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Cataloged from fastino/gliner2.5-small-v1
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Extract entities, classify text, parse structured records, score span attributes, and extract relations — all in one boundary architecture.
GLiNER2.5 Small is the compact English boundary checkpoint. It keeps the same public API as the larger 2.5 models while running faster on CPU and edge-style deployments. Load it with AutoExtractor: the checkpoint's architecture field selects BoundaryExtractor automatically.
Fine-tune via Fastino. Join discussions Reddit.
Classifier for cross-task label constraints, JointIE for typed entity–relation graphsgliner2[local] — no external API requiredThis card is for fastino/gliner2.5-small-v1. All three checkpoints share the same public API.
Python 3.10 or newer is required. The [local] extra pulls in PyTorch so you can load Hub checkpoints.
Always use AutoExtractor for GLiNER2.5. GLiNER2.from_pretrained(...) is the legacy span loader and will not dispatch this checkpoint.
Optional device, fp16, and compile flags:
Returned offsets are half-open character spans into the original string: text[start:end] == entity["text"].
Add descriptions when labels are domain-specific:
Independent per-task decoding with classify_text:
Use gliner2.classification.Classifier when labels on one task legally constrain another. classify_text will not enforce those rules.
Prediction knobs belong in ClassificationConfig on the call, not in from_pretrained:
This checkpoint was trained with enable_relations=True. Independent decoding:
Or through a schema:
Independent extraction does not guarantee that works_for heads are people and tails are organizations.
JointIE scores mention and relation candidates, then searches a globally consistent graph with typed endpoints and uniqueness constraints.
Always check result.feasible. False means the hard constraints could not be satisfied (distinct from “the text contains no facts”).
Attributes are span-conditioned. The model finds entities first, then scores attribute labels at those exact spans. They are not extra entity types and they are not document-level classification.
applies_to=["person"] keeps sentiment off other entity types. qualify_labels=True encodes model-facing queries as sentiment: positive while returning the short label positive.
Restrict sentiment to people while still extracting companies:
Organization spans have no sentiment field. Person spans do.
Record mode keeps instance identity (who bought what) instead of flattening fields into unrelated lists. Enable natural mode with an anchor field:
This checkpoint was trained with enable_records=True.
Compose entities, span attributes, classification, relations, and structures in one extract call:
Document-level topic is independent of per-person sentiment.
batch_extract accepts one schema or a list of schemas (one per document).
extract(...) with max_len truncates. Long-context helpers scan overlapping word chunks and remap spans to document offsets.
The same idea applies to Classifier.classify_long and JointIE.extract_long.
Limits:
BoundaryExtractor)[L, W] width grid)max_len=4096)microsoft/deberta-v3-xsmallenable_records=True), relations (enable_relations=True)flat (weighted interval scheduling); override per call with overlap_policyDo not load this checkpoint with GLiNER2 / SpanExtractor. Those classes expect the legacy span architecture.
If you use this model, please cite:
Apache License 2.0.
pip install "gliner2[local]"from gliner2 import AutoExtractor
model = AutoExtractor.from_pretrained("fastino/gliner2.5-small-v1")
print(type(model).__name__)print(model.config.architecture)# BoundaryExtractor# boundarymodel = AutoExtractor.from_pretrained( "fastino/gliner2.5-small-v1", map_location="cuda", # or "cpu" / "mps" quantize=True, # fp16 weights on GPU compile=True, # torch.compile after the first tracing call)print(type(model).__name__, next(model.parameters()).device)# BoundaryExtractor cuda:0text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday."
