Datasets:

rafmacalaba
/

fcv-extractions-meta

license: cc-by-4.0
language:
  - en
tags:
  - data-use
  - provenance
  - usage-impact
  - world-bank
  - fcv
  - named-entity-recognition
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl

fcv-extractions-meta

Data-use mention extractions from the World Bank Fragility, Conflict and Violence (FCV) document corpus, enriched by the fine-tuned rafmacalaba/lfm2.5-350M-datause-multitask model (a LoRA SFT of LiquidAI/LFM2.5-350M).

Shape

long — one row per mention; provenance and usage_impact are flattened to top-level columns (data_type, usage_action, impact_label, usage_summary, producer, year, geography, acronym).

Configs

config	rows
default	33,471

Fields

Document trace: corpus_id, config, pdf_url, title, page, chunk.
Mention span: mention (long) or entities[].text (nested), label (NAMED_DATA / DESCRIPTIVE_DATA / VAGUE_DATA), score, start, end.
Document metadata (when built with --with-metadata): project_id, doc_id, document_type, country, sector, language, disclosure_status, document_date, region, topics, themes, authors, abstract, and more, joined from rafmacalaba/fcv-corpus.

provenance

producer, year, geography, acronym — verbatim substrings of the source text; keys are omitted when the model finds nothing.

usage_impact

data_type: survey, administrative, indicator, database, geospatial, report, other
usage_action: source, analyze, curate, validate, inform
impact_label: none, evidence-base, policy, uptake
usage_summary: one-sentence free text describing how the author uses the data.

Attribution coverage

33,328 of 33,471 extracted entities matched an inference row (99.6%). Entities without a match carry only the raw extraction fields (no provenance / usage_impact).

How it was built

data_use/run.py — GLiNER extraction of data mentions over chunked document pages.
data_use/infer_multitask.py — LFM2.5-350M multitask model predicts provenance + usage/impact per mention.
data_use/attribute.py — joins the inference back onto the extraction chunks on (corpus_id, page, chunk, start, end) and attaches corpus document labels.