---
inference: false
language:
- bg
license: mit
datasets:
- oscar
- chitanka
- wikipedia
tags:
- torch
---

# BERT BASE (cased) finetuned on Bulgarian named-entity-recognition 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 named-entity-recognition 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(
>>>     'ner',
>>>     model='rmihaylov/bert-base-ner-theseus-bg',
>>>     tokenizer='rmihaylov/bert-base-ner-theseus-bg',
>>>     device=0,
>>>     revision=None)
>>> output = model('Здравей, аз се казвам Иван.')
>>> print(output)

[{'end': 26,
  'entity': 'B-PER',
  'index': 6,
  'score': 0.9937722,
  'start': 21,
  'word': '▁Иван'}]
```