Introducing Flair

Flair is a powerful NLP framework published as a Python package. It provides a simple interface that is friendly, easy to use, and caters to people from various backgrounds including those with little prior knowledge in programming. It is published under the MIT License, which is one of the most permissive free software licenses.

Flair as an NLP framework comes with a variety of tools and uses. It can be defined in the following ways:

• It is an NLP framework used in NLP research for producing models that achieve state-of-the-art results across many NLP tasks such as POS tagging, NER, and chunking across several languages and datasets. In Flair's GitHub repository, you will find step-by-step instructions on how to reproduce these results.
• It is a tool for training, validating, and distributing NER, POS tagging, chunking, word sense disambiguation, and text classification models. It features tools that help ease the training and validation processes such as the automatic corpora downloading tool, and tools that facilitate model tuning such as the hyperparameter optimization tool. It supports a growing number of languages.
• It is a tool for downloading and using state-of-the-art pre-trained models. The models are downloaded seamlessly, meaning that they will be automatically downloaded the first time you use them and will remain stored for future use.
• It is a platform for the proposed state-of-the-art Flair embeddings. The state-of-the-art results Flair achieves in many NLP tasks can by and large be attributed to its proposed Flair contextual string embeddings described in more detail in the paper Contextual String Embeddings for Sequence Labeling. The author refers to them as "the secret sauce" of Flair.
• It is an NLP framework for working with biomedical data. A special section of Flair is dedicated solely to working with biomedical data and features a set of pretrained models that achieve state-of-the-art results, as well as a number of corpora and comprehensive documentation on how to train custom biomedical tagging models.
• It is a great practical introduction to NLP. Flair's extensive online documentation, simple interface, inclusive support for a large number of languages, and its ability to perform a lot of the tasks on non-GPU-equipped machines all make it an excellent entry point for someone aiming to learn about NLP through practical hands-on experimentation.