app.py

from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
from datasets import load_dataset

# Load the text dataset from the specified file
dataset = load_dataset("text", data_files="training.txt")


# Initialize the GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token

# Define a function to tokenize the dataset and prepare labels
def tokenize_function(examples):
    # Tokenize the text to input_ids, attention_mask, with reduced max_length
    tokenized_inputs = tokenizer(
        examples["text"], 
        padding="max_length", 
        truncation=True,  
        max_length=256  # Reduced from 512 to 256
    )
    # Prepare labels: labels are the same as input_ids for language modeling
    tokenized_inputs["labels"] = tokenized_inputs["input_ids"].copy()
    return tokenized_inputs

# Tokenize the entire dataset
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# Remove the 'text' column as it's no longer needed after tokenization
tokenized_datasets = tokenized_datasets.remove_columns(["text"])
# Set the format of the dataset to PyTorch tensors
tokenized_datasets.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])

# Load the GPT-2 model
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Define training arguments with adjusted settings
training_args = TrainingArguments(
    output_dir="./output",
    overwrite_output_dir=True,
    num_train_epochs=2,  # Optionally reduced for quicker iteration
    per_device_train_batch_size=2,  # Reduced from 4 to 2
    gradient_accumulation_steps=16,  # Added to compensate for smaller batch size
    save_steps=10_000,
    save_total_limit=2,
)

# Initialize the Trainer with the training dataset including labels
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
)

# Start the training process
trainer.train()

model.save_pretrained('C:\\Users\\Mike Corrigan\\Documents\\DMD Year 4 Sem 1\\FYP INFO\\fyp hugging face\\fypmc20277423\\output\\fine_tuned_gpt2_model')
tokenizer.save_pretrained('C:\\Users\\Mike Corrigan\\Documents\\DMD Year 4 Sem 1\\FYP INFO\\fyp hugging face\\fypmc20277423\\output\\fine_tuned_gpt2_model')