Datasets:

gnumanth
/

nullspan-dataset

license: mit
task_categories:
  - question-answering
  - token-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - nullspan
  - system-one
  - structured-extraction
  - calibrated-abstention
  - prompt-injection-resistance
  - contract-extraction
size_categories:
  - 1K<n<10K

`gnumanth/nullspan-dataset`

Multi-task System One structured extraction, multi-numeric commercial contract disambiguation, calibrated abstention (p_null), boolean/enum judgment, cross-field invariant verification, and distributional quantile estimation dataset for gnumanth/nullspan (344.2M ELECTRA-Large + SchemaAdapterBlock 512-token cross-encoder).

Splits & Zero-Leakage Composition

train.jsonl: 8,290 field-level training instances across structured telemetry, multi-numeric commercial contracts, and strictly disjoint SQuAD v2.0 training passages (0% context/question overlap with NullspanBench).
cal_dev.jsonl: 720 disjoint calibration instances used exclusively for post-hoc temperature scaling (null_temperature, span_temperature, null_threshold_bias).
val.jsonl: 336 field-level Open Pool validation instances across 64 documents.
golden_eval.json: 4 canonical multi-field scenarios + 2 adversarial prompt-injection & decoy stress scenarios + 24 held-out validation documents.

Supervision Targets per Instance

is_null: Binary abstention label (1 when requested field is absent, unassigned, or negated in state) paired with abstention_reason and nearest decoy guess.
span: Exact character offsets [start, end] in state.
spans: Multi-span list offsets [[s1, e1], [s2, e2], ...] for type: "list" (B/I/O token sequence tags).
op: 10-way System One operation routing target:
- 0: SPAN_TEXT
- 1: SPAN_NUMBER
- 2: ARITH_SUM
- 3: ARITH_DIFF
- 4: ARITH_MUL
- 5: ISO_DATETIME
- 6: DATE_DURATION
- 7: DIST_ESTIMATE_FORECAST
- 8: BOOL_JUDGE
- 9: ENUM_CLASSIFY
quantiles_norm: Log/linear normalized [p10, median, p90] targets in [0, 1] trained with multi-quantile pinball loss.