This repository provides a comprehensive collection of Jupyter notebooks and code samples covering a wide range of Natural Language Processing (NLP) concepts and applications. It's designed for students, researchers, and practitioners looking to learn and experiment with various NLP techniques, from fundamental tokenization to advanced transformer models and diverse application areas like sentiment analysis, machine translation, and question answering.

How It Works

The project explores NLP through a structured curriculum, detailing core concepts like tokenization, word embeddings (Word2Vec, GloVe, ELMo), and recurrent neural networks (RNN, LSTM, GRU). It then delves into advanced architectures such as attention mechanisms, Transformers, GPT-2, and BERT. The notebooks demonstrate practical implementations across various NLP tasks, including classification, generation, clustering, question answering, and ranking, often showcasing multiple model variants and performance improvements.

Quick Start & Requirements

• Installation: Primarily uses Jupyter notebooks, often run via Google Colab.
• Prerequisites: Python, standard NLP libraries (e.g., Spacy, Torchtext, Hugging Face Transformers), and potentially GPU access for larger models.
• Resources: Setup time varies based on model complexity; larger models like BERT and Transformers require significant computational resources.
• Links: The README itself serves as a detailed guide to the covered topics and their implementations.

Highlighted Details

• Extensive coverage of foundational NLP concepts and modern deep learning architectures.
• Practical implementations for diverse applications: sentiment analysis, machine translation, NER, image captioning, and more.
• Demonstrates performance improvements through techniques like attention, pre-trained embeddings, and model ensembling.
• Includes exploration of code-mixed language processing (Hinglish sentiment analysis) and specialized tasks like LaTeX equation generation.

Maintenance & Community

The repository is maintained by graviraja. Suggestions and feedback are encouraged via GitHub issues.

Licensing & Compatibility

The repository does not explicitly state a license. Users should verify compatibility for commercial or closed-source use.

Limitations & Caveats

While comprehensive, the project focuses on demonstrating various techniques rather than providing a production-ready framework. Some implementations might require specific dataset downloads or environment configurations not fully detailed. The difficulty level is subjective, and some advanced topics may require a strong foundational understanding.