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Fastino releases GLiNER2.5 with boundary prediction for efficient information extraction

By PulseAugur Editorial · [1 sources] ·

Fastino has released GLiNER2.5, an information extraction model that replaces span enumeration with boundary prediction. This architectural change allows for longer context windows up to 4,096 words and linear computation with respect to sequence length. The model supports joint entity-relation extraction, cross-task label constraints, and per-span attributes, offering a more flexible and efficient alternative to existing methods. Three checkpoints are available on Hugging Face, suitable for various deployment scenarios including CPU-based inference. AI

IMPACT Offers a more efficient and flexible approach to information extraction, potentially reducing costs for tasks like PII detection and knowledge graph creation.

RANK_REASON This is a release of a specific model for information extraction, not a frontier model release.

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

Fastino releases GLiNER2.5 with boundary prediction for efficient information extraction

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Tool
This is a release of a specific model for information extraction, not a frontier model release.
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model release, product
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High
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71 days old
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COVERAGE [1]

  1. MarkTechPost TIER_1 English(EN) · Michal Sutter ·

    Fastino Releases GLiNER2.5: A Boundary-Prediction Architecture That Removes Span Enumeration From Information Extraction

    <p>Fastino released GLiNER2.5, replacing span enumeration with boundary prediction so entity width no longer costs compute. Three Apache 2.0 checkpoints ship at 74M, 194M, and 287M parameters, all CPU-runnable. The release adds joint entity-relation decoding, constrained classifi…

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