Fastino Releases GLiNER2.5: A Boundary-Prediction Architecture That Removes Span Enumeration From Information Extraction
GLiNER2.5 ditches span enumeration entirely—entity extraction now scales with text length, not entity count. Three Apache 2.0 weights, all CPU-runnable.
Why it matters
A foundational shift in how NER models work: boundary prediction replaces the compute-heavy enumeration step that made extraction cost balloon with entity density. Practitioners building IE pipelines get faster, denser inference and open weights to build on.
The key facts
13 to knowFastino GLiNER2.5 released with boundary-prediction architecture (replaces span enumeration)
Three Apache 2.0 checkpoints: 74M, 194M, 287M parameters
All checkpoints CPU-runnable
Joint entity-relation decoding added
Constrained classification support
Span attributes capability
4,096-word context window
Macro F1: 56.17 on 16 zero-shot benchmarks
Entity width no longer scales compute cost with model size
Fastino released GLiNER2.5 with boundary-prediction architecture (replaces span enumeration)
Features: joint entity-relation decoding, constrained classification, span attributes, 4,096-word context
Macro F1 56.17 on 16 zero-shot benchmarks
Eliminates quadratic compute cost of candidate generation
Go to the source
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Publisher excerpt: 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 classification, span…