| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - ner |
| - span-extraction |
| - token-classification |
| - data-mentions |
| - information-extraction |
| - displacement |
| task_categories: |
| - token-classification |
| configs: |
| - config_name: gliner_displacement |
| data_files: |
| - split: train |
| path: gliner_displacement_train.jsonl |
| - split: val |
| path: gliner_displacement_val.jsonl |
| - split: holdout |
| path: gliner_displacement_holdout.jsonl |
| - config_name: bio_displacement |
| data_files: |
| - split: train |
| path: bio_displacement_train.jsonl |
| - split: val |
| path: bio_displacement_val.jsonl |
| - split: holdout |
| path: bio_displacement_holdout.jsonl |
| - config_name: readable_displacement |
| data_files: |
| - split: train |
| path: readable_displacement_train.jsonl |
| - split: val |
| path: readable_displacement_val.jsonl |
| - split: holdout |
| path: readable_displacement_holdout.jsonl |
| --- |
| |
| # Data-Use Mentions — Displacement View |
|
|
| Derived from [`rafmacalaba/data-use-mentions`](https://huggingface.co/datasets/rafmacalaba/data-use-mentions) (originals untouched). Every source span keeps its label verdict, so the kept-vs-dropped split is explicitly trackable and auditable in every row — nothing is silently removed. |
|
|
| - **kept → `DATA_MENTION`**: evidential and declaration data-use mentions (the positive label). |
| - **dropped → `NON_MENTION`**: non-mentions and junk (the negative label). |
|
|
| ## Why “displacement” |
|
|
| Non-mention/junk spans are *displaced* from the positive label set — so training sees them as negatives — but stay in the data with their verdict attached, rather than being deleted from the record. |
|
|
| ## Configurations |
|
|
| | config | fields | purpose | |
| |---|---|---| |
| | `gliner_displacement` | `tokenized_text`, `corpus`, `origin`, `language`, `ner`, `spans` | GLiNER-style span extraction; `ner` lists only kept `DATA_MENTION` spans, `spans` carries the per-span `uid`, `tier`, and `source` for full auditability | |
| | `bio_displacement` | `tokens`, `ner_tags`, `spans`, `corpus`, `origin`, `language` | token-level BIO with two explicit classes (`DATA_MENTION`, `NON_MENTION`), overlap-safe longest-first; `spans` is the readable reconstruction of the tags | |
| | `readable_displacement` | `text`, `spans`, `corpus`, `origin`, `origin_model`, `language` | human-readable: full window `text` with character-offset `spans` (`{start, end, text, label, tier, source}`) | |
|
|
| ## Label scheme |
|
|
| | label | meaning | |
| |---|---| |
| | `DATA_MENTION` | kept span — evidential or declaration data-use mention (positive) | |
| | `NON_MENTION` | dropped span — non-mention or junk (negative) | |
|
|
| ## Verdict provenance |
|
|
| - Luna judgments win for any span they cover (val/holdout, plus the judged train subset). |
| - Unjudged train spans use the v3 classifier's predicted class (`train_v3_remaining_scores.jsonl`). |
| - A span with no resolvable id stays tagged `DATA_MENTION` (asymmetric keep: never risk dropping a positive). |
| - Same-surface conflicts keep when any positive verdict is present. |
| - A Luna review pass (`refine_band.py`, label-blind) adjudicated the ~6.3k highest-risk kept spans across all splits; spans it confirmed T3/junk flipped to `NON_MENTION` (source `luna_review`), confirmed T1/T2 stayed. |
|
|
| ## Splits and sizes |
|
|
| Total: **122,864 rows**, **148,584 spans** (117,380 kept, 31,204 dropped, 1,149 same-surface conflicts). |
|
|
| | split | rows | spans | kept (`DATA_MENTION`) | dropped (`NON_MENTION`) | conflicts | |
| |---|---:|---:|---:|---:|---:| |
| | train | 85,003 | 103,230 | 81,995 | 21,235 | 758 | |
| | val | 19,152 | 23,302 | 18,141 | 5,161 | 229 | |
| | holdout | 18,709 | 22,052 | 17,244 | 4,808 | 162 | |
|
|
| ## Source corpora |
|
|
| Rows come from the `fcv` and `prwp` corpora with five source origins: `fcv_pads_east_africa`, `general_prwp`, `reliefweb`, `refugee_pads`, `jdc_operational`. This dataset contains only real (non-synthetic) rows; `origin_model` is `""` for every row. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("rafmacalaba/datause-displacement", |
| "readable_displacement", split="train") |
| ``` |
|
|
| The `origin` field is normalized (legacy suffixes stripped). |
|
|
| `{"holdout": {"by_source": {"luna": 21409}, "conflict_spans": 162, "dropped_T3junk": 4808, "kept_T1T2": 17244, "review_drop_flips": 159, "review_keep_flips": 0, "rows": 18709, "spans": 22052, "tiers": {"junk": 299, "tier1_evidential": 13326, "tier2_declaration": 3918, "tier3_nonmention": 4509}}, "train": {"by_source": {"classifier": 70385, "luna": 26779}, "conflict_spans": 758, "dropped_T3junk": 21235, "kept_T1T2": 81995, "review_drop_flips": 1983, "review_keep_flips": 0, "rows": 85003, "spans": 103230, "tiers": {"junk": 1758, "tier1_evidential": 63880, "tier2_declaration": 18115, "tier3_nonmention": 19477}}, "val": {"by_source": {"luna": 22552}, "conflict_spans": 229, "dropped_T3junk": 5161, "kept_T1T2": 18141, "review_drop_flips": 172, "review_keep_flips": 0, "rows": 19152, "spans": 23302, "tiers": {"junk": 299, "tier1_evidential": 14169, "tier2_declaration": 3972, "tier3_nonmention": 4862}}}` |
|
|
