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Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts)
The main features of urchade/gliner are: Instruction Tuning, Named Entity Recognition.
Open-source alternatives to urchade/gliner include: universal-ner/universal-ner — UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition Wenxuan Zhou, Sheng… yyding1/gner — Rethinking Negative Instances for Generative Named Entity Recognition. zalandoresearch/flair — Flair is a natural language processing framework for training and applying models for sequence labeling and text… flairnlp/flair — Flair is a transformer-based natural language processing framework used to build and train models for text… llmware-ai/llmware — llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model… bojone/globalpointer — 全局指针统一处理嵌套与非嵌套NER。.
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, Hoifung Poon (*Equal Contribution)
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
Flair is a natural language processing framework for training and applying models for sequence labeling and text classification. It provides a system for generating word embeddings and identifying semantic entities within text. The framework includes a dedicated system for zero and few-shot learning, enabling text classification and entity extraction using minimal training examples by leveraging pre-trained knowledge. Its capabilities cover named entity recognition, sentiment analysis, and the training of specialized models using custom datasets. It also includes tooling for the visual highl
