readme: ---
  inference: false
  language:
  - bg
  license: mit
  datasets:
  - oscar
  - chitanka
  - wikipedia
  tags:
  - torch
  ---
  
  # BERT BASE (cased) finetuned on Bulgarian part-of-speech data
  
  Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
  [this paper](https://arxiv.org/abs/1810.04805) and first released in
  [this repository](https://github.com/google-research/bert). This model is cased: it does make a difference
  between bulgarian and Bulgarian. The training data is Bulgarian text from [OSCAR](https://oscar-corpus.com/post/oscar-2019/), [Chitanka](https://chitanka.info/) and [Wikipedia](https://bg.wikipedia.org/).
  
  It was finetuned on public part-of-speech Bulgarian data.
  
  Then, it was compressed via [progressive module replacing](https://arxiv.org/abs/2002.02925).
  
  ### How to use
  
  Here is how to use this model in PyTorch:
  
  ```python
  >>> from transformers import pipeline
  >>> 
  >>> model = pipeline(
  >>>     'token-classification',
  >>>     model='rmihaylov/bert-base-pos-theseus-bg',
  >>>     tokenizer='rmihaylov/bert-base-pos-theseus-bg',
  >>>     device=0,
  >>>     revision=None)
  >>> output = model('Здравей, аз се казвам Иван.')
  >>> print(output)
  
  [{'end': 7,
    'entity': 'INTJ',
    'index': 1,
    'score': 0.9640711,
    'start': 0,
    'word': '▁Здравей'},
   {'end': 8,
    'entity': 'PUNCT',
    'index': 2,
    'score': 0.9998927,
    'start': 7,
    'word': ','},
   {'end': 11,
    'entity': 'PRON',
    'index': 3,
    'score': 0.9998872,
    'start': 8,
    'word': '▁аз'},
   {'end': 14,
    'entity': 'PRON',
    'index': 4,
    'score': 0.99990034,
    'start': 11,
    'word': '▁се'},
   {'end': 21,
    'entity': 'VERB',
    'index': 5,
    'score': 0.99989736,
    'start': 14,
    'word': '▁казвам'},
   {'end': 26,
    'entity': 'PROPN',
    'index': 6,
    'score': 0.99990785,
    'start': 21,
    'word': '▁Иван'},
   {'end': 27,
    'entity': 'PUNCT',
    'index': 7,
    'score': 0.9999685,
    'start': 26,
    'word': '.'}]
  ```
  