from datasets import load_dataset dataset = load_dataset("yelp_review_full") # لعرض عينة من البيانات print(dataset["train"][100]) from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", truncation=True) tokenized_datasets = dataset.map(tokenize_function, batched=True) small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(100)) small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(100)) from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5) training_args = TrainingArguments( output_dir="./results", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=3, weight_decay=0.01, ) trainer = Trainer( model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset, ) trainer.train()