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rafmacalaba/
gliner2-datause-large-v15-transformed

Name: gliner2-datause-large-v15-transformed
Availability: InStock

gliner2

ner

data-mention-extraction

lora

fastino/gliner2-large-v1

adapter

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GLiNER2 Data Mention Extractor (v15-transformed)

Fine-tuned GLiNER2 LoRA adapter for extracting structured data mentions from development economics and humanitarian research documents.

v4 key change: Trained with a patched GLiNER2 library (rafmacalaba/GLiNER2@feat/main-fixes-clw) that feeds mean-pooled passage token embeddings into count_pred instead of the schema [P] token. This is the first adapter version where multi-mention recall is trained via a meaningful gradient.

Task

Given a document passage, extracts structured information about each dataset mentioned:

Extractive field: mention_name (verbatim from text)
Classification fields (fixed choices):
- specificity_tag: named / descriptive / vague
- typology_tag: survey / census / database / administrative / indicator / geospatial / microdata / report / other
- is_used: True / False
- usage_context: primary / supporting / background

Training

Base model: fastino/gliner2-large-v1
Method: LoRA (r=16, alpha=32.0)
Target modules: ['encoder', 'span_rep', 'classifier', 'count_embed', 'count_pred']
Training examples: 1430
Val examples: 136
Best val loss: 379.9630
GLiNER2 branch: rafmacalaba/GLiNER2@feat/main-fixes-clw

Usage

python

from gliner2 import GLiNER2

# Install the patched library first
# pip install git+https://github.com/rafmacalaba/GLiNER2.git@feat/main-fixes-clw

extractor = GLiNER2.from_pretrained("fastino/gliner2-large-v1")
extractor.load_adapter("rafmacalaba/gliner2-datause-large-v15-transformed")

schema = (
    extractor.create_schema()
    .structure("data_mention")
        .field("mention_name",    dtype="str")
        .field("specificity_tag", dtype="str", choices=["named", "descriptive", "vague", "na"])
        .field("typology_tag",    dtype="str", choices=["survey", "census", "administrative",
                                                         "database", "indicator", "geospatial",
                                                         "microdata", "report", "other", "na"])
        .field("is_used",         dtype="str", choices=["True", "False", "na"])
        .field("usage_context",   dtype="str", choices=["primary", "supporting", "background", "na"])
)

result = extractor.extract(text, schema)