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universal-ner/universal-ner

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Universal 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)

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Cross-Domain Learning - Targeted distillation for open named entity recognition.
  • Instruction Tuning - Targeted distillation for open named entity recognition.
  • Named Entity Recognition - Targeted distillation for open-domain entity recognition.
    • 375 stars·32 forks·Python·MIT·23 views

    Star history

    Star history chart for universal-ner/universal-nerStar history chart for universal-ner/universal-ner

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Projects sharing features with Universal Ner

    These projects share indexed features with Universal Ner. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • yyding1/gneryyDing1 avatar

      yyDing1/GNER

      60View on GitHub↗

      Rethinking Negative Instances for Generative Named Entity Recognition

      Python
      View on GitHub↗60
    • urchade/glinerurchade avatar

      urchade/GLiNER

      3,333View on GitHub↗

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

      Python
      View on GitHub↗3,333
    • emseoyk/verifineremseoyk avatar

      emseoyk/VerifiNER

      13View on GitHub↗

      Official code for ACL 2024 paper: VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models.

      Python
      View on GitHub↗13
    • flairnlp/flairflairNLP avatar

      flairNLP/flair

      14,378View on 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

      Python
      View on GitHub↗14,378
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    Frequently asked questions

    What does universal-ner/universal-ner do?

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

    What are the main features of universal-ner/universal-ner?

    The main features of universal-ner/universal-ner are: Cross-Domain Learning, Instruction Tuning, Named Entity Recognition.

    Which projects share features with universal-ner/universal-ner?

    Projects with overlapping indexed features include: 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…