datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
data-use-mentions
Data-use mentions (NER / span extraction)
Data mentions extracted from World Bank Policy Research Working Papers and FCV documents, validated by a
context-only LLM judge, and formatted for span-extraction (GLiNER / GLiNER2) and
token-classification (LFM2.5-encoder) fine-tuning.
Labels
Three entity types (the judge's specificity axis):
NAMED_DATA — a proper name, title, or acronym of a specific data source
DESCRIPTIVE_DATA — a source described in words but not… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mentions.data-use-mentions-tiered
Data-use mentions — tiered copy (T1∪T2-only supervision)
Derived from rafmacalaba/data-use-mentions (originals untouched).
Same windows, same text; spans judged tier3_nonmention or junk
(Luna verdicts; unjudged train spans via v3 tier classifier at
p_t3+p_junk >= 0.9) are UNTAGGED — text stays, so they
act as hard negatives for span-extraction training.
Total untagged: 28807 spans.
Labels: single DATA_MENTION class (kept spans = T1 evidential ∪
T2 declaration). Specificity… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mentions-tiered.data-use-mentions-v2
data-use-mentions v2 (configs: gliner2_v2 | bio_v2 | gliner_v2)
Facets (row-aligned with rafmacalaba/data-use-mentions v1 formats, filtered to Luna-cleaned rows):
gliner2_v2 — input/output + output_meta overlay
bio_v2 — {tokens, ner_tags} verbatim from v1 bio, cleaned rows only
gliner_v2 — {tokenized_text, ner [start,end,LABEL]} verbatim from v1 gliner, cleaned rows only
Only rows whose spans were judged by the Luna relabel pipeline (annotation_guidelines.md v2.3,
dual-pass… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mentions-v2.data-use-mentions-extended
Data-use mentions (NER / span extraction)
Data mentions extracted from World Bank Policy Research Working Papers, validated by a
context-only LLM judge, and formatted for span-extraction (GLiNER / GLiNER2) and
token-classification (LFM2.5-encoder) fine-tuning.
Labels
Three entity types (the judge's specificity axis):
NAMED_DATA — a proper name, title, or acronym of a specific data source
DESCRIPTIVE_DATA — a source described in words but not named
VAGUE_DATA —… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mentions-extended.data-use-mentions-v2-holdout-predsmentions-devenements-climatiques-et-naturels-dans-les-archives-depuis-la-fin-du-xviiie-siecle
Mentions d’événements climatiques et naturels dans les archives depuis la fin du XVIIIe siècle
Source
Source officielle : https://www.data.gouv.fr/datasets/mentions-devenements-climatiques-et-naturels-dans-les-archives-depuis-la-fin-du-xviiie-siecle
Identifiant du jeu de données data.gouv.fr : 656021161c89036d00a6435b
Slug data.gouv.fr : mentions-devenements-climatiques-et-naturels-dans-les-archives-depuis-la-fin-du-xviiie-siecle
Licence indiquée dans les… See the full description on the dataset page: https://huggingface.co/datasets/Data-Gouv-ML/mentions-devenements-climatiques-et-naturels-dans-les-archives-depuis-la-fin-du-xviiie-siecle.