🧠 Natural Language Processing (NLP) Projects

Welcome to my Natural Language Processing (NLP) repository! 🚀
This space showcases a variety of projects where I explore and implement NLP techniques using Python and popular NLP libraries. Each project focuses on a specific aspect of NLP, offering hands-on examples and insights.

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👤 Contributor

• efeecllk

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⚙️ Technologies Used

This repository utilizes a diverse set of technologies, with plans to expand further in future projects:

🐍 Programming Language:

• Python 3.12

📚 NLP Libraries:

• SpaCy
• Scikit-learn
• NLTK
• Gensim

📊 Visualization Tools:

• Matplotlib
• Seaborn

🤖 Frameworks & Tools:

• LangChain
• Gradio

🛠️ Supporting Libraries:

• NumPy
• Pandas

🚀 Now Available:

• ✅ Hugging Face Transformers - BERT fine-tuning and inference
• ✅ PyTorch - Deep learning model implementations
• ✅ FastAPI - Production-ready API deployment
• ✅ Gradio - Interactive web demos
• ✅ Testing & CI/CD - Automated quality assurance

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🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/EudaLabs/nlp.git
cd nlp

# Install dependencies
pip install -r requirements.txt

Run Your First Demo

# Try sentiment analysis with Gradio
python -m gradio_demos.sentiment_analysis

# Or start the FastAPI server
python -m fastapi_deployment.app

Train Your First Model

# Train BERT for sentiment analysis
cd bert_classification
python -m bert_classification.train \
    --dataset imdb \
    --epochs 3 \
    --batch-size 16 \
    --output-dir ./models/bert-imdb

Run Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=. --cov-report=html

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📂 Project Structure

Each project folder includes:

• 📄 README.md: Detailed explanation of the project objectives, methodologies, and findings.
• 📝 Code Files: Python Scripts (.py) and Jupyter Notebooks (.ipynb) for reproducibility.
• 📊 Datasets (if applicable): Preprocessed and/or raw data used in the project.
• ✅ Tests: Unit tests to ensure code quality and correctness.

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🗺️ Expansion Roadmap

This repository is continuously growing from 31 projects to 100+ over 9 months!

📚 Complete Documentation Suite:

• 📋 Quick Summary - TL;DR of expansion plans (Start here!)
• 🎯 Immediate Priorities - Top 10 priorities + 30-day action plan
• 🗺️ Detailed Roadmap - Complete 10-phase expansion plan (38 weeks)
• 📊 Visual Overview - Diagrams, metrics, and priority matrices
• 🚀 Getting Started Guide - How to implement the roadmap

🎯 Top Priorities:

1. Testing Infrastructure (pytest, coverage)
2. CI/CD Pipeline (GitHub Actions)
3. BERT Text Classification
4. Named Entity Recognition
5. Question Answering System
6. FastAPI Model Deployment
7. Advanced RAG Enhancements
8. Text Generation Projects
9. Multilingual NLP Support
10. Evaluation Framework

📈 Coming in Next 3 Months:

• ✅ Advanced Transformer implementations (BERT, GPT, T5)
• ✅ Production deployment examples (FastAPI, Docker)
• ✅ Comprehensive testing infrastructure (>80% coverage)
• ✅ PyTorch & TensorFlow projects
• ✅ Model optimization techniques
• ✅ Evaluation and benchmarking tools

🌟 Long-term Vision (9 months):

• Multilingual NLP projects
• Speech and audio processing
• Domain-specific applications (Healthcare, Legal, Finance)
• MLOps best practices
• Research paper implementations
• Active community of contributors

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📊 Current Projects

🆕 Advanced Transformers & Text Generation

• T5 Text Generation - Multi-task text-to-text transformer for summarization, translation, and paraphrasing
• GPT-2 Fine-tuning - Text generation and completion with customizable training
• BERT Text Classification - Fine-tuning BERT for sentiment analysis and multi-class classification
• Training & Inference Pipeline - Complete implementation with evaluation metrics
• Configurable Architecture - Easy-to-use configuration classes

🎯 Specialized NLP Tasks

• Question Answering System - Extractive QA with BERT, RoBERTa, and SQuAD support
• Advanced QA Features - Batch processing, confidence scoring, and multi-document QA
• Named Entity Recognition - Multi-backend NER with SpaCy and BERT

📊 Model Evaluation Framework

• Classification Metrics - Accuracy, Precision, Recall, F1, ROC-AUC, confusion matrices
• Generation Metrics - BLEU, ROUGE, METEOR for text generation evaluation
• QA Metrics - Exact Match and F1 scoring for question answering
• NER Metrics - Token and entity-level evaluation with per-class metrics
• Visualization Tools - Confusion matrices, ROC curves, and model comparisons

🚀 Production Deployment

• FastAPI Model Serving - RESTful API for model inference with Docker support
• Health Checks & Monitoring - Production-ready endpoints with metrics
• Batch Processing - Efficient batch prediction support

🎨 Interactive Demos

• Gradio Applications - Web-based demos for sentiment analysis, text classification, and QA
• Zero-Shot Classification - Classify into custom categories without training
• Question Answering - Extractive QA with pre-trained models

⚙️ Infrastructure & Testing

• Testing Framework - pytest configuration with coverage reporting
• CI/CD Pipeline - GitHub Actions for automated testing and quality checks
• Pre-commit Hooks - Code formatting and linting automation
• Docker Support - Containerized deployment examples

Basic Text Processing

• Bag of Words implementation
• Lemmatization techniques
• Part-of-Speech tagging
• Similarity measures (Cosine, Euclidean)
• Spam mail detection

Word Embeddings

• Word2Vec implementations
• Custom embedding model training

SpaCy Projects

• Named Entity Recognition
• Document classification
• Text summarization
• Data preparation pipelines
• Visualization tools

LangChain & LLMs

• Python code debugger with Llama3
• Chain operations
• RAG (Retrieval-Augmented Generation) system

Hugging Face Transformers

• Sentiment analysis applications

Agentic AI

• Research assistant
• RAG with vector databases
• MLflow integration

Recommendation Systems

• Book recommendation engine

Summarization

• Sequence-to-sequence models
• Neural summarization training

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🤝 Contributing

Contributions are welcome and encouraged! 🚀
If you'd like to:

• Add a new project
• Improve an existing project
• Fix bugs or enhance documentation

📥 Please check out the Contribution Guidelines before submitting your pull request.

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📢 Stay Connected

• ⭐ Star this repository if you find it useful.
• 🗨️ Share feedback and suggestions via Issues.
• 🔔 Follow for updates on new projects and improvements.

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Let’s dive into the world of Natural Language Processing and build something amazing together! 🌟