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urchade avatar

urchade/GLiNER

0
View on GitHub↗
3,333 estrellas·287 forks·Python·Apache-2.0·9 vistasurchade.github.io/GLiNER↗

GLiNER

Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts)

Features

  • Instruction Tuning - Generalist model for named entity recognition.
  • Named Entity Recognition - Generalist model using bidirectional transformers for entity recognition.

Historial de estrellas

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Alternativas open-source a GLiNER

Proyectos open-source similares, clasificados según cuántas características comparten con GLiNER.
  • yyding1/gnerAvatar de yyDing1

    yyDing1/GNER

    60Ver en GitHub↗

    Rethinking Negative Instances for Generative Named Entity Recognition

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    Ver en GitHub↗60
  • universal-ner/universal-nerAvatar de universal-ner

    universal-ner/universal-ner

    375Ver en GitHub↗

    UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, Hoifung Poon (*Equal Contribution)

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    Ver en GitHub↗375
  • flairnlp/flairAvatar de flairNLP

    flairNLP/flair

    14,378Ver en GitHub↗

    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

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    Ver en GitHub↗14,378
  • zalandoresearch/flairAvatar de zalandoresearch

    zalandoresearch/flair

    14,378Ver en GitHub↗

    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

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    Ver en GitHub↗14,378
Ver las 30 alternativas a GLiNER→

Preguntas frecuentes

¿Qué hace urchade/gliner?

Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts)

¿Cuáles son las características principales de urchade/gliner?

Las características principales de urchade/gliner son: Instruction Tuning, Named Entity Recognition.

¿Qué alternativas de código abierto existen para urchade/gliner?

Las alternativas de código abierto para urchade/gliner incluyen: 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。.