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Flair

Flair is a Python framework built on PyTorch for applying natural-language processing models to text, using text embeddings, and training or experimenting with NLP models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Flair — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Flair when an NLP workflow needs Python interfaces for pretrained text models, embeddings or model experimentation within a PyTorch-based framework.

Who it is for

  • Python developers adding NLP predictions to applications or analysis workflows.
  • Researchers and practitioners experimenting with NLP models and embeddings.
  • Teams working with text tasks such as entity recognition, tagging, sentiment analysis or biomedical text.

Documented uses

  • Identify named entities in sentences.
  • Classify text for sentiment or other supported categories.
  • Apply part-of-speech tagging, sense disambiguation and sequence-labeling models.
  • Use or combine word and document embeddings.
  • Train models and experiment with NLP approaches using PyTorch.
Before you choose

Requirements & limits

The project is open source; its repository displays an MIT license badge. Installation is documented with pip, and Python 3.9 or later is required.

  • Flair is a library and framework for developers, rather than a hosted text-analysis service.
  • It builds directly on PyTorch and supports applying models as well as training and experimentation.
  • The documented NLP scope includes named-entity recognition, sentiment analysis, part-of-speech tagging, classification, embeddings and biomedical text.
  • The repository README describes support for a growing number of languages and lists example NER models for English, German, Dutch and Spanish.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Flair and tag named entities

Install the framework and use its documented example to predict entity tags for a sentence.

Before you start

  • Python 3.9 or later
  • A Python virtual environment
  • pip

Steps

  1. Install Flair with the command below.

    Documented command
    pip install flair

  2. Import Sentence from flair.data and Classifier from flair.nn.

  3. Create a Sentence, load a classifier with Classifier.load('ner'), and call predict(sentence).

  4. Print the sentence to inspect its annotations.

Expected outcome

The README example tags “Berlin” as a location entity in “I love Berlin .”

Guide limitations

  • This recipe covers the README's example prediction flow, not model training or deployment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
12 October 2026
Original website title
flairNLP/flair · Official homepage title · retained in its source language
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