File size: 1,981 Bytes
3f69ec7 eb118eb e85ab91 3f69ec7 e85ab91 3f69ec7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
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': '.'}]
```
|