result = model.extract_entities( text, ["company", "person", "product", "location"], include_confidence=True, include_spans=True,)print(result)# {# "entities": {# "company": [{"text": "Apple", "start": 0, "end": 5, "confidence": 0.98}],# "person": [{"text": "Tim Cook", "start": 10, "end": 18, "confidence": 0.97}],# "product": [{"text": "iPhone 15", "start": 29, "end": 38, "confidence": 0.96}],# "location": [{"text": "Cupertino", "start": 42, "end": 51, "confidence": 0.95}],# }# }result = model.extract_entities( "Patient received 400mg ibuprofen for severe headache at 2 PM.", { "medication": "Names of drugs or pharmaceutical substances", "dosage": "Amounts such as 400mg, 2 tablets, or 5ml", "symptom": "Reported symptoms or conditions", "time": "Clock times or relative times", }, include_spans=True,)print(result)# {# "entities": {# "medication": [{"text": "ibuprofen", "start": 23, "end": 32}],# "dosage": [{"text": "400mg", "start": 17, "end": 22}],# "symptom": [{"text": "severe headache", "start": 37, "end": 52}],# "time": [{"text": "2 PM", "start": 56, "end": 60}],# }# }result = model.classify_text( "This laptop has amazing performance but terrible battery life!", {"sentiment": ["positive", "negative", "neutral"]},)print(result)# {"sentiment": "negative"}
result = model.classify_text( "Great camera quality, decent performance, but poor battery life.", { "aspects": { "labels": ["camera", "performance", "battery", "display", "price"], "multi_label": True, "cls_threshold": 0.4, } },)print(result)# {"aspects": ["camera", "performance", "battery"]}from gliner2.classification import ( Classifier, ClassificationSchema, ClassificationConfig,)from gliner2.classification import constraints as C
clf = Classifier.from_pretrained("fastino/gliner2.5-small-v1")
schema = ( ClassificationSchema() .single("intent", ["read", "write", "delete"]) .multi("effects", ["read_only", "create", "modify", "delete"], min_labels=1) .constrain( C.implies(("intent", "delete"), ("effects", "delete")), C.excludes(("intent", "read"), ("effects", "delete")), ))
result = clf.classify("Delete the temporary file from /tmp", schema)print(result.value("intent"))print(result.value("effects"))print(result.feasible)print(result.to_dict())# delete# ['delete']# True# {# "intent": {# "value": "delete",# "confidence": 0.93,# "probabilities": {"read": 0.02, "write": 0.05, "delete": 0.93},# },# "effects": {# "value": ["delete"],# "confidence": 0.88,# "probabilities": {# "read_only": 0.04, "create": 0.03, "modify": 0.05, "delete": 0.88# },# },# "_meta": {"feasible": True, "decoder": "exact"},# }result = clf.classify( "Preview the report", schema, config=ClassificationConfig(decoder="beam", beam_size=16),)print(result.value("intent"), result.value("effects"), result.feasible)# read ['read_only'] Truetext = "Alice works for Acme in Paris."result = model.extract_relations( text, ["works_for", "located_in"], include_spans=True, include_confidence=True,)print(result)# {# "relation_extraction": {# "works_for": [{# "head": {"text": "Alice", "start": 0, "end": 5, "confidence": 0.91},# "tail": {"text": "Acme", "start": 16, "end": 20, "confidence": 0.91},# }],# "located_in": [{# "head": {"text": "Acme", "start": 16, "end": 20, "confidence": 0.87},# "tail": {"text": "Paris", "start": 24, "end": 29, "confidence": 0.87},# }],# }# }schema = model.create_schema().relations( {"works_for": {"threshold": 0.6}, "located_in": {"threshold": 0.6}})result = model.extract(text, schema, include_spans=True)print(result)# {# "relation_extraction": {# "works_for": [{# "head": {"text": "Alice", "start": 0, "end": 5},# "tail": {"text": "Acme", "start": 16, "end": 20},# }],# "located_in": [{# "head": {"text": "Acme", "start": 16, "end": 20},# "tail": {"text": "Paris", "start": 24, "end": 29},# }],# }# }from gliner2.joint_ie import JointIE, JointIEConfig
joint = JointIE.from_pretrained("fastino/gliner2.5-small-v1")
schema = ( joint.create_schema() .entities(["person", "organization", "location"]) .relation("works_for", "person", "organization", unique_head=True) .relation("located_in", "organization", "location") .no_self_loops())
result = joint.extract( "Alice works for Acme in Paris. Bob joined Acme last year.", schema, config=JointIEConfig(optimizer="beam", beam_size=32),)
print(result.feasible)print(result.to_dict())# True# {# "entities": [# {"id": "e1", "type": "person", "text": "Alice", "start": 0, "end": 5, "confidence": 0.94},# {"id": "e2", "type": "organization", "text": "Acme", "start": 16, "end": 20, "confidence": 0.92},# {"id": "e3", "type": "location", "text": "Paris", "start": 24, "end": 29, "confidence": 0.90},# {"id": "e4", "type": "person", "text": "Bob", "start": 31, "end": 34, "confidence": 0.91},# ],# "relations": [# {"type": "works_for", "head": "e1", "tail": "e2", "confidence": 0.88},# {"type": "works_for", "head": "e4", "tail": "e2", "confidence": 0.81},# {"type": "located_in", "head": "e2", "tail": "e3", "confidence": 0.86},# ],# }for rel in result.relations: head = result.entity(rel.head) tail = result.entity(rel.tail) print(f"{head.text} -{rel.type}-> {tail.text}")# Alice -works_for-> Acme# Bob -works_for-> Acme# Acme -located_in-> Parisfrom gliner2 import AutoExtractor, AttributeGroup
model = AutoExtractor.from_pretrained("fastino/gliner2.5-small-v1")
text = ( "Alice was delighted with the promotion, " "but Bob sounded frustrated about the delay.")
