README.md

metadata

task_categories:
  - token-classification
tags:
  - ner
  - span-extraction
  - data-mentions
license: apache-2.0
configs:
  - config_name: gliner
    data_files:
      - split: train
        path: gliner_train.jsonl
      - split: val
        path: gliner_val.jsonl
      - split: holdout
        path: gliner_holdout.jsonl
  - config_name: gliner2
    data_files:
      - split: train
        path: gliner2_train.jsonl
      - split: val
        path: gliner2_val.jsonl
      - split: holdout
        path: gliner2_holdout.jsonl
  - config_name: probe_splits
    data_files:
      - split: train
        path: probe_train.jsonl
      - split: val
        path: probe_val.jsonl
      - split: holdout
        path: probe_holdout.jsonl
  - config_name: probe_candidates
    data_files:
      - split: pool
        path: probe_candidate_pool.jsonl

Datause NER (catch-all DATA_MENTION + probe configs)

Catch-all NER views over rafmacalaba/datause-probe-v3 passages (29,346 spans grouped into passage examples). Single entity type DATA_MENTION: every candidate span is tagged, keeps and drops alike — the probe head (not NER tags) owns the keep/drop boundary. No NAMED/DESCRIPTIVE/VAGUE subtypes, no NON_MENTION.

Per-origin thresholds (head best-F1, published holdout sweep)

origin	threshold
fcv_pads_east_africa	0.5
general_prwp	0.3
jad_paddy_docs	0.3
jdc_operational	0.5
refugee_pads	0.6
reliefweb	0.3

Columns

• gliner: tokenized_text + ner — every candidate span tagged DATA_MENTION (catch-all; keeps and drops alike).
• gliner2: input + output (data_mention = all span strings).
• Keep/drop lives in spans[].luna_label + head_score/threshold traceability, not in tags — the probe head owns that boundary.
• Traceability on every row: corpus_id, page, chunk, split, spans (per-span text, pred, luna_label, head_score, threshold, char offsets, key, source). Luna keep/drop verdicts are preserved in spans[].luna_label for audit, but tagging follows the head-threshold operating point.

Splits

Doc-disjoint train / val / holdout, inherited from rafmacalaba/datause-probe-v3 (outputs/probe_v3_data), so NER splits match probe splits. See split_stats.json.

Usage

from datasets import load_dataset
gliner  = load_dataset("rafmacalaba/datause-ner", "gliner")
gliner2 = load_dataset("rafmacalaba/datause-ner", "gliner2")
# catch-all supervision: every span is DATA_MENTION:
#   gliner['train']['ner'] / gliner2['train']['output']

Files

• gliner_{train,val,holdout}.jsonl, gliner2_{train,val,holdout}.jsonl
• labels.json, thresholds.json, split_stats.json
• probe_{train,val,holdout}.jsonl, probe_candidate_pool.jsonl, probe_split_stats.json — mirror of rafmacalaba/datause-probe-v3 (same data, probe_* configs)

Probe configs (same data as rafmacalaba/datause-probe-v3)

splits = load_dataset("rafmacalaba/datause-ner", "probe_splits")
cands  = load_dataset("rafmacalaba/datause-ner", "probe_candidates")