🧠 Building and Deploying a Question Answering System with Hugging Face

📌 Project Overview

This project implements an Extractive Question Answering (QA) System using Hugging Face Transformers and Streamlit. While the QA system is extractive in nature, it is built on a transformer-based Large Language Model (LLM) and is inspired by advances in Natural Language Processing (NLP) and Generative AI (GenAI). A pre-trained DistilBERT model was fine-tuned on the SQuAD dataset to improve accuracy in extracting answers from a given context. The final model was deployed as an interactive Streamlit web application for real-time user interaction.

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🚀 Project Workflow

✅ Step 1: Dataset Preparation

• Loaded and preprocessed the SQuAD dataset for fine-tuning.
• Extracted context, questions, and answers, ensuring accurate start-end token mappings.
• Converted the data into Hugging Face Dataset format for training.
• Saved processed data as train_data.csv and validation_data.csv for reuse.

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✅ Step 2: Baseline Model Evaluation (Pretrained DistilBERT)

• Loaded train_data.csv and validation_data.csv for model benchmarking.
• Used DistilBERT (distilbert-base-cased) for zero-shot QA evaluation.
• Evaluated performance before fine-tuning to establish baseline metrics.
• Observed limitations in handling domain-specific or complex queries.
• Saved the baseline model as baseline_model.

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✅ Step 3: Fine-Tuning the DistilBERT Model

• Loaded the baseline_model and fine-tuned it using the Hugging Face Trainer API.
• Used AdamW optimizer with learning rate scheduling.
• Configured batch size, epochs, and gradient accumulation for optimal performance.
• Trained the model on SQuAD dataset and saved the best version as best_fine_tuned_model.

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✅ Step 4: Model Evaluation

• Compared baseline vs fine-tuned model performance.
• Evaluated using Exact Match (EM) and F1 Score.
• Achieved significant improvements post fine-tuning:
  • EM: 74.50%
  • F1: 83.07%
• Generated evaluation_report.json summarizing all key metrics.
• Visualized improvement using bar charts via matplotlib.
• Saved incorrect predictions for further error analysis.

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✅ Step 5: Deployment via Streamlit

• Developed a Streamlit web app for real-time QA interaction.
• Integrated best_fine_tuned_model to power the backend inference.
• Enabled users to input both context and question for on-the-fly answers.
• Implemented robust error handling for invalid inputs and edge cases.
• Tested thoroughly with real-world examples for stability and usability.

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📊 Final Results: Performance Comparison

Model	Exact Match (EM)	F1 Score
Baseline Model	71.75%	80.65%
Fine-Tuned Model	74.50%	83.07%

✅ Fine-tuning improved EM by ~3% and F1 Score by ~2.5%
✅ Real-time QA interaction successfully enabled through the deployed UI

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✅ Conclusion

This project demonstrates the complete lifecycle of building an NLP solution—from dataset preparation and model fine-tuning to deployment and evaluation. Using Hugging Face Transformers and PyTorch, we improved a DistilBERT model’s performance on SQuAD and deployed it via Streamlit, making it accessible for real-time use cases such as knowledge assistants, intelligent search engines, and chatbot integrations. By leveraging techniques from Generative AI, the project highlights the real-world application of Large Language Models (LLMs) in building intelligent systems capable of natural language understanding and interactive deployment.

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🛠️ Technologies & Tools

• Hugging Face Transformers
• PyTorch
• Hugging Face Datasets
• Streamlit
• SQuAD Dataset
• Matplotlib, Pandas
• JSON, CSV, Tokenizers

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📂 Project Structure

📂 Project Structure
├── data/
│   ├── train_data.csv
│   └── validation_data.csv
├── models/
│   ├── baseline_model/
│   └── best_fine_tuned_model/
├── evaluation/
│   ├── evaluation_report.json
│   └── performance_plots.png
├── app.py               # Streamlit Application
├── train.py             # Fine-tuning Script
├── requirements.txt
└── README.md

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📖 Sample Dataset Entry

{
  "context": "The Amazon rainforest is one of the world's most biodiverse habitats. It plays a critical role in regulating the global climate.",
  "question": "What role does the Amazon rainforest play in the climate?",
  "answer": "regulating the global climate"
}

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📚 Learning Outcomes

• Fine-tuning pre-trained transformer models for extractive QA
• Understanding tokenization and label alignment for QA
• Evaluating NLP models using EM and F1 metrics
• Building and deploying interactive ML apps using Streamlit
• Exposure to Hugging Face's ecosystem and Trainer API
• Gained practical experience with Large Language Models (LLMs) and exposure to the foundations of Generative AI (GenAI) by fine-tuning and deploying a transformer-based question answering system.