Supervised Fine-Tuning with Financial Sentiment Analysis

This project demonstrates how to perform supervised fine-tuning on language models using financial sentiment data on CPU.

Overview

The notebook Supervised_Fine_Tuning_With_FinancialSentiment_on_CPU.ipynb implements a fine-tuning pipeline for sentiment analysis in financial texts. It's designed to run on CPU environments, making it accessible without requiring specialized hardware.

Prerequisites

• Python 3.8+
• PyTorch
• Transformers
• pandas
• numpy

pip install torch transformers pandas numpy scikit-learn

Dataset

The project uses a financial sentiment dataset containing:

• Financial text/news
• Sentiment labels (positive, negative, neutral)

Features

• Data preprocessing for financial texts
• Model fine-tuning configuration for CPU
• Sentiment classification training

Usage

1. Open the notebook in Jupyter or Google Colab
2. Install required dependencies
3. Load and preprocess the financial sentiment dataset
4. Configure and run the fine-tuning process

Model Architecture

The notebook uses a pre-trained transformer model fine-tuned for sentiment classification tasks.