- Pre-trained models for common NLP tasks, such as named entity recognition, part-of-speech tagging, sentiment analysis, text classification, and more.
- A simple and flexible interface to combine different models and embeddings, such as word embeddings, character embeddings, flair embeddings, and transformer embeddings.
- A modular and extensible design that lets you create your own models and embeddings, or use custom datasets and corpora.
- A fast and efficient implementation that leverages PyTorch's GPU support and caching mechanisms.
Flair is an open-source project that is developed and maintained by a community of researchers and developers. You can find the source code, documentation, tutorials, and examples on GitHub: https://github.com/flairNLP/flair
