Upload t5_project_all_in_one.py

t5_project_all_in_one.py

@@ -8,28 +8,38 @@ import torch
 import matplotlib.pyplot as plt
 
 # Step 1: Log in to Hugging Face
-# Students: Replace "YOUR_HUGGING_FACE_TOKEN" with your actual Hugging Face token from https://huggingface.co/settings/tokens
-hf_token = "YOUR_HUGGING_FACE_TOKEN"
-if not hf_token or hf_token == "YOUR_HUGGING_FACE_TOKEN":
-    raise ValueError("Please replace 'YOUR_HUGGING_FACE_TOKEN' in the code with your actual Hugging Face token")
-login(token=hf_token)
-print("Logged in to Hugging Face successfully")
+# Students: Replace with your actual Hugging Face token from https://huggingface.co/settings/tokens
+hf_token = "YOUR_HUGGING_FACE_TOKEN" #Replace your YOUR_HUGGING_FACE_TOKEN here
+if not hf_token or hf_token == "YOUR_HUGGING_FACE_TOKEN": # Don't replace here 
+    raise ValueError("Please replace 'YOUR_HUGGING_FACE_TOKEN' with your actual Hugging Face token")
+try:
+    login(token=hf_token)
+    print("Logged in to Hugging Face successfully")
+except Exception as e:
+    raise ValueError(f"Failed to log in to Hugging Face: {str(e)}")
 
 # Step 2: Load and convert dataset
-# Students: Replace "dataset.csv" or "dataset.json" with your dataset file name
-dataset_name = "dataset.csv"  # Change to "dataset.json" if using JSON
+# Students: Replace with your dataset file name (CSV or JSON)
+dataset_name = "dataset.json"  # Change to "dataset.csv" if using CSV
 dataset_path = dataset_name
+if not os.path.exists(dataset_path):
+    raise FileNotFoundError(f"Dataset file '{dataset_path}' not found in the project folder")
 if dataset_name.endswith('.csv'):
-    # Convert CSV to JSON for consistency
+      # Convert CSV to JSON for consistency
     print(f"Converting {dataset_name} to JSON format...")
-    df = pd.read_csv(dataset_path)
-    df.to_json('dataset.json', orient='records', lines=True)
-    dataset_path = 'dataset.json'
+    try:
+        df = pd.read_csv(dataset_path)
+        df.to_json('dataset.json', orient='records', lines=True)
+        dataset_path = 'dataset.json'
+    except Exception as e:
+        raise ValueError(f"Failed to convert CSV to JSON: {str(e)}")
 
 # Load dataset
 print(f"Loading dataset from {dataset_path}...")
-dataset = load_dataset('json', data_files=dataset_path)
-
+try:
+    dataset = load_dataset('json', data_files=dataset_path)
+except Exception as e:
+    raise ValueError(f"Failed to load dataset: {str(e)}")
 # Step 3: Split dataset into training and validation
 # 85% training, 15% validation to monitor model performance
 print("Splitting dataset into training and validation sets...")
@@ -39,12 +49,14 @@ eval_dataset = train_test_split['test']
 
 # Step 4: Download and load tokenizer and model
 print("Downloading T5-small model and tokenizer...")
-tokenizer = T5Tokenizer.from_pretrained('t5-small')
-model = T5ForConditionalGeneration.from_pretrained('t5-small')
-# Save model weights locally for fine-tuning
-model.save_pretrained('./t5_small_weights')
-tokenizer.save_pretrained('./t5_small_weights')
-print("Model and tokenizer saved to './t5_small_weights'")
+try:
+    tokenizer = T5Tokenizer.from_pretrained('t5-small')
+    model = T5ForConditionalGeneration.from_pretrained('t5-small')
+    model.save_pretrained('./t5_small_weights') # Save model weights locally for fine-tuning
+    tokenizer.save_pretrained('./t5_small_weights') 
+    print("Model and tokenizer saved to './t5_small_weights'")
+except Exception as e:
+    raise ValueError(f"Failed to download or save model/tokenizer: {str(e)}")
 
