Open source. Open possibilities.

Official code repository for 'Build a Large Language Model (From Scratch)': step-by-step PyTorch implementation of GPT-like models, covering pretraining and finetuning with notebooks and exercises.

DocsGPT is an open-source AI platform for building intelligent agents and assistants, featuring document analysis, multi-model support, and enterprise search capabilities.

A collection of Natural Language Processing experiments focusing on word vectors, transformers, and fundamental NLP algorithms.

Resume Matcher is an open-source AI tool that tailors resumes and cover letters for job applications using 100+ LLMs, with local or cloud options, templates, and multi-language support.

NLTK is a comprehensive suite of open-source Python modules, datasets, and tutorials designed to support research and development in Natural Language Processing.

A collection of machine learning and deep learning code examples and tutorials accompanying online courses. Covers topics from linear regression to reinforcement learning, NLP, computer vision, and generative AI.

HanLP is a production-ready multilingual NLP toolkit based on PyTorch and TensorFlow 2.x, supporting tokenization, POS tagging, named entity recognition, syntactic parsing, semantic analysis, and more, with both RESTful and native APIs.

Hugging Face Transformers is a model-definition framework for machine learning across text, vision, audio, and multimodal tasks. It provides a unified API for inference and training with over 1 million pretrained checkpoints.

A community-maintained catalog of Bangla (Bengali) NLP resources — 712 papers, 63 datasets, 20 models, and 9 tools across 13 tasks — built as a static Astro site with strict data verification and shareable filters.

TextBlob is a Python library for natural language processing, offering a simple API for sentiment analysis, part-of-speech tagging, noun phrase extraction, classification, tokenization, and more, built on NLTK and pattern.

Made With ML is an open-source course and codebase teaching how to design, develop, deploy and iterate on production-grade machine learning applications, covering MLOps, testing, serving, CI/CD and continual learning.