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.
- 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.
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