| --- |
| 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 |
|
|
| 1. `data_use/run.py` — GLiNER extraction of data mentions over chunked document pages. |
| 2. `data_use/infer_multitask.py` — LFM2.5-350M multitask model predicts provenance + usage/impact per mention. |
| 3. `data_use/attribute.py` — joins the inference back onto the extraction chunks on `(corpus_id, page, chunk, start, end)` and attaches corpus document labels. |
|
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|
