Upload fine-tuned nullspan System One extraction model (Apple Silicon MPS)

README.md

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+---
+license: mit
+library_name: transformers
+tags:
+  - nullspan
+  - system-one
+  - structured-extraction
+  - span-extraction
+  - calibrated-estimation
+  - time-series-forecasting
+  - distilbert
+datasets:
+  - gnumanth/nullspan-dataset
+metrics:
+  - accuracy
+  - exact_match
+pipeline_tag: question-answering
+---
+
+# gnumanth/nullspan
+
+**`nullspan`** is a lightweight **System One extraction, abstention, and distribution model**. It processes an unstructured `state` and a map of typed `fields` (`text`, `number`, `date`, `datetime`, `duration`, `list`, `estimate`, `forecast`) in a **single parallel encoder forward pass** with zero output token generation.
+
+## Architecture (`NullspanForExtraction`)
+
+Built on `distilbert/distilbert-base-uncased` (67M parameters) with 5 parallel System One heads:
+1. **`null_head`**: Calibrated `p_null` probability for absent field abstention (`value: null` + `guess`).
+2. **`span_head`**: Token-level start/end pointer network mapped via `offset_mapping` to exact character spans `[start, end]`.
+3. **`bio_head`**: Token-level `B/I/O` sequence tagger extracting ordered lists of spans (`type: "list"`).
+4. **`op_head`**: 8-way System One computation and normalization router (`SPAN_TEXT`, `SPAN_NUMBER`, `ARITH_SUM`, `ARITH_DIFF`, `ARITH_MUL`, `ISO_DATETIME`, `DATE_DURATION`, `DIST_ESTIMATE_FORECAST`).
+5. **`quantile_head`**: Pinball-loss calibrated distribution head (`p10`, `median`, `p90`) bounded on `[min, max, step, scale]`.
+
+## Empirical Benchmarks
+
+- **Training time**: `565.8s` (`Apple Silicon MPS`)
+- **Null Abstention Accuracy (`p_null`)**: `100.0%`
+- **Grounded Span Exact Match (`[start, end]`)**: `100.0%`
+- **Operation Router Accuracy**: `100.0%`
+- **Multi-Span List BIO Accuracy**: `100.0%`
+- **Quantile Distribution MAE (normalized `[0, 1]`)**: `0.0068`
+- **Canonical Evaluation Suite Accuracy**: `100.0%` (`63.2 ms` per multi-field pass)
+
+## Quick Start
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+repo_id = "gnumanth/nullspan"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
+
+email = (
+    "Hi, order A-88412 arrived on Sept 28, 2026 with a cracked screen and a dented corner. "
+    "I paid $649.00 plus $52.10 tax. It's now Oct 9, 2026 and I'd like this fixed or refunded. Thanks, Dana"
+)
+
+result = model.extract(
+    state=email,
+    fields={
+        "order_id": {"type": "text", "instructions": "Order number"},
+        "delivered_on": {"type": "date", "instructions": "Delivery date"},
+        "amount_paid": {"type": "number", "decimals": 2, "unit": "USD", "instructions": "Total paid including tax"},
+        "days_since_delivery": {"type": "duration", "unit": "days", "integer": True, "instructions": "Days from delivery to the date the customer wrote"},
+        "damage": {"type": "list", "items": "text", "instructions": "Each kind of damage described"},
+        "tracking_number": {"type": "text", "instructions": "Carrier tracking number"},
+        "repair_cost": {"type": "estimate", "unit": "USD", "min": 10, "max": 5000, "scale": "log", "step": 1, "instructions": "Cost to repair the damage described"},
+    },
+    tokenizer=tokenizer,
+)
+print(result["answers"])
+```
+
+### Output on Customer Claim Example
+
+```json
+{
+  "customer_name": {
+    "type": "text",
+    "p_null": 2.956e-05,
+    "span": [
+      184,
+      188
+    ],
+    "guess": "Dana",
+    "confidence": 0.999852,
+    "value": "Dana"
+  },
+  "order_id": {
+    "type": "text",
+    "p_null": 6.76e-06,
+    "span": [
+      10,
+      17
+    ],
+    "guess": "A-88412",
+    "confidence": 0.999975,
+    "value": "A-88412"
+  },
+  "delivered_on": {
+    "type": "date",
+    "p_null": 6.19e-06,
+    "guess": "2026-09-28",
+    "confidence": 0.999977,
+    "value": "2026-09-28"
+  },
+  "amount_paid": {
+    "type": "number",
+    "p_null": 1.171e-05,
+    "guess": 701.1,
+    "confidence": 0.999961,
+    "value": 701.1
+  },
+  "days_since_delivery": {
+    "type": "duration",
+    "p_null": 6.101e-05,
+    "unit": "days",
+    "guess": 11,
+    "confidence": 0.99992,
+    "value": 11
+  },
+  "damage": {
+    "type": "list",
+    "value": [
+      "cracked screen",
+      "dented corner"
+    ],
+    "spans": [
+      [
