Create basic

basic

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+from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("wikitext", "wikitext-2-raw-v1")
+
+# Load tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+
+# Tokenize dataset
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    save_steps=10_000,
+    save_total_limit=2,
+    logging_dir="./logs",
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"],
+)
+
+# Train the model
+trainer.train()