Datasets:
license: apache-2.0
task_categories:
- image-text-to-text
language:
- en
pretty_name: Suture
tags:
- insurance
- document-qc
- policy-issuance
- synthetic
- verifier-as-oracle
configs:
- config_name: benchmark
data_files: data/benchmark.jsonl
- config_name: validation
data_files: data/validation.jsonl
- config_name: train
data_files: data/train.jsonl
- config_name: train_seed7
data_files: data/train_seed7.jsonl
Suture
Gold tasks for policy-issuance QC: a structured underwriting binder, a structured issued policy, and the exact discrepancy set. Images are not stored. Re-render with suture_forge.generate.render_doc from the GitHub repo.
Paper and adapter: caiotheodoro/suture · caiotheodoro/suture-8b. Predictions on this gold: caiotheodoro/suture-evals.
Splits
| Config | Seed | n | What |
|---|---|---|---|
benchmark |
777 | 1000 | Contracted held-out set. Never in train. |
validation |
7 holdout | 80 | Val. Optimistic vs 777; DEDUCTIBLE_HIGHER gold n=0. |
train |
7+11+13+17+19+23 | 5120 | Published SFT mix (train_dedhi). |
train_seed7 |
7 | 320 | Official stratified split, no class boost. |
Leakprobe vs val and vs benchmark is 0. Signature = SHA-256 over sorted ground-truth fields. Build refuses overlap > 0.
Row schema
Each line is a Task: task_id, seed, binder, policy, image_*_sha256, expected (PASS/FLAG + discrepancies), signature, difficulty. Taxonomy and weights: repo CONTRACTS.md (13 classes).
Render
from datasets import load_dataset
from suture_forge.schema import Binder, Policy
from suture_forge.generate import render_doc
row = load_dataset("caiotheodoro/suture", "benchmark", split="train")[0]
png = render_doc(Binder.model_validate(row["binder"]), "BINDER",
row["difficulty"], row["task_id"])
Linux vs macOS raster can differ (SHA mismatch). Labels do not. Eval in the project re-renders on the GPU host.
License / use
Apache-2.0. Synthetic ACORD-style pages, not real submissions. Form numbers are public ISO ids. Do not treat this as a carrier document corpus.