Embedding_AI-in-Finance

Author: Tianyi Zhu

Project Overview

This project focuses on fine-tuning the BERT model for text classification tasks, specifically using business descriptions as input data. The implementation includes data preprocessing, tokenization, dataset splitting, and fine-tuning BERT for classification.

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Table of Contents

1. Project Workflow
2. Requirements
3. Installation
4. Usage
5. File Structure
6. Results
7. License

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Project Workflow

Step 1: Data Preprocessing

• Clean and process the input data (business descriptions).
• Tokenize text using Hugging Face Transformers library.

Step 2: Dataset Splitting

• Split the dataset into training, validation, and test sets for model evaluation.

Step 3: Fine-Tuning BERT

• Load the pretrained BERT model.
• Fine-tune the model for sequence classification tasks using PyTorch and Hugging Face.

Step 4: Evaluation and Results

• Evaluate the fine-tuned model on test data.
• Visualize the results using matplotlib and seaborn.

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Requirements

Ensure you have the following Python libraries installed:

• transformers
• torch
• numpy
• pandas
• matplotlib
• seaborn
• tqdm

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Installation

1. Clone this repository:

   git clone https://github.com/your-username/AS12-BERT-Classification.git
   cd AS12-BERT-Classification

2. Install the required dependencies:

   pip install -r requirements.txt

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Usage

1. Run the notebook:
   Open the Jupyter Notebook AS12.ipynb in a Jupyter environment:

   jupyter notebook AS12.ipynb

2. Steps in the Notebook:

   • Data preprocessing
   • Tokenization
   • Fine-tuning BERT
   • Model evaluation and results

3. Input Data: Ensure you have the business description data in the correct format.

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File Structure

AS12-BERT-Classification/
│-- AS12.ipynb              # Main Jupyter Notebook for implementation
│-- data/                   # Folder to store input data
│-- results/                # Folder to save outputs and visualizations
│-- models/                 # Folder to save fine-tuned models
│-- README.md               # Project documentation
│-- requirements.txt        # Required dependencies

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Results

• Results of fine-tuning the BERT model, including evaluation metrics, are documented in the notebook.
• Visualizations include confusion matrices and classification accuracy plots.

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