BookLensX: AI-Powered Book Intelligence System

> Transforming book data into intelligent insights using NLP and multi-task learning

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Overview

BookLensX is an advanced Natural Language Processing (NLP) system designed to analyze and generate insights from book data. It leverages transformer-based deep learning (T5) to perform multiple tasks simultaneously:

• 📖 Genre Classification
• ⭐ Rating Prediction
• 📝 Title Generation

This project demonstrates how multi-task learning with large language models can extract meaningful patterns from textual data and enhance recommendation systems.

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Key Features

• 🔹 Multi-task learning with a single T5 model
• 🔹 Automatic genre classification from descriptions
• 🔹 Predictive rating system (regression)
• 🔹 AI-generated book titles
• 🔹 Clean and scalable ML pipeline

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Model Architecture

• Model: T5-small (Text-to-Text Transfer Transformer)
• Approach: Multi-task learning via task-specific prompts

Example Input Format:

Task prefixes are used to guide the model:

• "classify genre: <description>"
• "predict rating: <description>"
• "generate title: <description>"

Why T5?

• Unified text-to-text framework
• Efficient for multi-task learning
• Strong performance on NLP tasks

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Results

Task	Metric	Score
Genre Classification	Accuracy	70%
Rating Prediction	RMSE	2.99
Title Generation	BLEU	4.00

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Example Output

Input:

"A young wizard embarks on a journey to defeat a dark lord."

Model Output:

• Genre → Fantasy
• Rating → 4.5
• Title → The Wizard’s Destiny

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🛠 Tech Stack

• Python
• PyTorch
• Hugging Face Transformers
• Pandas / NumPy
• Scikit-learn

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Project Structure

BookLensX/
│
├── data/               # Dataset files
├── notebooks/          # EDA & experimentation
├── src/                # Core scripts
├── models/             # Trained models
├── results/            # Evaluation outputs
├── README.md
└── requirements.txt

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⚙️ Installation

git clone https://github.com/datazenith-labs/BookLensX-NLP-DS-Project.git
cd BookLensX-NLP-DS-Project
pip install -r requirements.txt

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▶️ Usage

Run training:

python train.py

Run inference:

python inference.py

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📈 Future Improvements

• 🔹 Deploy as a web app (Streamlit / FastAPI)
• 🔹 Fine-tune larger models (T5-base, FLAN-T5)
• 🔹 Add recommendation system
• 🔹 Improve evaluation with human feedback

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Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

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About

Developed as part of a Data Science & NLP project at HAW Hamburg. Focused on building real-world AI applications using modern LLM techniques.

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⭐ Acknowledgements

• Hugging Face 🤗
• Open-source NLP community

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