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Cataloged from knowledgator/gliner-relex-large-v1.0
All models by knowledgator →GLiNER-relex is a unified model for zero-shot Named Entity Recognition (NER) and Relation Extraction (RE) that performs both tasks simultaneously in a single forward pass. Built on the GLiNER architecture, it extends the span-based approach to jointly identify entities and extract relationships between them.
"other" label to extract entities whose types are not explicitly defined but can be inferred from the specified relationsFirst, install the GLiNER library:
Expected output:
You can provide natural language descriptions for entity labels to give the model richer context, which can improve extraction accuracy, especially for ambiguous or domain-specific types:
When using descriptions, pass labels as a dictionary where keys are label names and values are their descriptions. The model encodes these descriptions alongside the text for better semantic matching.
When you want to extract entities involved in specific relations without predefining every entity type, use the special "other" label. The model will identify entities based on the relations they participate in, even if their type does not match any of the explicitly defined labels:
Expected output:
This is particularly useful when:
If you only need entities without relations:
You can fine-tune extraction sensitivity with separate thresholds:
We recommend lowering the threshold (entity extraction threshold) and keeping it in the range of 0.3–0.5. For adjacency_threshold, the model provides good results in the 0.5–0.65 range. For relation_threshold, use larger values like 0.7–0.9. Feel free to adjust all of these values based on your project requirements.
GLiNER-relex uses a unified encoder architecture that:
This joint approach allows the model to leverage entity information when extracting relations, leading to more coherent predictions.
pip install gliner -Ufrom gliner import GLiNER
# Load the modelmodel = GLiNER.from_pretrained("knowledgator/gliner-relex-large-v1.0")
# Define your entity types and relation typesentity_labels = ["location", "person", "date", "structure"]relation_labels = ["located in", "designed by", "completed in"]
# Input texttext = "The Eiffel Tower, located in Paris, France, was designed by engineer Gustave Eiffel and completed in 1889."
# Run inference - returns both entities and relationsentities, relations = model.inference( texts=[text], labels=entity_labels, relations=relation_labels, threshold=0.3, relation_threshold=0.5, return_relations=True, flat_ner=False)
# Print entitiesprint("Entities:")for entity in entities[0]: print(f" {entity['text']} -> {entity['label']} (score: {entity['score']:.3f})")
# Print relationsprint("\nRelations:")for relation in relations[0]: head = relation["head"]["text"] tail = relation["tail"]["text"] rel_type = relation["relation"] score = relation["score"] print(f" {head} --[{rel_type}]--> {tail} (score: {score:.3f})")Entities: Eiffel Tower -> structure (score: 0.912) Paris -> location (score: 0.934) France -> location (score: 0.891) Gustave Eiffel -> person (score: 0.923) 1889 -> date (score: 0.856)
Relations: Eiffel Tower --[located in]--> Paris (score: 0.823) Eiffel Tower --[designed by]--> Gustave Eiffel (score: 0.847) Eiffel Tower --[completed in]--> 1889 (score: 0.789)entity_labels = { "person": "A human individual, including fictional characters", "organization": "A company, institution, agency, or other group of people", "location": "A physical place, geographic region, or address", "date": "A calendar date, time period, or temporal expression"}relation_labels = ["works for", "located in", "founded on"]
text = "Tim Cook has been leading Apple Inc. from its headquarters in Cupertino since 2011."
entities, relations = model.inference( texts=[text], labels=entity_labels, relations=relation_labels, threshold=0.5, relation_threshold=0.7, return_relations=True, flat_ner=False)entity_labels = ["person"]relation_labels = ["author of", "born in"]
text = "Gabriel García Márquez, born in Aracataca, wrote One Hundred Years of Solitude."
entities, relations = model.inference( texts=[text], labels=entity_labels + ["other"], # "other" captures relation-driven entities relations=relation_labels, threshold=0.5, relation_threshold=0.7, return_relations=True, flat_ner=False)Entities: Gabriel García Márquez -> person (score: 0.931) Aracataca -> other (score: 0.724) One Hundred Years of Solitude -> other (score: 0.689)
Relations: Gabriel García Márquez --[born in]--> Aracataca (score: 0.812) Gabriel García Márquez --[author of]--> One Hundred Years of Solitude (score: 0.795)texts = [ "Elon Musk founded SpaceX in Hawthorne, California.", "Microsoft, led by Satya Nadella, acquired GitHub in 2018.", "The Louvre Museum in Paris houses the Mona Lisa."]
entity_labels = ["person", "organization", "location", "artwork"]relation_labels = ["founder of", "CEO of", "located in", "acquired", "houses"]
entities, relations = model.inference( texts=texts, labels=entity_labels, relations=relation_labels, threshold=0.5, relation_threshold=0.5, batch_size=8, return_relations=True, flat_ner=False)
for i, (text_entities, text_relations) in enumerate(zip(entities, relations)): print(f"\nText {i + 1}:") print(f" Entities: {[e['text'] for e in text_entities]}") print(f" Relations: {[(r['head']['text'], r['relation'], r['tail']['text']) for r in text_relations]}")entities = model.inference( texts=[text], labels=entity_labels, relations=[], # Empty list for relations threshold=0.5, return_relations=False, # Skip relation extraction flat_ner=False)entities, relations = model.inference( texts=texts, labels=entity_labels, relations=relation_labels, threshold=0.5, # Entity confidence threshold adjacency_threshold=0.6, # Threshold for entity pair candidates relation_threshold=0.7, # Relation classification threshold flat_ner=True, # Enforce non-overlapping entities multi_label=False, # Single label per entity span return_relations=True){ "start": int, # Start character position "end": int, # End character position "text": str, # Entity text span "label": str, # Entity type "score": float # Confidence score (0-1)}{ "head": { "start": int, "end": int, "text": str, "type": str, "entity_idx": int # Index in entities list }, "tail": { "start": int, "end": int, "text": str, "type": str, "entity_idx": int }, "relation": str, # Relation type "score": float # Confidence score (0-1)}