Datasetpublic

davanstrien/eval-mentions-bootstrap

davanstrien/eval-mentions-bootstrap Bootstrap NER dataset produced by urchade/gliner_multi-v2.1 over /input/cleaned-cards.parquet. Generated using uv-scripts/gliner/extract-entities.py. Provenance Source dataset /input/cleaned-cards.parquet (split train) Text column card Bootstrap model urchade/gliner_multi-v2.1 Entity types benchmark name, evaluation dataset, evaluation metric Confidence threshold 0.6 Samples processed 10000 Total… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/eval-mentions-bootstrap.

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Dataset Card

davanstrien/eval-mentions-bootstrap

Bootstrap NER dataset produced by `urchade/gliner_multi-v2.1` over `/input/cleaned-cards.parquet`.

Generated using `uv-scripts/gliner/extract-entities.py`.

Provenance


Source dataset	`/input/cleaned-cards.parquet` (split `train`)
Text column	`card`
Bootstrap model	`urchade/gliner_multi-v2.1`
Entity types	`benchmark name, evaluation dataset, evaluation metric`
Confidence threshold	0.6
Samples processed	10000
Total entities extracted	15811
Inference device	`cuda`
Wall clock	951.7s (10.51 samples/s)

Schema

Original /input/cleaned-cards.parquet columns plus an entities column:

python

entities: list of {
    "start": int,    # character offset, inclusive
    "end": int,      # character offset, exclusive
    "text": str,     # the matched span
    "label": str,    # one of ['benchmark name', 'evaluation dataset', 'evaluation metric']
    "score": float,  # GLiNER confidence in [0, 1]
}

Caveats

—These are bootstrap labels, not human-reviewed. Treat low-confidence (< 0.7) entities as candidates for review.
—GLiNER is zero-shot: changing --entity-types changes what it extracts, but quality varies by entity type.
—Long texts were truncated at 8000 characters before inference.