NLP Enhanced Emailer

Table of Contents

• Overview
• Why DistilBERT and GPT-2?
  • DistilBERT
  • GPT-2
• How It Works
• Benefits of Combining DistilBERT and GPT-2
• Efficiency and Data
• Prerequisites
• Improvements and Future Work
  • GUI Application
• Contributor

Overview

NLP Enhanced Emailer is a tool designed to automate the process of email synthesis using state-of-the-art Natural Language Processing (NLP) techniques. This project integrates two powerful models—DistilBERT and GPT-2—to analyze user input and generate contextually appropriate emails, making communication more efficient and effective.

Why DistilBERT and GPT-2?

DistilBERT

DistilBERT is a smaller, faster, and lighter version of BERT, designed for sequence classification tasks. In this project, DistilBERT is used for:

• Intent Classification: Understanding the purpose of the user's input. For instance, whether the email should be a request, a complaint, or an informational message.
• Tone Classification: Identifying the tone of the email, such as formal, informal, polite, or urgent.

The benefits of using DistilBERT include its efficiency and reduced computational resource requirements, making it ideal for real-time applications where speed and performance are crucial.

GPT-2

GPT-2 is a powerful language model known for its ability to generate coherent and contextually relevant text. In this project, GPT-2 is employed to:

• Email Generation: Crafting well-structured and contextually appropriate email content based on the analyzed intent and tone from DistilBERT.

GPT-2's capability to generate human-like text ensures that the output is not only relevant but also polished and professional.

How It Works

1. User Input: The user provides a prompt that outlines the content and purpose of the email.
2. Intent and Tone Analysis: DistilBERT analyzes the prompt to classify its intent and tone.
3. Email Generation: GPT-2 uses the classified intent and tone to generate a complete email draft based on the provided prompt.

Benefits of Combining DistilBERT and GPT-2

Merging DistilBERT and GPT-2 leverages the strengths of both models:

• Enhanced Accuracy: DistilBERT’s fine-grained classification of intent and tone allows GPT-2 to generate more accurate and contextually relevant emails.
• Efficiency: DistilBERT’s efficiency complements GPT-2’s powerful text generation capabilities, resulting in a system that is both fast and capable of producing high-quality text.
• Seamless Integration: The combination of intent and tone classification with sophisticated text generation ensures that emails are not only contextually appropriate but also tailored to the specific needs and style of the user.

Efficiency and Data

The efficiency and accuracy of the models improve as they are trained on more data. For this project, we used a synthetic dataset consisting of approximately 200 email examples. The use of a larger and more diverse dataset would likely enhance the model’s performance further.

Training the models for more epochs also contributes significantly to their efficiency. By increasing the number of epochs, the models can learn more effectively from the data, improving both classification accuracy and text generation quality.

Prerequisites

Before running the project, ensure you have the following installed:

• Python 3.x
• PyTorch
• Transformers library
• Scikit-learn
• Pandas

Improvements and Future Work

GUI Application

One significant enhancement planned for the NLP Enhanced Emailer is the development of a graphical user interface (GUI) application. This application will:

• Provide a user-friendly interface for inputting email prompts.
• Allow users to directly send emails using SMTP or similar protocols, streamlining the process from generation to delivery.

This GUI app will make the tool more accessible and practical for everyday use, offering a complete solution from email creation to sending.

Contributor

This project is developed by B M Manohara @Manohara-Ai