This notebook regroups the code sample of the video below, which is a part of the Hugging Face course.

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#@title
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/M6adb1j2jPI?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

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Install the Transformers and Datasets libraries to run this notebook.

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! pip install datasets transformers[sentencepiece]

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from transformers import AutoTokenizer

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
sentences = [
    "I've been waiting for a HuggingFace course my whole life.",
    "I hate this.",
]
tokens = [tokenizer.tokenize(sentence) for sentence in sentences]
ids = [tokenizer.convert_tokens_to_ids(token) for token in tokens]
print(ids[0])
print(ids[1])

[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]
[1045, 5223, 2023, 1012]

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import torch

ids = [[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012],
       [1045, 5223, 2023, 1012]]

input_ids = torch.tensor(ids)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-b3483c81e2fd> in <module>
      4        [1045, 5223, 2023, 1012]]
      5 
----> 6 input_ids = torch.tensor(ids)

ValueError: expected sequence of length 14 at dim 1 (got 4)

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import torch

ids = [[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012],
       [1045, 5223, 2023, 1012,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0]]

input_ids = torch.tensor(ids)

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from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
tokenizer.pad_token_id

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from transformers import AutoModelForSequenceClassification

ids1 = torch.tensor(
    [[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]]
)
ids2 = torch.tensor([[1045, 5223, 2023, 1012]])
all_ids = torch.tensor(
    [[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012],
     [1045, 5223, 2023, 1012,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0]]
)

model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
print(model(ids1).logits)
print(model(ids2).logits)
print(model(all_ids).logits)

tensor([[-2.7276,  2.8789]], grad_fn=<AddmmBackward>)
tensor([[ 3.9497, -3.1357]], grad_fn=<AddmmBackward>)
tensor([[-2.7276,  2.8789],
        [ 1.5444, -1.3998]], grad_fn=<AddmmBackward>)

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all_ids = torch.tensor(
    [[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012],
     [1045, 5223, 2023, 1012,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0]]
)
attention_mask = torch.tensor(
    [[   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,    0,    0]]
)

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model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
output1 = model(ids1)
output2 = model(ids2)
print(output1.logits)
print(output2.logits)

tensor([[-2.7276,  2.8789]], grad_fn=<AddmmBackward>)
tensor([[ 3.9497, -3.1357]], grad_fn=<AddmmBackward>)

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output = model(all_ids, attention_mask=attention_mask)
print(output.logits)

tensor([[-2.7276,  2.8789],
        [ 3.9497, -3.1357]], grad_fn=<AddmmBackward>)

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from transformers import AutoTokenizer

checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
sentences = [
    "I've been waiting for a HuggingFace course my whole life.",
    "I hate this.",
]
print(tokenizer(sentences, padding=True))

{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], [101, 1045, 5223, 2023, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[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, 0, 0]]}

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