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README.md

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+---
+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-reliefweb
+
+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 | 33,328 |
+
+## 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 33,328 extracted entities matched an inference row (100.0%). 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.
+