Slavic-Cyrillic ([UD_Belarusian](https://universaldependencies.org/be/) [UD_Bulgarian](https://universaldependencies.org/bg/) [UD_Russian](https://universaldependencies.org/ru/) [UD_Serbian](https://universaldependencies.org/treebanks/sr_set/) [UD_Ukrainian](https://universaldependencies.org/treebanks/uk_iu/)) for POS-tagging and dependency-parsing, derived from [ruBert-large](https://huggingface.co/sberbank-ai/ruBert-large). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
  
  ## How to Use
  
  ```py
  from transformers import AutoTokenizer,AutoModelForTokenClassification
  tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-slavic-cyrillic-upos")
  model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-slavic-cyrillic-upos")
  ```
  
  or
  
  ```py
  import esupar
  nlp=esupar.load("KoichiYasuoka/bert-large-slavic-cyrillic-upos")
  ```
  
  ## See Also
  
  [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
  
  