legacy-datasets/wikipedia
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How to use rmihaylov/bert-base-squad-theseus-bg with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="rmihaylov/bert-base-squad-theseus-bg") # Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("rmihaylov/bert-base-squad-theseus-bg")
model = AutoModelForQuestionAnswering.from_pretrained("rmihaylov/bert-base-squad-theseus-bg")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.
It was finetuned on private squad Bulgarian data.
Then, it was compressed via progressive module replacing.
Here is how to use this model in PyTorch:
>>> from transformers import pipeline
>>>
>>> model = pipeline(
>>> 'question-answering',
>>> model='rmihaylov/bert-base-squad-theseus-bg',
>>> tokenizer='rmihaylov/bert-base-squad-theseus-bg',
>>> device=0,
>>> revision=None)
>>>
>>> question = "С какво се проследява пандемията?"
>>> context = "Епидемията гасне, обяви при обявяването на данните тази сутрин Тодор Кантарджиев, член на Националния оперативен щаб. Той направи този извод на база на данните от математическите модели, с които се проследява развитието на заразата. Те показват, че т. нар. ефективно репродуктивно число е вече в границите 0.6-1. Тоест, 10 души заразяват 8, те на свой ред 6 и така нататък. "
>>> output = model(**{'question': question, 'context': context})
>>> print(output)
{'score': 0.85157310962677, 'start': 162, 'end': 186, 'answer': ' математическите модели,'}