README.md

---
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<n<1M
---

# Sumtablets-Cuneiform-Full-Fable5-Remaster — Cuneiform Vision-Language Training Dataset

A rebuilt, leakage-proof, multi-task training dataset for teaching
vision-language models (target: **Qwen3-VL-8B-Instruct** LoRA) to **visually
read, transliterate, and translate Sumerian cuneiform tablets** from
photographs. The mission: produce useful first-pass readings for the ~90% of
excavated tablets that have never been published or translated.

Current release: **v1.0.0** — 455,506 records (402,004 train / 24,507
validation / 24,446 test / 4,549 external grounding), built on **52,602
tablets with at least one tier-A/B image** and 87,764 aligned surface-text
pairs. Everything below is reproducible from the public pipeline in this
repository.

> **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 `<SURFACE>` 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 `<SURFACE>` / `<COLUMN>` /
`<RULING>` / `<BLANK_SPACE>`, subscript numerals, damage tokens (`<unk>`,
`...`); refusals are exactly `<ILLEGIBLE_IMAGE> …`; 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)