FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP
Abstrak
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 .
Topik & Kata Kunci
Penulis (6)
A. Akbik
Tanja Bergmann
Duncan A. J. Blythe
Kashif Rasul
Stefan Schweter
Roland Vollgraf
Akses Cepat
- Tahun Terbit
- 2019
- Bahasa
- en
- Total Sitasi
- 912×
- Sumber Database
- Semantic Scholar
- DOI
- 10.18653/v1/N19-4010
- Akses
- Open Access ✓