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rmihaylov/bert-base-theseus-bg
bert-base-theseus-bg is a fill-mask model from rmihaylov. Use it when you need the model to fill a missing word. It is set up for transformers. The card lists the license as mit.
Pretrained model on 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: it does make a difference between bu…
Downloads · 30 days
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From the Hugging Face model README
Pretrained model on 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: it does make a difference between bulgarian and Bulgarian. The training data is Bulgarian text from OSCAR, Chitanka and Wikipedia.
The model was compressed via progressive module replacing.
Here is how to use this model in PyTorch:
>>> from transformers import pipeline
>>>
>>> model = pipeline(
>>> 'fill-mask',
>>> model='rmihaylov/bert-base-theseus-bg',
>>> tokenizer='rmihaylov/bert-base-theseus-bg',
>>> device=0,
>>> revision=None)
>>> output = model("София е [MASK] на България.")
>>> print(output)
[{'score': 0.1586454212665558,
'sequence': 'София е столица на България.',
'token': 76074,
'token_str': 'столица'},
{'score': 0.12992817163467407,
'sequence': 'София е столица на България.',
'token': 2659,
'token_str': 'столица'},
{'score': 0.06064048036932945,
'sequence': 'София е Перлата на България.',
'token': 102146,
'token_str': 'Перлата'},
{'score': 0.034687548875808716,
'sequence': 'София е представителката на България.',
'token': 105456,
'token_str': 'представителката'},
{'score': 0.03053216263651848,
'sequence': 'София е присъединяването на България.',
'token': 18749,
'token_str': 'присъединяването'}]