readme: ---
  inference: false
  language:
  - bg
  license: mit
  datasets:
  - oscar
  - chitanka
  - wikipedia
  tags:
  - torch
  ---
  
  # BERT BASE (cased) finetuned on Bulgarian natural-language-inference 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 private NLI 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
  >>> import torch
  >>> from transformers import AutoModelForSequenceClassification, AutoTokenizer
  >>> 
  >>> model_id = 'rmihaylov/bert-base-nli-theseus-bg'
  >>> model = AutoModelForSequenceClassification.from_pretrained(model_id)
  >>> tokenizer = AutoTokenizer.from_pretrained(model_id)
  >>>
  >>> inputs = tokenizer.encode_plus(
  >>>     'Няколко момчета играят футбол.', 
  >>>     'Няколко момичета играят футбол.', 
  >>>     return_tensors='pt')
  >>>
  >>> outputs = model(**inputs)
  >>> contradiction, entailment, neutral = torch.softmax(outputs[0][0], dim=0).detach()
  >>> contradiction, neutral, entailment
  
  (tensor(0.9998), tensor(0.0001), tensor(5.9929e-05))
  ```
  