Text Processing
MOA-NLP-Toolkit
The MOA-NLP-Toolkit is an interactive web application built using Streamlit, showcasing advanced natural language processing capabilities through the Mixture of Agents (MoA) approach. It integrates cutting-edge models from the Hugging Face Transformers library to perform tasks like text summarization, question-answering, and text generation, making NLP tools accessible and intuitive for users.
Overview
Delivering a user-friendly application that combines multiple NLP functionalities, such as summarization, question-answering, and text generation, required integrating advanced AI models while ensuring seamless user interactions.
The MOA-NLP-Toolkit was developed as a Streamlit-based application, providing users with a cohesive interface to interact with specialized NLP models. By leveraging the MoA approach, the application seamlessly integrates text summarization, question-answering, and text generation functionalities into a single platform.
Research
The project explored the Mixture of Agents (MoA) approach, which employs a combination of specialized NLP models to handle diverse language tasks. It utilized state-of-the-art models like GPT-2 and DistilBERT from the Hugging Face Transformers library to balance performance and resource efficiency.
Design
The MOA-NLP-Toolkit is designed with simplicity and efficiency, focusing on delivering a seamless user experience. The application features:
A Streamlit-powered interface that allows users to select tasks (summarization, question-answering, or text generation) from a sidebar.
Input fields tailored for each task, such as a text area for input text and an additional question field for question-answering.
Real-time processing feedback with a spinner to indicate ongoing task execution.
Example data for each task provided in the sidebar to guide users through the functionalities.
Results
Successfully integrated multiple NLP models from Hugging Face Transformers into a single web-based application.
Achieved high accuracy and reliable performance for tasks like summarization, question-answering, and text generation.
Delivered a responsive and user-friendly interface, allowing users to interact with advanced NLP functionalities effortlessly.
Enabled real-time processing, ensuring efficient task execution even with large input data.
Enhanced accessibility to NLP tools for a broad audience, from beginners to advanced users.
