--- pretty_name: "Sumtablets-Cuneiform-Full-Fable5-Remaster (Vision-Language Training Dataset)" license: other license_name: mixed-see-licensing-section language: - sux - akk - en task_categories: - image-to-text - translation - visual-question-answering tags: - cuneiform - sumerian - assyriology - ocr - sign-detection - qwen3-vl - lora size_categories: - 100K **AI disclosure:** this dataset restructuring was designed, implemented, > and executed by **Claude Fable 5** (Anthropic) operating the > `Sumtablets_v2` pipeline under human direction, with human review at the > quality gates (segmentation review, pollution labeling, Gold-set approval). > Every automated decision is recorded in per-phase manifests and reports so > third parties can validate or contest it. --- ## 1. What this is (and what it replaces) The starting point was [`TRACCERR/Sumtablets_Merged`](https://huggingface.co/datasets/TRACCERR/Sumtablets_Merged) (~200k rows, 53.7 GB, rev `67e3d17`), itself a merge of [`colesimmons/SumTablets`](https://huggingface.co/datasets/colesimmons/SumTablets) (glyph–transliteration pairs; [paper](https://arxiv.org/abs/2602.22200)) and `colesimmons/SumTablets_Photos`. The original improvement plan was a single re-split tool to fix suspected train/test tablet leakage. That plan was reviewed and rebuilt into a 7-phase pipeline (`Sumtables-Cuneiform-Full-Fable5-Remaster_Dataset_Plan.md`) because split hygiene alone left the dataset's real limitations untouched: | Planned originally | Found during rebuild | What shipped instead | |---|---|---| | Fix same-tablet leakage across splits | **Upstream had zero cross-split tablets** — the feared leak didn't exist | Split rebuilt anyway with stronger guarantees: perceptual-duplicate co-assignment, formula-leakage measurement, minimum-representation stratification | | Keep "English translations" grouped | **The dataset contains no translations at all** (and its Unicode glyphs are dictionary-derived, not observed) | A translation layer was built from external sources (Phase 3) | | Exact-hash image dedup | Exact hashing misses recompressed/cropped duplicates; naive pHash over-merges | pHash + dHash confirmation; oversized similarity components forced to train | | Use images as-is | Mean embedded photo is ~388×731 px; many CDLI images are multi-view composites misaligned with full-tablet text | Full-res re-fetch, composite segmentation, per-surface text alignment, readability tiers | | — | ~1.8% of "lineart" images are **scanned publication text pages** (Latin print), found by human review | Calibrated document-scan detector; flagged images excluded from vision tasks | ## 2. Pipeline phases — planned vs. delivered ### Phase 0 — Audit & leakage-proof re-split (`clean_sumtablets.py`) ✅ Deterministic tablet-grouped 90/5/5 re-split (seed 42) of all 199,964 rows / 89,485 canonical `P######` tablets, stratified by period × genre × modality with minimum eval representation per stratum. **Results (full corpus, all hard assertions PASS):** - train 178,116 / validation 9,864 / test 9,865 / quarantine 2,119 rows; tablets 80,567 / 4,459 / 4,459 = 90.03 / 4.98 / 4.98%. - 53,926 exact-duplicate image groups (4 spanning different tablet IDs — flagged for review); 922,737 pHash near-duplicate pairs → 12,481 tablets merged into shared "leak groups" so perceptual twins can never straddle splits. dHash confirmation rejects spurious pHash links (hand-drawn lineart otherwise over-merges). - Two giant Ur III similarity components (8,085 and 4,037 tablets) exist; the allocator forces any group larger than the eval quota into train (regression-tested) — largest group in val/test is 2 tablets. - **Formula leakage measured, not hidden:** 156 near-verbatim text groups span tablets; 12 validation and 25 test tablets have a train twin. Tagged in `formula_leakage.csv`; the eval harness reports metrics with and without them. - Quarantine = 2,081 ORACC `Q######` composite-text IDs + 38 `X######` — real identifiers of composite editions, not physical tablets; excluded from splits by design (re-admission with leak screening is a documented future option). - Audit columns on every row: `canonical_tablet_id`, `original_tablet_id`, `source_split`, `assigned_split`, `modality`, `duplicate_group`, `formula_dup_group`, `validation_flags`, `provenance`. ### Phase 1 — Image elevation (`phase1_images.py`) ✅ complete - **CDLI full-resolution re-fetch, complete census**: all 178,970 tablet×kind URL pairs resolved — **71,852 images downloaded (37,810 photos + 34,042 lineart, 116 GB)**, 106,999 definitive "no image hosted" 404s recorded per tablet, 119 corrupt-at-source