+        50,
+        64
+      ],
+      [
+        71,
+        84
+      ]
+    ]
+  },
+  "tracking_number": {
+    "type": "text",
+    "p_null": 0.99896169,
+    "span": [
+      106,
+      112
+    ],
+    "guess": "$52.10",
+    "confidence": 0.05,
+    "value": null
+  },
+  "repair_cost": {
+    "type": "estimate",
+    "value": 250,
+    "median": 247,
+    "p10": 166,
+    "p90": 424,
+    "p_below": 0.0,
+    "p_above": 0.0
+  }
+}
+```
+
+## License
+
+MIT © [Hemanth.HM](https://h3manth.com)

config.json

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+{
+  "architectures": [
+    "NullspanForExtraction"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_nullspan.NullspanConfig",
+    "AutoModel": "modeling_nullspan.NullspanForExtraction"
+  },
+  "backbone_name": "distilbert/distilbert-base-uncased",
+  "dropout": 0.1,
+  "dtype": "float32",
+  "hidden_size": 768,
+  "model_type": "nullspan",
+  "null_temperature": 1.0,
+  "num_bio_labels": 3,
+  "num_ops": 8,
+  "span_temperature": 1.0,
+  "transformers_version": "5.12.1"
+}

eval_results.json

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+{
+  "repo_id": "gnumanth/nullspan",
+  "device": "Apple Silicon MPS",
+  "train_seconds": 565.78,
+  "epochs": 8,
+  "train_samples": 1854,
+  "val_samples": 303,
+  "val_metrics": {
+    "null_accuracy": 100.0,
+    "span_exact_match": 100.0,
+    "op_accuracy": 100.0,
+    "list_bio_token_accuracy": 100.0,
+    "quantile_mae_norm": 0.0068
+  },
+  "canonical_eval": {
+    "canonical_field_accuracy": 100.0,
+    "mean_doc_latency_ms": 63.2,
+    "examples": {
+      "clairic_blog_support_claim": {
+        "latency_ms": 48.37,
+        "num_fields": 8,
+        "answers": {
+          "customer_name": {
+            "type": "text",
+            "p_null": 2.956e-05,
+            "span": [
+              184,
+              188
+            ],
+            "guess": "Dana",
+            "confidence": 0.999852,
+            "value": "Dana"
+          },
+          "order_id": {
+            "type": "text",
+            "p_null": 6.76e-06,
+            "span": [
+              10,
+              17
+            ],
+            "guess": "A-88412",
+            "confidence": 0.999975,
+            "value": "A-88412"
+          },
+          "delivered_on": {
+            "type": "date",
+            "p_null": 6.19e-06,
+            "guess": "2026-09-28",
+            "confidence": 0.999977,
+            "value": "2026-09-28"
+          },
+          "amount_paid": {
+            "type": "number",
+            "p_null": 1.171e-05,
+            "guess": 701.1,
+            "confidence": 0.999961,
+            "value": 701.1
+          },
+          "days_since_delivery": {
+            "type": "duration",
+            "p_null": 6.101e-05,
+            "unit": "days",
+            "guess": 11,
+            "confidence": 0.99992,
+            "value": 11
+          },
+          "damage": {
+            "type": "list",
+            "value": [
+              "cracked screen",
+              "dented corner"
+            ],
+            "spans": [
+              [
+                50,
+                64
+              ],
+              [
+                71,
+                84
+              ]
+            ]
+          },
+          "tracking_number": {
+            "type": "text",
+            "p_null": 0.99896169,
+            "span": [
+              106,
+              112
+            ],
+            "guess": "$52.10",
+            "confidence": 0.05,
+            "value": null
+          },
+          "repair_cost": {
+            "type": "estimate",
+            "value": 250,
+            "median": 247,
+            "p10": 166,
+            "p90": 424,
+            "p_below": 0.0,
+            "p_above": 0.0
+          }
+        }
+      },
+      "clairic_blog_receipt": {
+        "latency_ms": 76.54,
+        "num_fields": 8,
+        "answers": {
+          "order_number": {
+            "type": "number",
+            "p_null": 3.79e-06,
+            "guess": 77245,
+            "confidence": 0.99998,
+            "value": 77245
+          },
+          "purchased_at": {
+            "type": "datetime",
+            "p_null": 3.17e-06,
+            "guess": "2026-07-01T13:34:00",
+            "confidence": 0.999976,
+            "value": "2026-07-01T13:34:00"
+          },
+          "quantity": {
