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GitHub Repository: huggingface/notebooks
 Path: blob/main/course/ru/chapter4/section2_tf.ipynb
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Использование предобученных моделей (TensorFlow)

Install the Transformers, Datasets, and Evaluate libraries to run this notebook.

!pip install datasets evaluate transformers[sentencepiece]

from transformers import pipeline

camembert_fill_mask = pipeline("fill-mask", model="camembert-base")
results = camembert_fill_mask("Le camembert est <mask> :)")
[ {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'} ]
from transformers import CamembertTokenizer, TFCamembertForMaskedLM

tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
model = TFCamembertForMaskedLM.from_pretrained("camembert-base")

from transformers import AutoTokenizer, TFAutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("camembert-base")
model = TFAutoModelForMaskedLM.from_pretrained("camembert-base")