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Knowledgator releases GLiFormer for token-free information extraction

By PulseAugur Editorial · [1 sources] ·

Knowledgator Engineering has introduced GLiFormer, a novel encoder framework designed for information extraction tasks. This model, available in Base (264.2M parameters) and Large (575.6M parameters) versions, can perform named-entity recognition, text classification, relation extraction, and nested JSON structuring without generating tokens. GLiFormer achieves competitive performance on benchmarks, particularly in nested JSON extraction where its Large version reached a 91.10 F1 score, and offers significant speed advantages over traditional LLMs for these tasks. AI

IMPACT Offers a faster, token-free alternative for structured information extraction, potentially improving efficiency in data processing pipelines.

RANK_REASON The item describes a new model release with technical details and benchmark performance, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Knowledgator releases GLiFormer for token-free information extraction

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The item describes a new model release with technical details and benchmark performance, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. MarkTechPost TIER_1 English(EN) · Asif Razzaq ·

    Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens

    <p>GLiFormer Large scores 91.10 F1 on nested JSON, near GPT-5.6-luna's 91.96, while grounding every value in source spans.</p> <p>The post <a href="https://www.marktechpost.com/2026/09/16/knowledgator-releases-gliformer-a-575m-parameter-encoder-that-hits-91-10-f1-on-nested-json-e…

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