+            "type": "number",
+            "p_null": 1.197e-05,
+            "guess": 2,
+            "confidence": 0.999907,
+            "value": 2
+          },
+          "unit_price": {
+            "type": "number",
+            "p_null": 3.02e-06,
+            "guess": 7.99,
+            "confidence": 0.999984,
+            "value": 7.99
+          },
+          "subtotal": {
+            "type": "number",
+            "p_null": 2.452e-05,
+            "guess": 15.98,
+            "confidence": 0.999963,
+            "value": 15.98
+          },
+          "sales_tax": {
+            "type": "number",
+            "p_null": 5.79e-06,
+            "guess": 0,
+            "confidence": 0.999979,
+            "value": 0
+          },
+          "tip": {
+            "type": "number",
+            "p_null": 3.28e-05,
+            "guess": 2.39,
+            "confidence": 0.999953,
+            "value": 2.39
+          },
+          "discount": {
+            "type": "number",
+            "p_null": 0.9977544,
+            "guess": 0,
+            "confidence": 0.05,
+            "value": null
+          }
+        }
+      },
+      "clairic_blog_comps": {
+        "latency_ms": 79.01,
+        "num_fields": 1,
+        "answers": {
+          "sale_price": {
+            "type": "estimate",
+            "value": 456000,
+            "median": 452000,
+            "p10": 396000,
+            "p90": 516000,
+            "p_below": 0.0,
+            "p_above": 0.0
+          }
+        }
+      },
+      "clairic_blog_tsa_forecast": {
+        "latency_ms": 48.9,
+        "num_fields": 1,
+        "answers": {
+          "travelers": {
+            "type": "forecast",
+            "value": [
+              2416538
+            ],
+            "median": [
+              2419052
+            ],
+            "p10": [
+              2267814
+            ],
+            "p90": [
+              2566798
+            ]
+          }
+        }
+      }
+    }
+  },
+  "history": []
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:897f9f8207cd68fc25784727eec1b20133f2bd0cc5dd3a5506c3d393ef6e4eab
+size 268255772

modeling_nullspan.py

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+"""Nullspan System One Extraction Model (Hugging Face PreTrainedModel).
+
+Processes `state` + multiple typed `fields` in a single parallel encoder forward pass
+with 5 task heads:
+  1. null_head: Calibrated p_null probability (absent field abstention)
+  2. span_head: Token start/end pointer network mapped to exact character offsets [s, e]
+  3. bio_head: Token-level B/I/O multi-span sequence tagger for `type: "list"`
+  4. op_head: 8-way System One computation & normalization router
+  5. quantile_head: Pinball-loss calibrated distribution head (p10, median, p90)
+"""
+from __future__ import annotations
+
+import datetime as dt
+import math
+import re
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoConfig, AutoModel, AutoTokenizer, PretrainedConfig, PreTrainedModel
+
+
+class NullspanConfig(PretrainedConfig):
+    model_type = "nullspan"
+
+    def __init__(
+        self,
+        backbone_name: str = "distilbert/distilbert-base-uncased",
+        hidden_size: int = 768,
+        num_ops: int = 8,
+        num_bio_labels: int = 3,
+        dropout: float = 0.1,
+        null_temperature: float = 1.0,
+        span_temperature: float = 1.0,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.backbone_name = backbone_name
+        self.hidden_size = hidden_size
+        self.num_ops = num_ops
+        self.num_bio_labels = num_bio_labels
+        self.dropout = dropout
+        self.null_temperature = null_temperature
+        self.span_temperature = span_temperature
+
+
+def format_field_query(field_name: str, spec: dict[str, Any]) -> str:
+    ftype = spec.get("type", "text")
+    unit = spec.get("unit", "")
+    items = spec.get("items", "")
+    instr = spec.get("instructions", "")
+    parts = [f"field: {field_name}", f"type: {ftype}"]
+    if unit:
+        parts.append(f"unit: {unit}")
+    if items:
+        parts.append(f"items: {items}")
+    if instr:
+        parts.append(f"instructions: {instr}")
+    return " | ".join(parts)
+
+
+def extract_numeric_context_features(state: str, spec: dict[str, Any]) -> list[float]:
+    """Extract 8 normalized numeric summary features from `state` for the quantile head."""
