Install the Transformers, Datasets, and Evaluate libraries to run this notebook.
!pip install datasets evaluate transformers[sentencepiece]
import torch
from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification
# Same as before
checkpoint = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequences = [
"I've been waiting for a HuggingFace course my whole life.",
"This course is amazing!",
]
batch = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
# This is new
batch["labels"] = torch.tensor([1, 1])
optimizer = AdamW(model.parameters())
loss = model(**batch).loss
loss.backward()
optimizer.step()
from datasets import load_dataset
raw_datasets = load_dataset("glue", "mrpc")
raw_datasets
DatasetDict({
train: Dataset({
features: ['sentence1', 'sentence2', 'label', 'idx'],
num_rows: 3668
})
validation: Dataset({
features: ['sentence1', 'sentence2', 'label', 'idx'],
num_rows: 408
})
test: Dataset({
features: ['sentence1', 'sentence2', 'label', 'idx'],
num_rows: 1725
})
})
raw_train_dataset = raw_datasets["train"]
raw_train_dataset[0]
{'idx': 0,
'label': 1,
'sentence1': 'Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
'sentence2': 'Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'}
raw_train_dataset.features
{'sentence1': Value(dtype='string', id=None),
'sentence2': Value(dtype='string', id=None),
'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),
'idx': Value(dtype='int32', id=None)}
from transformers import AutoTokenizer
checkpoint = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
tokenized_sentences_1 = tokenizer(raw_datasets["train"]["sentence1"])
tokenized_sentences_2 = tokenizer(raw_datasets["train"]["sentence2"])
inputs = tokenizer("This is the first sentence.", "This is the second one.")
inputs
{
'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}
tokenizer.convert_ids_to_tokens(inputs["input_ids"])
['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']
tokenized_dataset = tokenizer(
raw_datasets["train"]["sentence1"],
raw_datasets["train"]["sentence2"],
padding=True,
truncation=True,
)
def tokenize_function(example):
return tokenizer(example["sentence1"], example["sentence2"], truncation=True)
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
tokenized_datasets
DatasetDict({
train: Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],
num_rows: 3668
})
validation: Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],
num_rows: 408
})
test: Dataset({
features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],
num_rows: 1725
})
})
from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
samples = tokenized_datasets["train"][:8]
samples = {k: v for k, v in samples.items() if k not in ["idx", "sentence1", "sentence2"]}
[len(x) for x in samples["input_ids"]]
[50, 59, 47, 67, 59, 50, 62, 32]
batch = data_collator(samples)
{k: v.shape for k, v in batch.items()}
{'attention_mask': torch.Size([8, 67]),
'input_ids': torch.Size([8, 67]),
'token_type_ids': torch.Size([8, 67]),
'labels': torch.Size([8])}