---
task_categories:
  - token-classification
tags:
  - ner
  - span-extraction
  - data-mentions
license: apache-2.0
configs:
  - config_name: gliner
    data_files:
      - split: holdout
        path: gliner_holdout.jsonl
  - config_name: to_annotate
    data_files:
      - split: holdout_annotate
        path: to_annotate_holdout_annotate.jsonl
  - config_name: annotate_paddy
    data_files:
      - split: holdout_annotate
        path: annotate_paddy.jsonl
  - config_name: annotate_aj
    data_files:
      - split: holdout_annotate
        path: annotate_aj.jsonl
  - config_name: annotate_aivin
    data_files:
      - split: holdout_annotate
        path: annotate_aivin.jsonl
  - config_name: annotate_rafael
    data_files:
      - split: holdout_annotate
        path: annotate_rafael.jsonl
  - config_name: annotate_paddy_part2
    data_files:
      - split: holdout_annotate
        path: annotate_paddy_part2.jsonl
  - config_name: annotate_aj_part2
    data_files:
      - split: holdout_annotate
        path: annotate_aj_part2.jsonl
  - config_name: annotate_aivin_part2
    data_files:
      - split: holdout_annotate
        path: annotate_aivin_part2.jsonl
  - config_name: annotate_rafael_part2
    data_files:
      - split: holdout_annotate
        path: annotate_rafael_part2.jsonl
---

# Data-use-ner (human holdout)

GLiNER-format human-adjudicated holdout: 473 spans — `annotator190` (190, `origin=fcv_pads_east_africa`) + `jdc283` (283, `origin=jdc_operational`). Never trained on.

Source: `rafmacalaba/datause-displacement-reviewed` holdout (`gliner_reviewed` token spans + `readable_reviewed` passages, v2.4 labels) with v3 probe `head_score` (`outputs/gliner_datause_v3_probe_human473.jsonl`).

## Columns

`text` (full passage = `" ".join(tokenized_text)`; span char offsets index into it) + `tokenized_text` (whitespace tokens) + `ner` (catch-all `DATA_MENTION` word spans; drops are `[]` by convention — truth is `spans[].luna_label`) + traceability `corpus_id`, `page`, `chunk`, `split`, `spans` (per-span `text`, `pred` @ threshold 0.5, `luna_label` 1/0 (v2.4 gold), `human_verdict` agree/disagree/unsure + `human_note` (raw annotator input), `head_score`, `threshold`, char `start`/`end` into `" ".join(tokenized_text)`, `key`, `source`).

## Usage

```python
from datasets import load_dataset
ds = load_dataset("rafmacalaba/data-use-ner", "gliner", split="holdout")  # 473 rows
```

```python
ann = load_dataset("rafmacalaba/data-use-ner", "to_annotate")  # Luna-verdict spans + passages, human_verdict null
```