Transformer Conversational Chatbot in Python using TensorFlow 2.0

Transformer Conversational Chatbot in Python using TensorFlow 2.0

Abstract:

This project describes a Python-TensorFlow 2.0 Transformer chatbot project. Chatbots are popular in artificial intelligence, but they struggle to understand user intent and provide accurate information. Deep learning and TensorFlow 2.0 are used to overcome these limitations. To improve its conversational skills, the chatbot will use the Cornell Movie-Dialogs Corpus dataset and Multi-Head Attention with Model sub-classing. The Functional API is used to build a Transformer model for performance.

Introduction:

Audio and text chatbots are becoming more popular. Existing chatbots often misinterpret user intent, resulting in frustrating interactions. This project uses deep learning and TensorFlow 2.0 to create a conversational chatbot to overcome these challenges.

Objectives:

This project aims to create a conversational chatbot that understands user intent and responds accurately. The chatbot can handle complex dialogues and improve user experience using the Transformer model and TensorFlow 2.0.

Project Details:

1. Dataset Selection:
   • The Cornell Movie-Dialogs Corpus will be used as the dataset for training the chatbot.
   • This dataset contains a large collection of dialogues from movies, providing a diverse range of conversational examples.
2. Deep Learning Architecture:
   • The project will utilize the Transformer model, a state-of-the-art architecture known for its effectiveness in natural language processing tasks.
   • Multi-Head Attention mechanism will be implemented using Model sub-classing to enhance the chatbot’s ability to capture contextual information and generate meaningful responses.
   • The Functional API of TensorFlow 2.0 will be employed to build the Transformer model, allowing for efficient training and improved performance.
3. Preprocessing and Training:
   • The Cornell Movie-Dialogs Corpus dataset will undergo preprocessing steps, including tokenization, padding, and data augmentation if necessary, to prepare it for training.
   • The Transformer model will be trained on the preprocessed dataset using appropriate loss functions and optimization algorithms.
   • The training process will involve fine-tuning the model parameters to improve its conversational capabilities.

Conclusion:

In conclusion, this project aims to create a Python Transformer model-based conversational chatbot using TensorFlow 2.0. Using the Cornell Movie-Dialogs Corpus dataset and Multi-Head Attention with Model sub-classing, the chatbot will understand user intentions and respond accurately. The project builds a functional API-based Transformer model. This project aims to improve chatbot conversation and user interactions in various domains.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.