Create train_model.py

train_model.py

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+from datasets import load_dataset
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from transformers import Trainer, TrainingArguments
+
+# Load dataset (replace with your actual dataset path or URL)
+dataset = load_dataset("path_to_your_dataset")
+
+# Load the pre-trained model and tokenizer
+model_name = "gpt2"  # Or another model like T5, BART, etc.
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+# Preprocess the dataset: tokenizing the answers and feedback
+def preprocess_function(examples):
+    return tokenizer(examples['answer'], truncation=True, padding='max_length', max_length=512)
+
+# Apply preprocessing to the dataset
+encoded_dataset = dataset.map(preprocess_function, batched=True)
+
+# Define the training arguments
+training_args = TrainingArguments(
+    output_dir="./results",            # Where to save the model
+    evaluation_strategy="epoch",       # Evaluate after each epoch
+    per_device_train_batch_size=4,     # Batch size for training
+    per_device_eval_batch_size=4,      # Batch size for evaluation
+    num_train_epochs=3,                # Number of training epochs
+    logging_dir='./logs',              # Directory for logging
+    logging_steps=10,
+    save_steps=500,                    # Save model checkpoint every 500 steps
+    warmup_steps=200,                  # Gradual learning rate warm-up steps
+    weight_decay=0.01,                 # Weight decay for regularization
+    load_best_model_at_end=True,       # Load the best model based on evaluation metric
+)
+
+# Initialize the Trainer
+trainer = Trainer(
+    model=model,                       # The pre-trained model you want to fine-tune
+    args=training_args,                # The training configuration
+    train_dataset=encoded_dataset['train'],  # Your training data
+    eval_dataset=encoded_dataset['test'],    # Your evaluation data
+)
+
+# Start training
+trainer.train()
+
+# Save the model after training
+model.save_pretrained("./fine_tuned_model")
+tokenizer.save_pretrained("./fine_tuned_model")
+
+print("Model fine-tuning complete!")