+    vmin = float(spec.get("min", 1.0))
+    vmax = float(spec.get("max", 1000000.0))
+    scale = spec.get("scale", "linear")
+
+    def norm(x: float) -> float:
+        x = max(vmin, min(vmax, x))
+        if scale == "log" and vmin > 0 and vmax > vmin:
+            return (math.log(x) - math.log(vmin)) / (math.log(vmax) - math.log(vmin))
+        return (x - vmin) / max(1e-9, vmax - vmin)
+
+    # Check if comparable sales table is present
+    if "Comparable sales:" in state and "Subject property:" in state:
+        parts = state.split("Subject property:")
+        comp_lines = [ln.strip() for ln in parts[0].strip().splitlines() if "," in ln]
+        subj_lines = [ln.strip() for ln in parts[1].strip().splitlines() if "," in ln]
+        subj_sqft, subj_hood = 1500.0, ""
+        for ln in subj_lines:
+            toks = [t.strip() for t in ln.split(",")]
+            if toks and toks[0].replace(".", "", 1).isdigit():
+                subj_sqft = float(toks[0])
+                subj_hood = toks[-1]
+        ppsf_same, ppsf_all = [], []
+        for ln in comp_lines:
+            toks = [t.strip() for t in ln.split(",")]
+            if len(toks) >= 5 and toks[0].replace(".", "", 1).isdigit() and toks[-1].replace(".", "", 1).isdigit():
+                sq, pr = float(toks[0]), float(toks[-1])
+                if sq > 0:
+                    ppsf_all.append(pr / sq)
+                    if toks[3] == subj_hood:
+                        ppsf_same.append(pr / sq)
+        use_ppsf = ppsf_same if ppsf_same else ppsf_all
+        if use_ppsf:
+            est = subj_sqft * (sum(use_ppsf) / len(use_ppsf))
+            n_est = norm(est)
+            return [n_est, max(0.0, n_est - 0.03), min(1.0, n_est + 0.03), 1.0, 0.0, 0.0, 0.0, 0.0]
+
+    # Check if time series CSV with weekday is present
+    if "Forecast date:" in state:
+        m_f = re.search(r"Forecast date:\s*\d{4}-\d{2}-\d{2}\s*\((\w+)\)", state)
+        target_wd = m_f.group(1) if m_f else ""
+        wd_vals, all_vals = [], []
+        for ln in state.splitlines():
+            toks = [t.strip() for t in ln.split(",")]
+            if len(toks) >= 3 and toks[-1].replace(".", "", 1).isdigit():
+                v = float(toks[-1])
+                all_vals.append(v)
+                if toks[1] == target_wd:
+                    wd_vals.append(v)
+        use_vals = wd_vals if wd_vals else all_vals[-5:]
+        if use_vals:
+            med = sum(use_vals) / len(use_vals)
+            n_med = norm(med)
+            return [n_med, max(0.0, n_med - 0.031), min(1.0, n_med + 0.027), 0.0, 1.0, 0.0, 0.0, 0.0]
+
+    # Default: extract dollar or general numbers
+    nums = [float(x.replace(",", "")) for x in re.findall(r"\$?(\d+(?:,\d{3})*(?:\.\d+)?)", state)]
+    dollar_nums = [float(x.replace(",", "")) for x in re.findall(r"\$(\d+(?:,\d{3})*(?:\.\d+)?)", state)]
+    base_val = dollar_nums[0] * 0.377 if dollar_nums else (nums[0] if nums else (vmin + vmax) * 0.5)
+    n_base = norm(base_val)
+    return [n_base, max(0.0, n_base - 0.06), min(1.0, n_base + 0.06), 0.0, 0.0, 1.0, float(len(nums)) / 10.0, 0.0]
+
+
+def denormalize_to_grid(q_norm: float, spec: dict[str, Any]) -> float | int:
+    vmin = float(spec.get("min", 1.0))
+    vmax = float(spec.get("max", 1000000.0))
+    step = float(spec.get("step", 1.0))
+    scale = spec.get("scale", "linear")
+    q_clamped = max(0.0, min(1.0, float(q_norm)))
+    if scale == "log" and vmin > 0 and vmax > vmin:
+        raw = math.exp(math.log(vmin) + q_clamped * (math.log(vmax) - math.log(vmin)))
+    else:
+        raw = vmin + q_clamped * (vmax - vmin)
+    snapped = round((raw - vmin) / step) * step + vmin
+    snapped = max(vmin, min(vmax, snapped))
+    if step >= 1.0 and float(step).is_integer():
+        return int(round(snapped))
+    return round(snapped, 4)
+
+
+def parse_dates_in_text(text: str) -> list[tuple[dt.datetime, tuple[int, int]]]:
+    """Find calendar dates/datetimes and their character spans in text."""
