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        <title>FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP</title>
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    <abstract>We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models. The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings. This effectively hides all embedding-specific engineering complexity and allows researchers to “mix and match” various embeddings with little effort. The framework also implements standard model training and hyperparameter selection routines, as well as a data fetching module that can download publicly available NLP datasets and convert them into data structures for quick set up of experiments. Finally, FLAIR also ships with a “model zoo” of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications. This paper gives an overview of the framework and its functionality. The framework is available on GitHub at https://github.com/zalandoresearch/flair .</abstract>
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%0 Conference Proceedings
%T FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP
%A Akbik, Alan
%A Bergmann, Tanja
%A Blythe, Duncan
%A Rasul, Kashif
%A Schweter, Stefan
%A Vollgraf, Roland
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F akbik-etal-2019-flair
%X We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models. The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings. This effectively hides all embedding-specific engineering complexity and allows researchers to “mix and match” various embeddings with little effort. The framework also implements standard model training and hyperparameter selection routines, as well as a data fetching module that can download publicly available NLP datasets and convert them into data structures for quick set up of experiments. Finally, FLAIR also ships with a “model zoo” of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications. This paper gives an overview of the framework and its functionality. The framework is available on GitHub at https://github.com/zalandoresearch/flair .
%R 10.18653/v1/N19-4010
%U https://aclanthology.org/N19-4010
%U https://doi.org/10.18653/v1/N19-4010
%P 54-59

Markdown (Informal)

[FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP](https://aclanthology.org/N19-4010) (Akbik et al., NAACL 2019)

• FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP (Akbik et al., NAACL 2019)

ACL

• Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 54–59, Minneapolis, Minnesota. Association for Computational Linguistics.