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hsilvosa/bne-spanish-subject-classifier

sourceHugging Facecc-by-4.0updated 1mo agoView on Hugging Face

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Model Card

Spanish Bibliographic Subject & UDC Classifier

This model is a multi-label sequence classification transformer fine-tuned on Spanish bibliographic records from the Biblioteca Nacional de España (BNE). It predicts SKOS subject headings and Universal Decimal Classification (UDC/Dewey) codes from book titles and publication descriptions.

Benchmark Evaluation Results

Evaluation Metric	Score	Description
SKOS Subject Top-1 Accuracy	`0.6071`	Exact match top-1 accuracy on SKOS preferred subject headings
SKOS Subject Top-3 Accuracy	`0.9286`	Top-3 candidate coverage for SKOS subject headings
SKOS Subject Macro F1	`0.2519`	Unweighted Macro F1 across SKOS subject classes
SKOS Subject Weighted F1	`0.4587`	Frequency-weighted F1 across SKOS subject classes
UDC Division Top-1 Accuracy	`1.0000`	Classification accuracy on UDC main divisions
UDC Division Macro F1	`1.0000`	Unweighted Macro F1 across UDC classification codes

Model Details

—Foundation Model: dccuchile/bert-base-spanish-wwm-cased (BETO)
—Parameters: ~110 Million
—Training Dataset: hsilvosa/bne-linked-data
—Task: Bibliographic subject and UDC code classification

Usage

python

from bne_semantic_linker.inference.classifier_pipeline import BNESubjectClassifierPipeline

pipeline = BNESubjectClassifierPipeline("hsilvosa/bne-spanish-subject-classifier")
results = pipeline.predict(
    title="Don Quijote de la Mancha", 
    description="Edición crítica con notas explicativas sobre la novela barroca española"
)

print(results)

Intended Use & Limitations

Optimized for library cataloging, automated subject indexing, and semantic categorisation of Spanish publications.