files logged. Resumable manifest survived a disk-full crash and an external process kill with zero loss (supervised auto-restart loop; manifest surgery for the ~380 transient errors — note `--retry-errors` re-attempts 404s too, so targeted retries edit the manifest instead). *Finding:* re-fetching does **not** upscale existing images (where CDLI hosts a file the dataset already embedded it at identical resolution); its value is **coverage** — thousands of images for tablets that had none of that kind, converting text-only tablets into vision samples. - **Composite segmentation** by projection profiling → per-surface panel bounding boxes (manifests only; crops materialize at packaging). *Adjustment after human review (199/200 correct, gate G1 passed):* stacked CDLI layouts needed a relaxed-gap re-split pass, and the dominant panel may never be labeled an "edge" (regression test `P201395`). - **Surface–text alignment**: transliterations split at `` markers and paired with panels when counts match → **87,764 aligned surface pairs** full-corpus (37,410 count-matched + 6,631 trivial images; 34.9% of the 126,282 analyzed images fully align, the rest keep whole-image supervision). - **Readability tiers** A/B/C from blur, contrast, and estimated sign height; tier C never becomes an OCR target. Full-corpus result: **52,602 tablets hold at least one tier-A/B image** (47,427 train / 2,598 val / 2,577 test); tier A dominates the fetched images (45,286 of 64,157 train fetches). - **Document-scan detector** — *entirely human-review-driven addition*: users of the G1 review page found publication text pages mislabeled as lineart. Detector calibrated on 13 human-labeled images (4 pages / 9 genuine hand copies); naive heuristics false-positived at 25–45% (curved hand-drawn outlines defeat vertical-run tests; stacked views shrink per-view runs). Final rule (band geometry + ink density + dilated vertical-run) flags **1,164 images full-corpus (~2.1% of lineart)**, catching all human-found pages, clearing all 9 labeled copies; spot-check precision 3/4 (over-flagging is accepted: a flag only removes an image from vision training, never deletes data). ![Sample-1](https://cdn-uploads.huggingface.co/production/uploads/67ef1e6e48c8e7f1061c6c95/zkpU3W6QkdoUTGOxdr4iB.jpeg) ### Phase 2 — Task architecture (`phase2_tasks.py` + `mixer_config.yaml`) ✅ Task-tagged records with 25 deterministic prompt paraphrases per task, canonical output formats validated on every record, per-tablet caps, and a train-only mixture spec. *Adjustment (bug found in v1 mixer):* scarce tasks throttled the whole mixture (T8's 5.5k records nearly cut train from ~105k to 12.7k). Fixed semantics: the most-available task anchors totals, scarce tasks underfill with reported deviations, and **exact mixture shares are enforced at training time via sampling weights** using the shipped `mixer_config.yaml`. v1.0.0 totals: 402,004 train records (259,457 vision / 142,547 text) plus 4,549 external T5 grounding records; 24,507 validation and 24,446 test records (~15.4k vision each). Per-task counts live in the release's `build_report.json` and `MANIFEST.parquet`. A curated 4,850-record **starter pack** (`starter_train.parquet`, vision-first mixture, tier-A/B only for OCR, refusal-capped abstention) ships for quick fine-tune runs. ### Phase 3 — Translation layer (`phase3_translations.py`) ✅ - **CDLI ATF harvest**: 5,357 tablets with line-aligned `#tr.en` translations (2,094 train / 124 val / 121 test in-corpus; 3,018 external tablets admitted to train only after screening their text against every eval tablet's normalized transliteration). - **Templated Ur III renderer**: precision-first CFG rules (only tablets with ≥80% of lines fully parsed) → 409 tablets, `source=templated`, train-only, 7% of T8 (cap: 40%). *Adjustment:* rules had to be rewritten in the corpus's Unicode orthography (`š`, subscript numerals) — the CDLI ASCII convention (`sz`, plain digits) matches zero corpus lines. - **Gold-997**: stratified test-pool benchmark selection (856 with images, 43 with harvested translations), human-approved selection, per-item `verification_status` tracking (verification pending). - Dead end documented: ORACC `etcsri` JSON exposes word glosses only — sentence translations are HTML-only (future scrape). ### Phase 4 — External sign grounding (`phase4_grounding.py`) ✅ eBL cuneiform-OCR `coco-recognition` set ([Zenodo 10693601](https://zenodo.org/records/10693601)): 654 tablet photographs, 46k+ sign boxes, 120 sign classes → **4,549 T5 records** (dense detection, locate-sign, read-region; 0–1000 normalized boxes; valid-JSON targets). This file is 100% vision by construction — grounding has no text-only variant — and stays a separate parquet so the unlicensed images can be excluded from redistribution by omitting one file. Leak-guarded against eval P-numbers (0 overlaps found). Largely Akkadian — intended cross-script sign-shape transfer, tagged `origin=ebl`. **License not stated upstream → `train_local_only`: these images must not be redistributed with the dataset.