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This project employs fine-tuning techniques on LLM such as LLamas 2, GPT to develop a specialized Q&A chatbot for enhancing customer services.

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Finetune Q&A LLM Chatbot using Amazon Q&A Dataset

link to Colab: Open In Colab

This repository contains the code for a Q&A chatbot application leveraging the GPT-2 model. It is designed to provide intelligent, conversational responses to user queries, making it suitable for applications like customer service, information retrieval, and interactive dialogue systems.

Features

  • GPT-2 and BERT Integration: Utilizes the GPT-2 medium variant for generating conversational responses and BERT for question answering capabilities.
  • Custom Dataset Handling: Includes scripts for processing and preparing custom datasets for training.
  • Efficient Training: Implements a training pipeline using PyTorch and the Hugging Face Transformers library.
  • Modular Code Structure: Easy to understand and modify to suit different use-cases or to experiment with different models.

Prerequisites

  • Python 3.8 or higher
  • PyTorch
  • Hugging Face's Transformers library
  • Other dependencies listed in requirements.txt

Installation

Clone the repository and install the required packages:

git clone https://github.com/ducanhho2296/LLM_CustomerServiceChatbot.git
cd LLM_CustomerServiceChatbot
pip install -r requirements.txt

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This project employs fine-tuning techniques on LLM such as LLamas 2, GPT to develop a specialized Q&A chatbot for enhancing customer services.

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