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This notebook regroups the code sample of the video below, which is a part of the Hugging Face course.
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Install the Transformers and Datasets libraries to run this notebook.
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[{'label': 'POSITIVE', 'score': 0.9598047137260437},
, {'label': 'NEGATIVE', 'score': 0.9994558095932007}] [ ]
{'input_ids': tensor([[ 101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172,
2607, 2026, 2878, 2166, 1012, 102],
[ 101, 1045, 5223, 2023, 2061, 2172, 999, 102, 0, 0,
0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}
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Some weights of the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english were not used when initializing DistilBertModel: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'classifier.weight'] - This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
torch.Size([2, 16, 768])
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tensor([[-1.5607, 1.6123],
[ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)
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tensor([[4.0195e-02, 9.5980e-01],
[9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)
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{0: 'NEGATIVE', 1: 'POSITIVE'} [ ]