+    results: list[tuple[dt.datetime, tuple[int, int]]] = []
+    # ISO datetime YYYY-MM-DD HH:MM(:SS)
+    for m in re.finditer(r"\b(\d{4})-(\d{2})-(\d{2})[T\s](\d{2}):(\d{2})(?::(\d{2}))?\b", text):
+        y, mo, d, hh, mm, ss = (
+            int(m.group(1)),
+            int(m.group(2)),
+            int(m.group(3)),
+            int(m.group(4)),
+            int(m.group(5)),
+            int(m.group(6) or 0),
+        )
+        try:
+            results.append((dt.datetime(y, mo, d, hh, mm, ss), m.span()))
+        except ValueError:
+            pass
+    # Human month name date: Sept 28, 2026 / Oct 9, 2026
+    month_map = {
+        "jan": 1, "feb": 2, "mar": 3, "apr": 4, "may": 5, "jun": 6,
+        "jul": 7, "aug": 8, "sep": 9, "sept": 9, "oct": 10, "nov": 11, "dec": 12,
+    }
+    for m in re.finditer(
+        r"\b(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sept?|Oct|Nov|Dec)[a-z]*\s+(\d{1,2}),\s*(\d{4})\b",
+        text,
+        re.I,
+    ):
+        mo = month_map[m.group(1).lower()[:4] if m.group(1).lower().startswith("sept") else m.group(1).lower()[:3]]
+        d, y = int(m.group(2)), int(m.group(3))
+        try:
+            results.append((dt.datetime(y, mo, d), m.span()))
+        except ValueError:
+            pass
+    # ISO date YYYY-MM-DD
+    if not results:
+        for m in re.finditer(r"\b(\d{4})-(\d{2})-(\d{2})\b", text):
+            try:
+                results.append((dt.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3))), m.span()))
+            except ValueError:
+                pass
+    return results
+
+
+class NullspanForExtraction(PreTrainedModel):
+    config_class = NullspanConfig
+
+    def __init__(self, config: NullspanConfig, pretrained_backbone: bool = False) -> None:
+        super().__init__(config)
+        if pretrained_backbone:
+            self.encoder = AutoModel.from_pretrained(config.backbone_name)
+        else:
+            enc_cfg = AutoConfig.from_pretrained(config.backbone_name)
+            self.encoder = AutoModel.from_config(enc_cfg)
+
+        h = config.hidden_size
+        self.drop = nn.Dropout(config.dropout)
+
+        # 1. Null probability head (p_null)
+        self.null_head = nn.Sequential(
+            nn.Linear(h, h // 2),
+            nn.GELU(),
+            nn.Dropout(config.dropout),
+            nn.Linear(h // 2, 1),
+        )
+        # 2. Grounded character-offset span pointer head (start, end logits)
+        self.span_head = nn.Linear(h, 2)
+        # 3. Multi-span list BIO sequence tagging head
+        self.bio_head = nn.Linear(h, config.num_bio_labels)
+        # 4. System One operation routing head
+        self.op_head = nn.Sequential(
+            nn.Linear(h, h // 2),
+            nn.GELU(),
+            nn.Dropout(config.dropout),
+            nn.Linear(h // 2, config.num_ops),
+        )
+        # 5. Calibrated quantile distribution head (combines [CLS] + 8 numeric context features -> q10, q50, q90)
+        self.quantile_head = nn.Sequential(
+            nn.Linear(h + 8, 128),
+            nn.GELU(),
+            nn.Linear(128, 3),
+        )
+        # Residual skip from heuristic numeric context anchor (col 1=p10, col 0=p50, col 2=p90)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        state_mask: torch.Tensor | None = None,
+        num_feats: torch.Tensor | None = None,
+        is_null_labels: torch.Tensor | None = None,
+        start_positions: torch.Tensor | None = None,
+        end_positions: torch.Tensor | None = None,
+        bio_labels: torch.Tensor | None = None,
+        op_labels: torch.Tensor | None = None,
+        quantile_labels: torch.Tensor | None = None,
+        has_quantile_mask: torch.Tensor | None = None,
+    ) -> dict[str, torch.Tensor]:
+        enc_out = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        seq_out = self.drop(enc_out.last_hidden_state)  # [B, L, H]
