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datause-extraction

Token Classification
GLiNER2
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GLiNER
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ner
named-entity-recognition
world-bank
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data-use
lora
adapter
forced-displacement
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@@ -26,7 +26,7 @@ metrics:
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  - f1
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  ---
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- # datause-extraction-v12rerun
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  This repository contains the LoRA fine-tuned adapter weights (`v12-rerun`) for dataset mention extraction. It is trained on top of the base model `fastino/gliner2-large-v1`.
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@@ -85,7 +85,7 @@ text = (
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  # Extract dataset mentions using this specific adapter
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  result = extract_from_text(
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  text,
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- adapter_id="rafmacalaba/datause-extraction-v12rerun",
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  include_confidence=True
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  )
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@@ -117,7 +117,7 @@ else:
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  model = GLiNER2.from_pretrained("fastino/gliner2-large-v1", **kwargs)
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  # 2. Download and apply the LoRA adapter weights
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- adapter_path = snapshot_download("rafmacalaba/datause-extraction-v12rerun")
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  model.load_adapter(adapter_path)
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  # 3. Perform inference
 
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  - f1
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  ---
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+ # datause-extraction
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  This repository contains the LoRA fine-tuned adapter weights (`v12-rerun`) for dataset mention extraction. It is trained on top of the base model `fastino/gliner2-large-v1`.
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  # Extract dataset mentions using this specific adapter
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  result = extract_from_text(
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  text,
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+ adapter_id="ai4data/datause-extraction",
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  include_confidence=True
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  )
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  model = GLiNER2.from_pretrained("fastino/gliner2-large-v1", **kwargs)
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  # 2. Download and apply the LoRA adapter weights
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+ adapter_path = snapshot_download("ai4data/datause-extraction")
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  model.load_adapter(adapter_path)
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  # 3. Perform inference