A very simple framework for state-of-the-art Natural Language Processing (NLP).
A 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), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.
A text embedding library. Flair has simple interfaces that allow you to use and combine different word and document embeddings, including the proposed Flair embeddings, BERT embeddings and ELMo embeddings.
A PyTorch NLP framework. The framework builds directly on PyTorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.
module spider flairNLP to find out what environment modules are available for this application.
- HPC_FLAIRNLP_DIR - installation directory
If you publish research that uses flairNLP you have to cite it as follows:
Please cite the following paper when using Flair embeddings
If you use the Flair framework for your experiments, please cite this paper
If you use our new "FLERT" models or approach, please cite this paper
If you use our TARS approach for few-shot and zero-shot learning, please cite this paper