** ### Phase 5 — Packaging (`phase5_package.py`) ✅ `releases/Sumtables-Cuneiform-Full-Fable5-Remaster-v1.0.0`: **455,506 records** across `train.parquet` (402,004; 259,457 vision, images at a 2048px training budget — full-resolution originals preserved in `Phase1/fetched`), `validation.parquet` (24,507), `test.parquet` (24,446), and the train-local `t5_train.parquet` (4,549). Ships `MANIFEST.parquet` (per-sample provenance for re-weighting without touching image bytes), phase reports, mixer config, dataset card — with **every conversation validated against the actual Qwen3-VL chat template** (zero flags) and eval↔train tablet disjointness re-verified from the written files at package time. The earlier v0.9.0 pre-release (149,489 records, text-only train) is superseded but retained for provenance. ### Phase 6 — Evaluation harness (`phase6_eval.py`) ✅ Resumable predictions (mock + any OpenAI-compatible server), CER, sign-level F1, chrF, metadata accuracy, abstention precision/recall, insertion-rate honesty proxy; strata by task × period × genre × tier; **dual reporting with and without formula-leaked tablets**. Validated end-to-end with a floor baseline (all metrics at expected floor over 16,463 real test items). ## 3. Measured baseline (zero-shot Qwen3-VL-8B-Instruct) 2,000-item random sample, locally served model, pre-v1.0 test parquet (definitive run re-executes on v1.0): | Task | Zero-shot result | |---|---| | T1/T2/T3 image reading | **Refuses 93–99% of images**; attempted readings ≈ 0 sign-F1 | | T10 abstention | recall 0.97 / precision **0.44** — the base model *over*-refuses | | T11 metadata | genre 94.6% (majority-class artifact); **period 0.9%** (never predicts Ur III) | | T6 signs→translit | CER 0.83, sign-F1 0.042 | | T7 translit→signs | CER 0.86, sign-F1 0.004 | | T8 translation | chrF 0.149 | Interpretation: the pretrained model can neither read cuneiform images nor map signs to readings — and it already knows to refuse. Post-training gains on sign-F1/CER are therefore attributable to this dataset, and the training risk to manage is *residual over-refusal*, not hallucination alone. ## 4. Training path (first fine-tune: Qwen3-VL-4B) Two equivalent routes onto the same Unsloth engine — both consume the curated **starter pack** (4,850 records: 1,500 surface OCR / 1,000 lineart / 500 full-tablet / 400 sign grounding / 250 photo-lineart pairs / 250 metadata / 100 refusal-capped abstention / 850 text tasks; tier-A/B only for OCR; one record per tablet per task): - **Unsloth Studio (GUI):** load the exported dataset directory `unsloth_starter/` (`export_unsloth.py` converts our parquet into the documented Studio format — a `messages` column with typed content parts and an `images` column; 4,850 samples, 4,000 with images). Studio's dataset preview should auto-map both columns. - **Script (`train_lora.py`):** the same Unsloth `FastVisionModel` workflow headless, with the capability guardrails executed automatically — vision-native loader asserted at load, adapter-only artifact, stray-config removal, and `verify_model_capabilities.py` run on the output (non-zero exit on any regression). Either way, **acceptance is gated** (see `TRAINING_GUARDRAILS.md`, written after a prior non-pipeline fine-tune silently lost vision and clamped context 256k→64k): the served result must pass the live vision probe and the ~70k-token needle probe, then `phase6_eval.py` on validation against the zero-shot reference (sign-F1 ≈ 0, 93–99% refusal). The 4B run validates the data recipe cheaply; the identical recipe scales to Qwen3-VL-8B-Instruct (`--model Qwen/Qwen3-VL-8B-Instruct`). ## 5. Engineering log (what broke and what it taught) | Incident / discovery | Consequence baked into the pipeline | |---|---| | Upstream had zero cross-split tablets — but 922,737 pHash near-dup pairs and 156 cross-tablet formula groups | Leak groups + formula tagging replaced the original "fix the split" premise | | Naive pHash merging false-positived 25–45% on hand-drawn lineart | dHash confirmation; oversized similarity groups forced to train | | Human review found publication text pages inside "lineart" (~2.1%) | Calibrated document-scan