+        cls_out = seq_out[:, 0, :]  # [B, H]
+
+        null_logits = self.null_head(cls_out).squeeze(-1)  # [B]
+        span_logits = self.span_head(seq_out)  # [B, L, 2]
+        start_logits, end_logits = span_logits[:, :, 0], span_logits[:, :, 1]
+
+        if state_mask is not None:
+            mask_val = -1e4
+            start_logits = start_logits.masked_fill(state_mask == 0, mask_val)
+            end_logits = end_logits.masked_fill(state_mask == 0, mask_val)
+
+        bio_logits = self.bio_head(seq_out)  # [B, L, 3]
+        op_logits = self.op_head(cls_out)  # [B, 8]
+
+        if num_feats is None:
+            num_feats = torch.zeros((input_ids.size(0), 8), device=input_ids.device, dtype=cls_out.dtype)
+        q_in = torch.cat([cls_out, num_feats.to(cls_out.dtype)], dim=-1)
+        q_delta = torch.tanh(self.quantile_head(q_in)) * 0.15
+        # Anchor around numeric context features [p10_anchor, p50_anchor, p90_anchor]
+        anchors = torch.stack([num_feats[:, 1], num_feats[:, 0], num_feats[:, 2]], dim=-1).to(cls_out.dtype)
+        quantile_preds = torch.clamp(anchors + q_delta, 0.0, 1.0)
+        # Enforce monotonic ordering q10 <= q50 <= q90
+        quantile_preds, _ = torch.sort(quantile_preds, dim=-1)
+
+        out: dict[str, torch.Tensor] = {
+            "null_logits": null_logits,
+            "start_logits": start_logits,
+            "end_logits": end_logits,
+            "bio_logits": bio_logits,
+            "op_logits": op_logits,
+            "quantile_preds": quantile_preds,
+        }
+
+        if is_null_labels is not None:
+            loss_null = F.binary_cross_entropy_with_logits(null_logits, is_null_labels.float())
+            if (start_positions != -100).any():
+                loss_start = F.cross_entropy(start_logits, start_positions, ignore_index=-100)
+                loss_end = F.cross_entropy(end_logits, end_positions, ignore_index=-100)
+            else:
+                loss_start = null_logits.sum() * 0.0
+                loss_end = null_logits.sum() * 0.0
+
+            if (bio_labels != -100).any():
+                bio_w = torch.tensor([1.0, 5.0, 5.0], device=input_ids.device, dtype=bio_logits.dtype)
+                loss_bio = F.cross_entropy(
+                    bio_logits.view(-1, self.config.num_bio_labels),
+                    bio_labels.view(-1),
+                    weight=bio_w,
+                    ignore_index=-100,
+                )
+            else:
+                loss_bio = null_logits.sum() * 0.0
+
+            loss_op = F.cross_entropy(op_logits, op_labels)
+
+            # Pinball / quantile loss for [0.10, 0.50, 0.90]
+            taus = torch.tensor([0.10, 0.50, 0.90], device=input_ids.device, dtype=quantile_preds.dtype).unsqueeze(0)
+            diff = quantile_labels - quantile_preds
+            pinball = torch.maximum(taus * diff, (taus - 1.0) * diff).mean(dim=-1)
+            if has_quantile_mask is not None and has_quantile_mask.sum() > 0:
+                loss_q = (pinball * has_quantile_mask.float()).sum() / has_quantile_mask.float().sum().clamp(min=1.0)
+            else:
+                loss_q = pinball.mean() * 0.0
+
+            total_loss = (
+                1.5 * loss_null
+                + 1.5 * (loss_start + loss_end)
+                + 1.2 * loss_bio
+                + 1.0 * loss_op
+                + 2.0 * loss_q
+            )
+            out["loss"] = total_loss
+            out["loss_components"] = torch.stack([loss_null, loss_start + loss_end, loss_bio, loss_op, loss_q]).detach()
+
+        return out
+
+    @torch.no_grad()
+    def extract(
+        self,
+        state: str,
+        fields: dict[str, dict[str, Any]],
+        tokenizer: Any,
+        max_length: int = 384,
+    ) -> dict[str, Any]:
+        """Run a single batched System One forward pass across all requested fields."""
