This project demonstrates the process of fine-tuning the GPT-2 model for sentiment analysis on movie reviews. Utilizing the IMDb dataset, we adjust GPT-2, a model originally designed for text generation, to classify text inputs into positive or negative sentiment categories. This README outlines the steps to prepare the data, fine-tune the model, evaluate its performance, and compare the fine-tuned model against the original GPT-2 model.

• Python 3.8 or later
• PyTorch
• Transformers library by Hugging Face
• Datasets library by Hugging Face
• Accelerate library for efficient training

First, ensure that Python and pip are already installed. Then, install the necessary Python packages using the following command:

pip install torch transformers datasets accelerate

The IMDb dataset is used, comprising movie reviews labeled as either positive or negative. The dataset is split into training, validation, and test sets, with labels encoded as 0 (negative) and 1 (positive).

Data Preparation: Load the IMDb dataset and prepare it for training by tokenizing the text data and encoding the labels.

Model Fine-tuning: Utilize the Trainer class from the Transformers library to fine-tune the GPT-2 model on the sentiment analysis task.

Evaluation: Assess the performance of the fine-tuned model on the test dataset, calculating metrics such as accuracy, precision, recall, and F1 score.

Comparison: Compare the performance of the fine-tuned GPT-2 model against the original, pre-trained GPT-2 model to understand the impact of fine-tuning.

To execute the project, run the Jupyter Notebook script provided, ensuring that the dataset path and other configurations are correctly set according to your environment.

The project includes code for visualizing the performance comparison between the original and fine-tuned models, showcasing the effectiveness of the fine-tuning process.

This project highlights the adaptability of pre-trained models like GPT-2 for specific tasks such as sentiment analysis, demonstrating the power of fine-tuning in achieving significant improvements in model performance.