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GLINER2.5 - NOW AVAILABLE

Open-source small language model for efficient information extraction

GLiNER2.5 replaces span enumeration with a new boundary-prediction architecture, unlocking unlimited span length, long-context extraction, joint entity-relation extraction, constrained classification, and span attributes. Still small, still no GPU required.

Use GLiNER2.5

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GitHub repo

35M+

Hugging Face

model downloads

Hugging Face model downloads

5.2K

GitHub

stars

5.2K

GitHub stars

1.1B+

End

users

1.1B+

End users

FEATURES

Five new capabilities with GLiNER2.5

Extract entities of any size, handle full-length documents, constrain label combinations, classify individual spans, and connect entities into graphs, all in a single call. Available in base, multilingual, and small versions.

New

Span length

Unlimited span length

Extract entities of any size, from full postal addresses to clause-length legal references, with no width limit to configure or work around.

New

Constraints

Constrained classification

Classify across several tasks with declared rules that keep label combinations consistent, preventing contradictory predictions.

New

Richer context

Span attributes

Classify any extracted span for a custom attribute, such as sentiment, severity, or negation, decoded in the same forward pass.

Classify any extracted span for a custom attribute, such as sentiment, severity, or negation, decoded in the same forward pass as the entity.

New

Full documents

Long-context extraction

Process contracts, reports, and transcripts in a single pass, with native chunking that merges results back to the original offsets for anything longer.

New

Knowledge graphs

Joint Information Extraction (IE)

Extract entities and relations as one connected graph, guaranteed to conform to your schema, for agent memory or knowledge bases.

COMPARISON

Comparing GLiNER models

GLiNER2.5

Constraint-aware structured extraction

GLiNER2

Entity extraction & structured parsing

GLiNER

First-gen GLiNER - Launched in 2023

MODEL SIZE (PARAMETERS)

0.3B (multi), 0.2B (base), 74M (small)

0.3B (multi) or 0.2BM (base)

90M (medium) or 50M (small)

GENERAL NER (FEW-NERD) - F1

52.37 (multi), 55.14 (base)

51.49 (multi), 47.22 (base)

—

NATURAL LANGUAGE INFERENCE (XNLI) - F1

62.30 (multi), 54.49 (base)

37.55 (multi), 49.01 (base)

—

NAMED ENTITY RECOGNITION

✓

TEXT CLASSIFICATION

✓ (plus cross-task constraints)

✓(single and multi-label)

—

STRUCTURED / JSON EXTRACTION

✓

—

RELATION EXTRACTION

✓ (more optimized)

✓

—

CROSS-TASK CONSTRAINTS

✓ (more optimized)

✓

—

LONG-DOCUMENT UTILITIES

✓

—

INPUT CONTEXT

4.096 words

2.048 tokens

512 tokens

MAX SPAN LENGTH

No cap

8 words

12 words

OPEN-SOURCE LICENSE

Apache 2.0

FROM THE COMMUNITY

A growing community of developers building on GLiNER.

Share your story

The GLiNER approach let us ship zero-shot hallucination typing in our LettuceDetect models. Users bring their own labels and a 300M encoder types error spans across prose, code, and tool output.
Ádám Kovács
CTO & Co-founder, KR Labs
GLiNER is one of the most practical models we’ve integrated into OpenMed. Its zero-shot approach enables flexible clinical extraction across CPUs, GPUs, Apple Silicon while keeping sensitive health text local.
Maziyar Panahi
Founder, OpenMed
I'm a big fan. I really love that it is open source and so I personally optimized it a lot to make it much much faster which was important for my use case.
Max Buckley
Former Head of Knowledge Research, Exa
I am building a chrome extension for PII redaction. The best part about GLiNER is the speed and the ease to use it the browser.
Hossein Kazemi
CEO, Xeco Labs

The GLiNER approach let us ship zero-shot hallucination typing in our LettuceDetect models. Users bring their own labels and a 300M encoder types error spans across prose, code, and tool output.
Ádám Kovács
CTO & Co-founder, KR Labs
GLiNER is one of the most practical models we’ve integrated into OpenMed. Its zero-shot approach enables flexible clinical extraction across CPUs, GPUs, Apple Silicon while keeping sensitive health text local.
Maziyar Panahi
Founder, OpenMed
I'm a big fan. I really love that it is open source and so I personally optimized it a lot to make it much much faster which was important for my use case.
Max Buckley
Former Head of Knowledge Research, Exa
I am building a chrome extension for PII redaction. The best part about GLiNER is the speed and the ease to use it the browser.
Hossein Kazemi
CEO, Xeco Labs

I’ve been working in the NER space for a long time, and I loved GLiNER from the first time I saw it. The architecture was simple, flexible, and immediately felt like something that could be pushed much further, so I decided to become one of its maintainers.
Ihor Stepanov
Co-founder, Knowledgator
GLiNER is great! I used it for redacting PII from text.
Pedro Probst
Machine Learning Engineer, Nubank
It is quite versatile. A quick run of GLiNER can be used as silver data labeling for a project, or it can be used to adapt to situation where we need NER on a corpus but the predefined list of the NER types is not known beforehand.
Hoan Nguyen
Head of Data Science, XOMAD

Move beyond general purpose language models.

Specialized, open language models that are faster, cheaper, and more accessible for most teams.

Use GLiNER 2.5

Move beyond general purpose language models.

Specialized, open language models that are faster, cheaper, and more accessible for most teams.

Use GLiNER 2.5

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