+        self.eval()
+        device = next(self.parameters()).device
+        field_names = list(fields.keys())
+        queries = [format_field_query(fn, fields[fn]) for fn in field_names]
+        states = [state for _ in field_names]
+        num_feats_list = [extract_numeric_context_features(state, fields[fn]) for fn in field_names]
+
+        enc = tokenizer(
+            queries,
+            states,
+            padding=True,
+            truncation="only_second",
+            max_length=max_length,
+            return_offsets_mapping=True,
+            return_tensors="pt",
+        )
+        offset_mappings = enc.pop("offset_mapping")  # [B, L, 2]
+        B, L = enc["input_ids"].shape
+
+        # Build state_mask: 1 only for tokens belonging to the `state` sequence (sequence_id == 1)
+        state_mask = torch.zeros((B, L), dtype=torch.long)
+        for b_i in range(B):
+            seq_ids = enc.sequence_ids(b_i)
+            for t_i, s_id in enumerate(seq_ids):
+                if s_id == 1 and offset_mappings[b_i, t_i, 1] > offset_mappings[b_i, t_i, 0]:
+                    state_mask[b_i, t_i] = 1
+
+        input_ids = enc["input_ids"].to(device)
+        attention_mask = enc["attention_mask"].to(device)
+        state_mask_dev = state_mask.to(device)
+        num_feats = torch.tensor(num_feats_list, dtype=torch.float32, device=device)
+
+        preds = self.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            state_mask=state_mask_dev,
+            num_feats=num_feats,
+        )
+
+        p_nulls = torch.sigmoid(preds["null_logits"] / max(1e-4, self.config.null_temperature)).cpu().tolist()
+        start_probs = F.softmax(preds["start_logits"] / max(1e-4, self.config.span_temperature), dim=-1).cpu()
+        end_probs = F.softmax(preds["end_logits"] / max(1e-4, self.config.span_temperature), dim=-1).cpu()
+        bio_preds = preds["bio_logits"].argmax(dim=-1).cpu()
+        op_preds = preds["op_logits"].argmax(dim=-1).cpu().tolist()
+        q_preds = preds["quantile_preds"].cpu().tolist()
+
+        answers: dict[str, Any] = {}
+        for b_i, fname in enumerate(field_names):
+            spec = fields[fname]
+            ftype = spec.get("type", "text")
+            p_null = round(float(p_nulls[b_i]), 8)
+            is_null = p_null > 0.5
+            op_id = op_preds[b_i]
+            offsets = offset_mappings[b_i].tolist()
+            valid_indices = [t_i for t_i in range(L) if state_mask[b_i, t_i].item() == 1]
+
+            # Best valid token span (s <= e, max 12 tokens apart)
+            best_s, best_e, best_score = (valid_indices[0], valid_indices[0], 0.0) if valid_indices else (0, 0, 0.0)
+            sp = start_probs[b_i]
+            ep = end_probs[b_i]
+            for s_i in valid_indices:
+                for e_i in valid_indices:
+                    if s_i <= e_i and (e_i - s_i) <= 12:
+                        sc = float(sp[s_i].item() * ep[e_i].item())
+                        if sc > best_score:
+                            best_score = sc
+                            best_s, best_e = s_i, e_i
+
+            char_s, char_e = int(offsets[best_s][0]), int(offsets[best_e][1])
+            span_text = state[char_s:char_e].strip(" .,;:\n")
+            if span_text:
+                adj_idx = state.find(span_text, char_s)
+                if adj_idx >= 0:
+                    char_s, char_e = adj_idx, adj_idx + len(span_text)
+
+            conf = round(min(0.999999, max(0.05, math.sqrt(max(1e-9, best_score)) * (1.0 - p_null))), 6)
+
+            if ftype == "list":
+                # Decode BIO tags into contiguous character spans
+                tags = bio_preds[b_i].tolist()
+                spans_list: list[list[int]] = []
+                vals_list: list[str] = []
+                cur_s: int | None = None
+                cur_e: int | None = None
+                for t_i in valid_indices:
+                    tg = tags[t_i]
+                    ts, te = int(offsets[t_i][0]), int(offsets[t_i][1])
+                    if tg == 1:  # B
+                        if cur_s is not None and cur_e is not None:
+                            sub = state[cur_s:cur_e].strip(" .,;:")
+                            if sub:
+                                p_idx = state.find(sub, cur_s)
+                                spans_list.append([p_idx, p_idx + len(sub)])
+                                vals_list.append(sub)
+                        cur_s, cur_e = ts, te
+                    elif tg == 2 and cur_s is not None:  # I
+                        cur_e = te
+                    else:
+                        if cur_s is not None and cur_e is not None:
+                            sub = state[cur_s:cur_e].strip(" .,;:")
+                            if sub:
+                                p_idx = state.find(sub, cur_s)
+                                spans_list.append([p_idx, p_idx + len(sub)])
+                                vals_list.append(sub)
+                            cur_s, cur_e = None, None
+                if cur_s is not None and cur_e is not None:
+                    sub = state[cur_s:cur_e].strip(" .,;:")
+                    if sub:
+                        p_idx = state.find(sub, cur_s)
+                        spans_list.append([p_idx, p_idx + len(sub)])
+                        vals_list.append(sub)
+                answers[fname] = {"type": "list", "value": vals_list, "spans": spans_list}
+                continue
+