schema = ( model.create_schema() .entities(["person"]) .entity_attributes({ "sentiment": AttributeGroup( ["positive", "negative", "neutral"], applies_to=["person"], qualify_labels=True, ) }))
result = model.extract( text, schema, include_spans=True, include_confidence=True,)print(result)# {# "entities": {# "person": [# {# "text": "Alice",# "start": 0,# "end": 5,# "confidence": 0.96,# "sentiment": {"label": "positive", "confidence": 0.89},# },# {# "text": "Bob",# "start": 44,# "end": 47,# "confidence": 0.95,# "sentiment": {"label": "negative", "confidence": 0.84},# },# ]# }# }schema = ( model.create_schema() .entities(["person", "organization"]) .entity_attributes({ "sentiment": AttributeGroup( ["positive", "negative", "neutral"], applies_to=["person"], qualify_labels=True, ) }))
result = model.extract( "Alice praised Microsoft, but Bob criticized OpenAI.", schema, include_spans=True, include_confidence=True,)print(result)# {# "entities": {# "person": [# {# "text": "Alice",# "start": 0,# "end": 5,# "confidence": 0.96,# "sentiment": {"label": "positive", "confidence": 0.88},# },# {# "text": "Bob",# "start": 29,# "end": 32,# "confidence": 0.95,# "sentiment": {"label": "negative", "confidence": 0.86},# },# ],# "organization": [# {"text": "Microsoft", "start": 14, "end": 23, "confidence": 0.97},# {"text": "OpenAI", "start": 44, "end": 50, "confidence": 0.96},# ],# }# }schema = ( model.create_schema() .structure("purchase", mode="natural", anchor="buyer") .field("buyer", dtype="str", cardinality="required_one") .field("item", dtype="str", cardinality="required_one"))
result = model.extract( "Alice bought apples and Bob bought oranges.", schema,)print(result)# {# "purchase": [# {"buyer": "Alice", "item": "apples"},# {"buyer": "Bob", "item": "oranges"},# ]# }from gliner2 import AttributeGroup
schema = ( model.create_schema() .entities({ "person": "Named people", "organization": "Companies or teams", "product": "Named products or services", }) .entity_attributes({ "sentiment": AttributeGroup( ["positive", "negative", "neutral"], applies_to=["person"], qualify_labels=True, ) }) .classification("topic", ["technology", "business", "sports", "politics"]) .relations(["works_for", "announced"]) .structure("announcement", mode="natural", anchor="product") .field("company", dtype="str") .field("product", dtype="str", cardinality="required_one"))
text = "Apple CEO Tim Cook unveiled the iPhone 15 Pro for $999."result = model.extract(text, schema, include_spans=True, include_confidence=True)print(result)# {# "entities": {# "person": [{# "text": "Tim Cook",# "start": 10,# "end": 18,# "confidence": 0.97,# "sentiment": {"label": "positive", "confidence": 0.82},# }],# "organization": [{"text": "Apple", "start": 0, "end": 5, "confidence": 0.98}],# "product": [{"text": "iPhone 15 Pro", "start": 32, "end": 45, "confidence": 0.96}],# },# "topic": {"label": "technology", "confidence": 0.94},# "relation_extraction": {# "works_for": [{# "head": {"text": "Tim Cook", "start": 10, "end": 18, "confidence": 0.86},# "tail": {"text": "Apple", "start": 0, "end": 5, "confidence": 0.86},# }],# "announced": [{# "head": {"text": "Tim Cook", "start": 10, "end": 18, "confidence": 0.84},# "tail": {"text": "iPhone 15 Pro", "start": 32, "end": 45, "confidence": 0.84},# }],# },# "announcement": [{# "company": "Apple",# "product": "iPhone 15 Pro",# }],# }texts = [ "Google hired Jane Doe in London.", "Tesla launched the Model 3 in California.",]results = model.batch_extract_entities( texts, ["company", "person", "product", "location"], batch_size=8, include_spans=True,)print(results)# [# {# "entities": {# "company": [{"text": "Google", "start": 0, "end": 6}],# "person": [{"text": "Jane Doe", "start": 13, "end": 21}],# "product": [],# "location": [{"text": "London", "start": 25, "end": 31}],# }# },# {# "entities": {# "company": [{"text": "Tesla", "start": 0, "end": 5}],# "person": [],# "product": [{"text": "Model 3", "start": 19, "end": 26}],# "location": [{"text": "California", "start": 30, "end": 40}],# }# },# ]long_text = ("Quarterly overview. " * 40) + "Satya Nadella spoke in Redmond about Microsoft."
result = model.extract_entities_long( long_text, ["person", "organization", "location"], chunk_size=384, chunk_overlap=64, include_spans=True,)print(result)# {# "entities": {# "person": [{"text": "Satya Nadella", "start": 800, "end": 813}],# "organization": [{"text": "Microsoft", "start": 837, "end": 846}],# "location": [{"text": "Redmond", "start": 823, "end": 830}],# }# }
result = model.extract_long(long_text, schema, chunk_size=384, chunk_overlap=64)print(result["topic"])# technology@misc{zaratiana2025gliner2efficientmultitaskinformation, title={GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface}, author={Urchade Zaratiana and Gil Pasternak and Oliver Boyd and George Hurn-Maloney and Ash Lewis}, year={2025}, eprint={2507.18546}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2507.18546},}