flair – simple framework for state-of-the-art NLP

flair is a very simple framework for state-of-the-art Natural Language Processing (NLP) 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

The project is based on PyTorch 0.4+ and Python 3.6+.

The software is released under an open source license.

Features include:

  • Powerful NLP library. Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification.
  • Ships with a “model zoo” of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications.
  • Multilingual. Thanks to the Flair community, there’s support for a rapidly growing number of languages.  The project also includes ‘one model, many languages’ taggers, i.e. single models that predict PoS or NER tags for input text in various languages.
  • A text embedding library. Flair has simple interfaces that allow you to use and combine different word and document embeddings, including our proposed Flair embeddings, BERT embeddings and ELMo embeddings.
  • A Pytorch NLP framework. Our framework builds directly on Pytorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.

Website: github.com/zalandoresearch/flair
Support: Tutorials
Developer: Zalando Research
License: MIT License

flair is written in Python. Learn Python with our recommended free books and free tutorials.

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