awesome-nlp is a curated list of resources dedicated to Natural Language Processing.
The project addresses the challenge of finding quality NLP resources by collecting and organizing materials across the field. It covers linguistic analysis, multilingual tooling, classical and neural methods, datasets, and evaluation approaches. The scope explicitly focuses on resources that advance core NLP tasks such as tokenization, multilinguality, machine translation, summarization, named entity recognition, question answering, factuality assessment, probing, and distillation. The list deliberately excludes general-purpose chatbots, agent frameworks, prompt-template repositories, code-generation tools, and RAG application starter kits, directing users to other curated lists for those categories instead.
Developers building NLP systems should consider this list when seeking foundational resources, datasets, or evaluation methods for specific linguistic tasks. It suits researchers and practitioners who need to understand the landscape of available tools and approaches rather than those looking for end-to-end application frameworks. The project's explicit scope boundaries mean it maintains focus on core NLP capabilities rather than attempting to cover the broader ecosystem of language model applications.
The project operates as a community-driven resource that welcomes contributions through pull requests, with documented contribution guidelines to maintain quality and consistency in the curated collection.
