Install the Transformers, Datasets, and Evaluate libraries to run this notebook.
!pip install datasets evaluate transformers[sentencepiece]
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequence = "I've been waiting for a HuggingFace course my whole life."
tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.tensor(ids)
# This line will fail.
model(input_ids)
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
tokenized_inputs = tokenizer(sequence, return_tensors="pt")
print(tokenized_inputs["input_ids"])
tensor([[ 101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172,
2607, 2026, 2878, 2166, 1012, 102]])
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequence = "I've been waiting for a HuggingFace course my whole life."
tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.tensor([ids])
print("Input IDs:", input_ids)
output = model(input_ids)
print("Logits:", output.logits)
Input IDs: [[ 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]] Logits: [[-2.7276, 2.8789]]
batched_ids = [
[200, 200, 200],
[200, 200]
]
padding_id = 100
batched_ids = [
[200, 200, 200],
[200, 200, padding_id],
]
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequence1_ids = [[200, 200, 200]]
sequence2_ids = [[200, 200]]
batched_ids = [
[200, 200, 200],
[200, 200, tokenizer.pad_token_id],
]
print(model(torch.tensor(sequence1_ids)).logits)
print(model(torch.tensor(sequence2_ids)).logits)
print(model(torch.tensor(batched_ids)).logits)
tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)
tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)
tensor([[ 1.5694, -1.3895],
[ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)
batched_ids = [
[200, 200, 200],
[200, 200, tokenizer.pad_token_id],
]
attention_mask = [
[1, 1, 1],
[1, 1, 0],
]
outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))
print(outputs.logits)
tensor([[ 1.5694, -1.3895],
[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)
sequence = sequence[:max_sequence_length]