Add named entity recognition to your app to automatically identify people, places, and organizations in text.
Analyze whether customer reviews or social media posts are positive or negative using built-in sentiment models.
Generate text embeddings to measure how similar two pieces of text are to each other.
Train a custom NLP model on your own labeled data using the PyTorch framework that Flair is built on.
Requires Python 3.9 or newer, install with a single pip command and models download automatically on first use.
Flair is a Python library for natural language processing, abbreviated as NLP. Natural language processing refers to software that reads, interprets, or analyzes human-written text. Flair was developed at Humboldt University of Berlin and is designed to make it straightforward to apply pre-built, research-grade language models to text data without needing to train those models from scratch. The library covers several core text analysis tasks. Named entity recognition identifies people, places, organizations, and other specific types of information within sentences. For example, given the sentence "I love Berlin," a named entity recognition model can automatically identify "Berlin" as a location. Sentiment analysis determines whether a piece of text is positive or negative in tone. Part-of-speech tagging labels each word by its grammatical role (noun, verb, adjective, and so on). Flair also supports more specialized tasks like word sense disambiguation (figuring out which meaning of an ambiguous word is intended), semantic role labeling, and biomedical text analysis for scientific and clinical content. A second major capability is text embeddings. An embedding is a mathematical way of representing words or entire documents as numbers, which allows models to measure similarity and relationships between pieces of text. Flair provides a simple interface for combining different embedding methods, including its own Flair embeddings and models from the transformer family, which are a class of neural network architectures commonly used in modern language models. The library builds on PyTorch, which is a widely used framework for developing and training machine learning models. This means developers can not only use Flair's pre-trained models out of the box but also train their own custom models using the same framework. Installation is a single pip command and requires Python 3.9 or newer. Many of Flair's models are also published on the Hugging Face model hub, where they can be browsed and tested interactively online.
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