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--- |
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inference: false |
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language: |
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- bg |
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license: mit |
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datasets: |
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- oscar |
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- chitanka |
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- wikipedia |
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tags: |
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- torch |
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--- |
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# BERT BASE (cased) finetuned on Bulgarian part-of-speech data |
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Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in |
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[this paper](https://arxiv.org/abs/1810.04805) and first released in |
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[this repository](https://github.com/google-research/bert). This model is cased: it does make a difference |
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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/). |
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It was finetuned on public part-of-speech Bulgarian data. |
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Then, it was compressed via [progressive module replacing](https://arxiv.org/abs/2002.02925). |
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### How to use |
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Here is how to use this model in PyTorch: |
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```python |
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>>> from transformers import pipeline |
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>>> |
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>>> model = pipeline( |
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>>> 'token-classification', |
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>>> model='rmihaylov/bert-base-pos-theseus-bg', |
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>>> tokenizer='rmihaylov/bert-base-pos-theseus-bg', |
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>>> device=0, |
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>>> revision=None) |
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>>> output = model('Здравей, аз се казвам Иван.') |
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>>> print(output) |
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[{'end': 7, |
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'entity': 'INTJ', |
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'index': 1, |
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'score': 0.9640711, |
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'start': 0, |
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'word': '▁Здравей'}, |
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{'end': 8, |
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'entity': 'PUNCT', |
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'index': 2, |
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'score': 0.9998927, |
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'start': 7, |
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'word': ','}, |
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{'end': 11, |
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'entity': 'PRON', |
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'index': 3, |
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'score': 0.9998872, |
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'start': 8, |
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'word': '▁аз'}, |
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{'end': 14, |
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'entity': 'PRON', |
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'index': 4, |
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'score': 0.99990034, |
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'start': 11, |
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'word': '▁се'}, |
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{'end': 21, |
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'entity': 'VERB', |
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'index': 5, |
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'score': 0.99989736, |
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'start': 14, |
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'word': '▁казвам'}, |
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{'end': 26, |
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'entity': 'PROPN', |
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'index': 6, |
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'score': 0.99990785, |
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'start': 21, |
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'word': '▁Иван'}, |
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{'end': 27, |
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'entity': 'PUNCT', |
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'index': 7, |
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'score': 0.9999685, |
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'start': 26, |
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'word': '.'}] |
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``` |
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