 # Step 5: Preprocess dataset
 # This ensures the input questions and answers are properly tokenized for T5
@@ -52,43 +64,42 @@ def preprocess_data(examples):
     # Add "question:" prefix to inputs and clean whitespace
     inputs = ["question: " + q.strip() for q in examples['input']]
     targets = [r.strip() for r in examples['response']]
-    # Tokenize inputs (questions)
+     # Tokenize inputs (questions)
     model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding='max_length')
     # Tokenize labels (answers)
     labels = tokenizer(targets, max_length=64, truncation=True, padding='max_length')
-    # Replace pad token IDs in labels with -100 to ignore them in loss calculation
     model_inputs['labels'] = [
         [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels['input_ids']
     ]
     return model_inputs
 
-# Apply preprocessing to training and validation datasets
 print("Preprocessing datasets...")
-processed_train_dataset = train_dataset.map(preprocess_data, batched=True, remove_columns=['input', 'response'])
-processed_eval_dataset = eval_dataset.map(preprocess_data, batched=True, remove_columns=['input', 'response'])
+try:
+    processed_train_dataset = train_dataset.map(preprocess_data, batched=True, remove_columns=['input', 'response'])
+    processed_eval_dataset = eval_dataset.map(preprocess_data, batched=True, remove_columns=['input', 'response'])
+except Exception as e:
+    raise ValueError(f"Failed to preprocess dataset: {str(e)}")
 
 # Step 6: Define training arguments
-# These settings control how the model is fine-tuned
 training_args = TrainingArguments(
-    output_dir='./results',                # Directory to save training outputs
-    num_train_epochs=10,                   # Number of training iterations over the dataset
-    per_device_train_batch_size=2,         # Batch size per device (GPU/CPU)
-    gradient_accumulation_steps=2,          # Accumulate gradients to simulate larger batch size
-    learning_rate=3e-4,                    # Learning rate for optimization
-    save_steps=500,                        # Save model checkpoint every 500 steps
-    save_total_limit=2,                    # Keep only the last 2 checkpoints
-    logging_steps=50,                      # Log training metrics every 50 steps
-    eval_strategy="steps",                 # Evaluate model during training at regular intervals
-    eval_steps=100,                        # Evaluate every 100 steps
-    load_best_model_at_end=True,           # Load the best model based on validation loss
-    metric_for_best_model="eval_loss",     # Use validation loss to select best model
-    greater_is_better=False,               # Lower validation loss is better
-    gradient_checkpointing=True,           # Save memory during training
-    max_grad_norm=1.0,                     # Clip gradients to prevent exploding gradients
+    output_dir='./results',
+    num_train_epochs=15,  # Increased for better convergence
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=2,
+    learning_rate=5e-4,  # Increased for faster learning
+    save_steps=500,
+    save_total_limit=2,
+    logging_steps=50,
+    eval_strategy="steps",
+    eval_steps=100,
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+    greater_is_better=False,
+    gradient_checkpointing=True,
+    max_grad_norm=1.0,
 )
 
 # Step 7: Initialize Trainer
-# The Trainer handles the fine-tuning process
 print("Initializing Trainer...")
 trainer = Trainer(
     model=model,
@@ -99,11 +110,13 @@ trainer = Trainer(
 
 # Step 8: Train the model
 print("Starting training...")
-trainer.train()
-print("Training finished.")
+try:
+    trainer.train()
+    print("Training finished.")
+except Exception as e:
+    raise ValueError(f"Training failed: {str(e)}")
 
 # Step 9: Plot training and validation loss
-# This helps students visualize model performance
 print("Generating training and validation loss plot...")
 logs = trainer.state.log_history
 steps = [log['step'] for log in logs if 'loss' in log or 'eval_loss' in log]