datause-relation-v0
Fine-tuned GLiNER2 adapter for relation extraction in Data Use impact assessment pipeline.
Architecture: LoRA adapter for fastino/gliner2-large-v1
Relations: has_organization, used_by, has_acronym, has_timeframe, has_geography
Training data: LLM-generated synthetic + real-world PDF text
Status: v0 alpha — initial fine-tuning.
Usage
from gliner import GLiNER2
model = GLiNER2.from_pretrained("fastino/gliner2-large-v1")
model.load_adapter("ai4data/datause-relation-v0")
Pipeline Integration
Call 1b in 3-model swarm:
- Call 1: Entity extraction (
ai4data/datause-extraction) - Call 1b: Relation extraction (this model)
- Call 2: Classification (
ai4data/datause-impact-v0)
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from gliner import GLiNER model = GLiNER.from_pretrained("ai4data/datause-relation-v0") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"])