This repository provides a comprehensive collection of Natural Language Processing (NLP) tutorials using TensorFlow 2.0+. It targets developers and researchers looking to learn and implement modern NLP techniques, from text preprocessing to advanced models like BERT, GPT, and LLMs, offering practical code examples and theoretical explanations.

How It Works

The project leverages TensorFlow 2.0+ for its deep learning implementations, covering a wide spectrum of NLP tasks. It includes practical code for text classification, named entity recognition, question answering, natural language inference, chatbots, keyword extraction, topic modeling, and LLM fine-tuning. The tutorials are designed to be runnable in Google Colab, eliminating the need for local Python or TensorFlow installations.

Quick Start & Requirements

• Colab Integration: Each tutorial's Python file contains a link to a Google Colab notebook. Accessing this link in Chrome allows immediate execution without local setup.
• Dependencies: TensorFlow 2.0+ is the primary framework. Specific model implementations may require additional libraries like Hugging Face Transformers, KeyBERT, and BERTopic.

Highlighted Details

• Covers text preprocessing, topic models, BERT, GPT, and LLMs.
• Includes practical implementations for text classification, NER, QA, NLI, chatbots, keyword extraction, and topic modeling.
• Features recent additions for LLM fine-tuning and KoGPT-2 text generation.
• Theoretical underpinnings are detailed in a 1,000-page e-book.

Maintenance & Community

The repository was opened on January 1, 2022, with significant updates throughout 2022 and a recent update in February 2024 adding LLM fine-tuning. A PyTorch version of the tutorials is also available.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should verify licensing for commercial or closed-source use.

Limitations & Caveats

The README does not specify a license, which may impact commercial adoption. While Colab integration simplifies execution, users requiring local setups will need to manage TensorFlow and other library dependencies.