Bert Base Bg

🚀 BERT BASE (cased)

A pre - trained model on the Bulgarian language using a masked language modeling (MLM) objective. It solves the problem of language understanding in Bulgarian and provides a useful tool for related NLP tasks.

🚀 Quick Start

This is a pre - trained model on the Bulgarian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased, which means it can distinguish between "bulgarian" and "Bulgarian".

✨ Features

• Trained on Bulgarian language using the masked language modeling (MLM) objective.
• Cased, able to differentiate between different cases of words.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

>>> from transformers import pipeline
>>> 
>>> model = pipeline(
>>>     'fill-mask',
>>>     model='rmihaylov/bert-base-bg',
>>>     tokenizer='rmihaylov/bert-base-bg',
>>>     device=0,
>>>     revision=None)
>>> output = model("София е [MASK] на България.")
>>> print(output)

[{'score': 0.12665307521820068,
  'sequence': 'София е  столица на България.',
  'token': 2659,
  'token_str': 'столица'},
 {'score': 0.07470757514238358,
  'sequence': 'София е Перлата на България.',
  'token': 102146,
  'token_str': 'Перлата'},
 {'score': 0.06786204129457474,
  'sequence': 'София е  Столицата на България.',
  'token': 45495,
  'token_str': 'Столицата'},
 {'score': 0.05533991754055023,
  'sequence': 'София е  Столица на България.',
  'token': 100524,
  'token_str': 'Столица'},
 {'score': 0.05485989898443222,
  'sequence': 'София е  столицата на България.',
  'token': 2294,
  'token_str': 'столицата'}]

📚 Documentation

Model description

The model was trained similarly to RuBert, where the Multilingual Bert was adapted for the Russian language.

The training data was Bulgarian text from OSCAR, Chitanka and Wikipedia.

📄 License

This project is licensed under the MIT license.