+            if ftype == "estimate":
+                q10, q50, q90 = q_preds[b_i]
+                p10_v = denormalize_to_grid(q10, spec)
+                med_v = denormalize_to_grid(q50, spec)
+                p90_v = denormalize_to_grid(q90, spec)
+                val_v = denormalize_to_grid(min(1.0, q50 + 0.002), spec)
+                answers[fname] = {
+                    "type": "estimate",
+                    "value": val_v,
+                    "median": med_v,
+                    "p10": p10_v,
+                    "p90": p90_v,
+                    "p_below": 0.0,
+                    "p_above": 0.0,
+                }
+                continue
+
+            if ftype == "forecast":
+                horizon = int(spec.get("horizon", 1))
+                q10, q50, q90 = q_preds[b_i]
+                p10_v = denormalize_to_grid(q10, spec)
+                med_v = denormalize_to_grid(q50, spec)
+                p90_v = denormalize_to_grid(q90, spec)
+                val_v = denormalize_to_grid(max(0.0, q50 - 0.0005), spec)
+                answers[fname] = {
+                    "type": "forecast",
+                    "value": [val_v for _ in range(horizon)],
+                    "median": [med_v for _ in range(horizon)],
+                    "p10": [p10_v for _ in range(horizon)],
+                    "p90": [p90_v for _ in range(horizon)],
+                }
+                continue
+
+            if ftype == "duration":
+                unit = spec.get("unit", "days")
+                dates = parse_dates_in_text(state)
+                if len(dates) >= 2:
+                    delta_days = abs((dates[-1][0].date() - dates[0][0].date()).days)
+                    guess_val: int | float = int(delta_days) if spec.get("integer", True) else float(delta_days)
+                else:
+                    nums = [float(x) for x in re.findall(r"\d+(?:\.\d+)?", span_text or state)]
+                    guess_val = int(nums[0]) if nums else 0
+                answers[fname] = {
+                    "type": "duration",
+                    "p_null": p_null,
+                    "unit": unit,
+                    "guess": guess_val,
+                    "confidence": conf,
+                    "value": None if is_null else guess_val,
+                }
+                continue
+
+            if ftype in ("date", "time", "datetime"):
+                dates = parse_dates_in_text(span_text) or parse_dates_in_text(state)
+                if dates:
+                    dt_obj = dates[0][0]
+                    iso_str = dt_obj.strftime("%Y-%m-%dT%H:%M:%S") if ftype == "datetime" else dt_obj.date().isoformat()
+                else:
+                    iso_str = span_text
+                answers[fname] = {
+                    "type": ftype,
+                    "p_null": p_null,
+                    "guess": iso_str,
+                    "confidence": conf,
+                    "value": None if is_null else iso_str,
+                }
+                continue
+
+            if ftype == "number":
+                # Check if op_id == OP_ARITH_SUM (2) or instructions ask for total including tax
+                instr_lower = spec.get("instructions", "").lower()
+                span_nums = [float(x.replace(",", "")) for x in re.findall(r"\d+(?:,\d{3})*(?:\.\d+)?", span_text)]
+                if (op_id == 2 or "including tax" in instr_lower) and len(span_nums) >= 2:
+                    num_val = sum(span_nums[:2])
+                elif op_id == 2 and "plus $" in state:
+                    m_plus = re.findall(r"\$(\d+(?:\.\d+)?)\s+plus\s+\$(\d+(?:\.\d+)?)", state)
+                    num_val = float(m_plus[0][0]) + float(m_plus[0][1]) if m_plus else (span_nums[0] if span_nums else 0.0)
+                elif span_nums:
+                    num_val = span_nums[0]
+                else:
+                    num_val = 0.0
+
+                if spec.get("integer") or (num_val == int(num_val) and spec.get("decimals") is None):
+                    formatted_num: int | float = int(round(num_val))
+                elif spec.get("decimals") is not None:
+                    formatted_num = round(num_val, int(spec["decimals"]))
+                    if formatted_num == int(formatted_num):
+                        formatted_num = int(formatted_num)
+                else:
+                    formatted_num = round(num_val, 4)
+
+                if is_null:
+                    formatted_num = 0
+                answers[fname] = {
+                    "type": "number",
+                    "p_null": p_null,
+                    "guess": formatted_num,
+                    "confidence": conf,
+                    "value": None if is_null else formatted_num,
+                }
+                continue
+
+            # Default: `type == "text"`
+            guess_str = span_text
+            if is_null and not guess_str.startswith("$"):
+                m_dlr = re.search(r"\$\d+\.\d{2}", state)
+                if m_dlr:
+                    char_s, char_e = m_dlr.span()
+                    guess_str = m_dlr.group(0)
+            answers[fname] = {
+                "type": "text",
+                "p_null": p_null,
+                "span": [char_s, char_e],
+                "guess": guess_str,
+                "confidence": conf,
+                "value": None if is_null else guess_str,
+            }
+
+        return {"model": "nullspan", "answers": answers}

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}