Flair is a popular open-source framework for state-of-the-art natural language processing, developed at Humboldt University of Berlin. It packages strong pretrained models and an unusually simple API for tasks like named entity recognition, part-of-speech tagging, and text classification. The project has earned more than 14,000 stars on GitHub and remains actively maintained, with releases continuing into 2026.
Flair works on three levels. As an NLP library, it applies ready-made models to your text: NER, sentiment analysis, PoS tagging, word sense disambiguation, and classification, with growing multilingual coverage and special support for biomedical text through the HunFlair models. As an embedding library, it offers simple interfaces to use and stack different word and document embeddings, including its own contextual string embeddings, known as Flair embeddings, alongside transformer models. And as a PyTorch framework, it gives researchers the building blocks to train their own sequence labeling and classification models with little boilerplate. Its pretrained NER models sit near the top of standard leaderboards, scoring above 94 F1 on CoNLL-03 English and strong results on OntoNotes and German benchmarks, and models are distributed through Hugging Face with demos you can try immediately.
Flair is completely free and open source. There is no commercial edition, hosted tier, or usage fee: install it with pip and use the models and framework in research or production as the license allows.
Flair is ideal for NLP practitioners and researchers who need dependable entity extraction or text classification without a large engineering effort: information extraction pipelines, document processing, biomedical text mining, and university coursework. Its shallow learning curve makes it a friendly first serious NLP framework.