| 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() |