gnumanth/nullspan

nullspan is a 67M-parameter System One extraction, calibrated abstention, invariant verification, and quantile distribution model. It processes an unstructured state and a map of typed fields (text, number, boolean, enum, date, datetime, duration, list, estimate, forecast) in a single parallel encoder forward pass with zero autoregressive token generation.

Architectural Enhancements (NullspanForExtraction)

Built on distilbert/distilbert-base-uncased (67M parameters) with 5 parallel System One heads and 10 routed operations:

1. null_head: Calibrated p_null probability (ECE: 0.00206, Brier: 0.0022) for absent field abstention (value: null + nearest guess decoy + abstention_reason).
2. span_head: Token-level start/end pointer network mapped via offset_mapping to exact character spans [start, end] with calibrated posterior confidence and Shannon span_entropy_bits.
3. bio_head: Class-weighted token-level B/I/O sequence tagger extracting ordered multi-span arrays (type: "list").
4. op_head: 10-way System One computation, normalization, boolean judgment (BOOL_JUDGE), and zero-shot enum classification (ENUM_CLASSIFY) router.
5. quantile_head: Pinball-loss calibrated distribution head (p10, median, p90) with monotonic sorting (p10 <= median <= p90) and cross-field algebraic constraint verification (verified: true/false).

Multi-Axis Empirical Evaluation (gnumanth/nullspan-dataset)

• Training time: 165.8s (Apple Silicon MPS)
• Null Abstention Accuracy (p_null): 99.75% (ECE: 0.00206, Brier: 0.0022)
• Grounded Span Exact Match ([start, end]): 99.24%
• 10-Way Operation Router Accuracy: 100.0%
• Multi-Span List BIO Token Accuracy: 100.0%
• Quantile Distribution MAE / Pinball Loss: 0.0007 / 0.0001
• Canonical Multi-Task Suite Accuracy: 100.0%
• Adversarial Decoy & Prompt-Injection Stress Accuracy: 100.0%
• Cross-Field Invariant Verification Accuracy: 100.0%

Quick Start

from transformers import AutoModel, AutoTokenizer

repo_id = "gnumanth/nullspan"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)

incident = (
    "Incident INC-40928 opened on Apr 12, 2026 with a 502 Bad Gateway errors and a TLS handshake timeouts. "
    "Issued $1240.50 plus $310.25 SLA credit. Severity is sev-1 and customer data loss was false. "
    "Root cause CVE not yet assigned. Cluster stabilized on Apr 19, 2026. Reported by Mira"
)

result = model.extract(
    state=incident,
    fields={
        "incident_id": {"type": "text", "instructions": "Incident ticket identifier"},
        "opened_on": {"type": "date", "instructions": "Date the incident opened"},
        "total_credit": {"type": "number", "decimals": 2, "unit": "USD", "instructions": "Total credit including SLA penalty"},
        "outage_days": {"type": "duration", "unit": "days", "integer": True, "instructions": "Days from incident open to cluster stabilization"},
        "failure_modes": {"type": "list", "items": "text", "instructions": "Each failure mode triggered"},
        "severity": {"type": "enum", "options": ["sev-1", "sev-2", "sev-3"], "instructions": "Incident severity tier"},
        "data_loss": {"type": "boolean", "instructions": "Whether customer data loss occurred"},
        "cve_id": {"type": "text", "instructions": "Assigned CVE identifier if present"},
        "remediation_cost": {"type": "estimate", "unit": "USD", "min": 100, "max": 25000, "scale": "log", "step": 10, "instructions": "Estimated engineering remediation cost"},
    },
    tokenizer=tokenizer,
)
print(result["answers"])

Output on SRE Incident Postmortem Example

{
  "reporter": {
    "type": "text",
    "p_null": 1.2e-06,
    "span": [
      276,
      280
    ],
    "guess": "Mira",
    "confidence": 0.999992,
    "span_entropy_bits": 0.0001,
    "value": "Mira"
  },
  "incident_id": {
    "type": "text",
    "p_null": 1.3e-06,
    "span": [
      9,
      18
    ],
    "guess": "INC-40928",
    "confidence": 0.999994,
    "span_entropy_bits": 0.0001,
    "value": "INC-40928"
  },
  "opened_on": {
    "type": "date",
    "p_null": 4e-08,
    "span": [
      29,
      41
    ],
    "guess": "2026-04-12",
    "confidence": 0.999999,
    "span_entropy_bits": 0.0,
    "value": "2026-04-12"
  },
  "total_credit": {
    "type": "number",
    "p_null": 7.51e-06,
    "span": [
      109,
      130
    ],
    "guess": 1550.75,
    "confidence": 0.99998,
    "span_entropy_bits": 0.0003,
    "value": 1550.75
  },
  "outage_days": {
    "type": "duration",
    "p_null": 3.18e-06,
    "unit": "days",
    "span": [
      29,
      41
    ],
    "guess": 7,
    "confidence": 0.999995,
    "span_entropy_bits": 0.0001,
    "value": 7
  },
  "failure_modes": {
    "type": "list",
    "value": [
      "502 Bad Gateway errors",
      "TLS handshake timeouts"
    ],
    "spans": [
      [
        49,
        71
      ],
      [
        78,
        100
      ]
    ],
    "confidence": 0.999932,
    "span_entropy_bits": 0.0011
  },
  "severity": {
    "type": "enum",
    "p_null": 4.779e-05,
    "span": [
      155,
      160
    ],
    "options": [
      "sev-1",
      "sev-2",
      "sev-3"
    ],
    "probs": {
      "sev-1": 0.99994,
      "sev-2": 3e-05,
      "sev-3": 3e-05
    },
    "guess": "sev-1",
    "confidence": 0.99994,
    "span_entropy_bits": 0.0003,
    "value": "sev-1"
  },
  "data_loss": {
    "type": "boolean",
    "p_null": 3.117e-05,
    "p_true": 3.7e-05,
    "span": [
      188,
      193
    ],
    "guess": false,
    "confidence": 0.999963,
    "span_entropy_bits": 0.0002,
    "value": false
  },
  "cve_id": {
    "type": "text",
    "p_null": 0.99994409,
    "span": [
      123,
      130
    ],
    "guess": "$310.25",
    "confidence": 0.05,
    "span_entropy_bits": 0.0008,
    "value": null,
    "abstention_reason": "unassigned_placeholder"
  },
  "remediation_cost": {
    "type": "estimate",
    "value": 1890,
    "median": 1870,
    "p10": 1270,
    "p90": 2660,
    "p_below": 0.0,
    "p_above": 0.0,
    "confidence": 0.999963
  }
}

License

MIT © Hemanth.HM