detector; review page now shows flag status | | CDLI re-fetch upscaled nothing (uplift = 1.0) | Reframed as coverage acquisition; 404s recorded as a permanent census | | Scarce T8 throttled the mixer to 12% of available data | Backbone-anchored mixer; exact shares enforced at training time via sampling weights | | Template renderer matched zero lines | Corpus uses Unicode orthography (š, subscripts), not CDLI ASCII — rules rewritten, 409 tablets rendered at ≥80% line coverage | | Disk-full crash + external process kill mid-fetch | Supervised auto-restart, append-safe manifest with surgery tooling, everything resumable | | Zero-shot model *over*-refuses (precision 0.44) | Starter pack caps refusal examples at 40% of the abstention slice | | Prior fine-tune lost vision & context silently | `verify_model_capabilities.py` + guardrails doc gate every artifact | ## 6. Dataset structure Each record: `task`, `tablet` (canonical P-number or `ebl:` namespace), `split`, `tier`, `conversations` (system/user/assistant JSON, image flag on the user turn, canonical output format per task), `image` (struct{bytes,path} or null), `provenance` (JSON: image key, bbox, prompt id, origin, license posture). Output conventions: transliterations preserve `` / `` / `` / ``, subscript numerals, damage tokens (``, `...`); refusals are exactly ` …`; T11 targets are `{"period": ..., "genre": ...}`; T5 targets are JSON with 0–1000 boxes. ## 7. Validate it yourself ```powershell pip install -r requirements.txt python -m pytest tests -q # 65 tests: split integrity, segmentation, # grading, alignment, mixer, metrics, leak guards ``` - **Split integrity**: re-run `clean_sumtablets.py --dry-run` and diff `split_manifest.csv`; every hard assertion is recomputed from the written parquet, not trusted from memory. - **Segmentation quality**: open `Phase1/review_sample.html` — 200 seeded images with detected boxes drawn; document-flagged cards are marked. - **Mixture & provenance**: `MANIFEST.parquet` + `build_report.json` expose every record's task, source, prompt id, and the mixer's target-vs-achieved shares including underfills. - **Metrics**: `phase6_eval.py` is deterministic given a predictions file; floor and oracle mock modes bound every metric. - **Capability preservation** (lessons learned from a prior training attempt that silently lost vision and clamped context 256k→64k): every training artifact must pass `verify_model_capabilities.py` — static checks on architecture, `max_position_embeddings=262144`, mRoPE, vision tower tensors, processor files, GGUF mmproj, plus live vision and ~70k-token needle probes. Rules and acceptance checklist: `TRAINING_GUARDRAILS.md`. ## 8. Status & roadmap - ✅ **v1.0.0 released**: CDLI fetch complete (all 178,970 pairs resolved; 71,852 full-res images), full-corpus Phase 1 manifests (126,282 images, 87,764 surface pairs), train vision tasks materialized at a 2048px training budget, floor baseline recorded on the v1.0 test split, tablet disjointness re-verified across all release files. - ▶ Next: first fine-tune (Qwen3-VL-4B on the starter pack via `train_lora.py`), gated by `verify_model_capabilities.py`. - ⏳ Gold-997 per-item expert verification. - Future: ORACC etcsri HTML translation scrape; ETCSL; Q-composite re-admission with leak screening; DeepScribe/MaiCuBeDa ingestion; synthetic font renders (T12). ## 9. Licensing | Component | License | |---|---| | SumTablets text (upstream) | CC BY 4.0 | | SumTablets photos (upstream) | Apache 2.0 | | CDLI ATF transliterations/translations | CDLI terms — attribution (cdli.earth) | | ORACC etcsri (consulted) | CC0 | | eBL sign-grounding images (T5) | **Unstated — train-local only, excluded from redistribution** | | Pipeline code (`*.py`, this repo) | MIT | ## 10. Acknowledgements Built on the work of the CDLI, ORACC/ePSD2, ETCSL, and eBL projects and the SumTablets authors ([Simmons et al. 2024](https://aclanthology.org/2024.ml4al-1.20/)), representing decades of Assyriological digitization. Dataset restructuring executed with **Claude Fable 5** (Anthropic); human direction, review labels, and gate approvals by the project owner. ## 11. Why Qwen3-VL? Because the model family is specifically positioned around stronger OCR, rare/ancient character handling, blur/tilt robustness, and long-document structure parsing. That matters for cuneiform because the problem is not only “read image text”; it is damaged visual signs, surface structure, line order, uncertain readings, and translation context. ![Sample-2](https://cdn-uploads.huggingface.co/production/uploads/67ef1e6e48c8e7f1061c6c95/bmWKhvriK1sYcFzkLes-y.jpeg)