'ClassyStandard' combines state-of-the-art Natural Language Processing (NLP) models with a sophisticated chatbot interface for effective text classification. The chatbot interacts with users, ensuring input standardization, and the processed data is then analyzed by BERT-based models. This approach addresses multi-column text classification problems, specifically focusing on the accurate prediction of a 'function' label.
- Project Overview
- Model Selection Process
- Chatbot Implementation
- Getting Started
- Contributing
- License
- Credits
The project encapsulates two primary components: a user-friendly chatbot interface for initial data interaction, and an ensemble of NLP models for data processing and text classification. These two components work in harmony to enhance the accuracy of 'function' label predictions in our multi-column text classification task.
The selection of the most suitable NLP model for our text classification task is a meticulous process. We've considered top-performing models such as BERT, RoBERTa, and DistilBERT. The selection process involves rigorous data analysis, preprocessing, model fine-tuning, and a comparative analysis to identify the model that delivers the best performance.
The front-end chatbot in 'ClassyStandard' serves as the user interface and the first point of data interaction. It standardizes user inputs by interacting actively with users and ensures clean data is fed into the BERT model. This way, it significantly improves the quality of data processed by the model, enhancing the overall accuracy of the system.
To get started with the 'ClassyStandard' project, clone the repository and follow the instructions in the installation guide. Please ensure your system meets the listed prerequisites for a smooth setup.
Contributions are welcomed! Please read our contributing guide for details on our code of conduct, and the process for submitting pull requests.
See the LICENSE.md file for details.
'ClassyStandard' is a project developed by SYS Labs. We extend our gratitude to the entire team for their effort and contributions. We also acknowledge the open-source community for the tools and frameworks that facilitated the realization of this project.