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README.md

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+---
+tags:
+  - gliner2
+  - ner
+  - dataset-extraction
+  - lora
+base_model: fastino/gliner2-large-v1
+library_name: gliner2
+---
+
+# GLiNER2 Dataset Mention Extractor
+
+Fine-tuned GLiNER2 model for extracting structured dataset mentions from research documents.
+
+## Task
+Given a document passage, extracts:
+- **Entity fields**: dataset_name, acronym, producer, geography, description, etc.
+- **Classifications**: dataset_tag (named/descriptive/vague), usage_context, is_used
+
+## Training
+- **Base model**: `fastino/gliner2-large-v1`
+- **Method**: LoRA (r=16, alpha=32)
+- **Data**: 1,197 synthetic training examples
+
+## Usage
+
+```python
+from gliner2 import GLiNER2
+
+extractor = GLiNER2.from_pretrained("fastino/gliner2-large-v1")
+extractor.load_adapter("rafmacalaba/gliner2-datause-large-v1")
+
+schema = (
+    extractor.create_schema()
+    .structure("dataset_mention")
+        .field("dataset_name", dtype="str")
+        .field("acronym", dtype="str")
+        .field("producer", dtype="str")
+        .field("geography", dtype="str")
+        .field("dataset_tag", dtype="str", choices=["named", "descriptive", "vague"])
+        .field("usage_context", dtype="str", choices=["primary", "supporting", "background"])
+        .field("is_used", dtype="str", choices=["True", "False"])
+)
+
+results = extractor.extract(text, schema)
+dataset_mentions = results["dataset_mention"]
+```