Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts)
Official code for ACL 2024 paper: VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models.
Flair is a transformer-based natural language processing framework used to build and train models for text classification and sequence tagging. It provides a specialized library for generating contextual text embeddings and performing linguistic analysis. The framework includes dedicated tools for named entity recognition, including the identification of specialized biomedical entities across multiple languages. It further supports entity linking to map identified text mentions to unique entries within general or biomedical knowledge bases. The project covers a broad range of language analys
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, Hoifung Poon (*Equal Contribution)
Die Hauptfunktionen von universal-ner/universal-ner sind: Cross-Domain Learning, Instruction Tuning, Named Entity Recognition.
Open-Source-Alternativen zu universal-ner/universal-ner sind unter anderem: yyding1/gner — Rethinking Negative Instances for Generative Named Entity Recognition. urchade/gliner — Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts). emseoyk/verifiner — Official code for ACL 2024 paper: VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large… flairnlp/flair — Flair is a transformer-based natural language processing framework used to build and train models for text… zalandoresearch/flair — Flair is a natural language processing framework for training and applying models for sequence labeling and text… llmware-ai/llmware — llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model…
