Introduction
This project implements a Question Answering (QA) system using Natural Language Processing and a pre-trained Transformer model. The system takes a paragraph of text as context and answers natural language questions by extracting the most relevant answer from the given context.
Project Overview
The system is built using the Hugging Face Transformers library and a DistilBERT model fine-tuned on the SQuAD dataset. It demonstrates how Transformer-based models understand questions and retrieve accurate answers from unstructured text.
Technologies Used
- Python
- Hugging Face Transformers
- DistilBERT (distilbert-base-cased-distilled-squad)
- Google Colab
- Jupyter Notebook
Working Principle
- A context paragraph is provided as input
- A natural language question is asked
- The model processes the question and context together
- The most relevant answer span is returned with a confidence score
Example
Question: Who is known for the RKO?
Answer: Randy Orton
How to Run
- Open the notebook in Google Colab
- Run all cells sequentially
- Modify the context and questions to test different queries
Note: Notebook outputs are cleared for GitHub compatibility. Run the notebook in Google Colab to view full results.
Project Structure
- Question_Answering_NLP.ipynb
- README.md
Applications
- Question answering chatbots
- Information retrieval systems
- Educational applications
- Customer support systems
Future Enhancements
- Add a web-based interface
- Support multiple context documents
- Use larger or domain-specific QA models
- Implement voice-based question answering