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FLAIR

State-of-the-art NLP, incredibly simple

LGPL-3.0

ABOUT

Applying state-of-the-art NLP to real-world documents requires juggling multiple models, tokenizers, and pipelines — each with their own API and dependencies. Flair provides a unified framework that wraps modern transformer models (BERT, RoBERTa, XLNet) alongside its own contextual string embeddings, with a consistent API for common NLP tasks like named entity recognition, text classification, and sequence labeling, all accessible in just a few lines of Python.

INSTALL
pip install flair

INTEGRATION GUIDE

1. Extract named entities (people, organizations, locations) from legal documents and contracts 2. Classify customer support tickets into intent categories with pre-trained transformer models 3. Build text embedding pipelines for semantic search and document similarity 4. Train custom sequence labeling models for domain-specific entity extraction 5. Power NLP preprocessing pipelines for downstream LLM applications

TAGS

nlpnamed-entity-recognitiontext-classificationsequence-labelingembeddingspytorch