| --- |
| license: other |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| --- |
| |
| # sroie_data_cleaned |
|
|
| The **sroie_data__x** family of the ElliotVL supervised-fine-tuning pool, **after VLM cleaning**. |
| |
| | | | |
| |---|---| |
| | images | 31,823 | |
| | QA turns | 85,935 | |
| | answers rewritten by the cleaning pass | 10,904 | |
| | QA created by the cleaning pass (`new_qa`) | 54,997 (64.0%) | |
| | shards | 1 | |
| |
| ## How this was cleaned |
| |
| A vision-language model read each image together with its QA and judged the item. The pass is |
| **not a filter that only removes rows** — it rewrites answers it finds wrong but salvageable, |
| drops what it cannot salvage, and adds QA where the image supports more than the source provided. |
| Each row carries the judge's own record in `clean_meta`, including the cleaner identity, the |
| policy it applied, and its per-item scores for legibility, richness and coverage. |
|
|
| **A large share of the QA here was written by the cleaning pass, not by the original dataset.** |
| Across the pool that share runs from roughly half to over 80% of a family's turns, and it is |
| reported in the table above. Those rows have an empty `org_answer` because no pre-clean original |
| exists. Treat them as model-generated supervision: they were judged against the image, but they |
| are not human annotation, and model-written QA is where formatting defects are most likely. |
|
|
| The effect on the answers that *were* carried over is substantive rather than cosmetic. In this pool the pass corrects values, not just |
| wording — a curve's critical point restated from `4.00` to `2.00`, a computed ratio from `1` to |
| `Approximately 1.33` — and for some families it removed the content entirely, which is why those |
| families are absent here rather than published empty. |
|
|
| ## `answer` vs `org_answer` |
| |
| - `answer` — the cleaned answer. **Train on this.** |
| - `org_answer` — the pre-cleaning answer from the same `(image, question)` in the uncleaned pool. |
| It is empty for QA the cleaning pass added, which have no original. |
|
|
| Where the two differ, the difference is the correction. Keeping both makes every edit auditable |
| instead of asking you to take the cleaning on trust. |
|
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|
|
| ## Schema |
|
|
| - `image` — HF `Image()`, renders directly in the dataset viewer |
| - `image_sha256` — content hash of the image |
| - `n_qa` — number of turns attached to this image |
| - `qa` — list of {`question`, `answer`, `org_answer`}, **in source order** |
| - `family` / `clean_meta` — provenance and the judge's per-row record |
|
|
| A row with more than one entry in `qa` is a **conversation over a single image**, not a set of |
| independent questions — the turns depend on each other and the image belongs to all of them. |
| Keep them together and in order when training. |
|
|
| Parquet row groups are written at ~32 MB so the dataset viewer pages without stalling. |
|
|
| ## What is NOT claimed |
|
|
| No rejection sampling was run against this family: there is no accept/reject verdict per row, and |
| `answer` is the cleaned reference rather than a model generation that a string verifier confirmed. |
| For that, use the `*_RS_think` / `*_rejected` datasets in [elliot-mllm](https://huggingface.co/elliot-mllm). |
|
|
|
|
| ## Examples |
| 10 rows taken straight from this dataset. Images are the **original files** as stored here - no downscaling, no recompression - and every turn is shown in full. |
|
|
| ### Example 1 — 4 turns on one image |
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|  |
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| | # | question | answer | |
| |---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | RM 29.70 | |
| | 2 | What color is the text in the image? | Black | |
| | 3 | What is the background color of the image? | White | |
| | 4 | Is the text in the image bold? | Yes | |
|
|
|
|
| ### Example 2 — 2 turns on one image |
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|  |
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| | # | question | answer (cleaned) | org_answer (pre-judge) | |
| |---|---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | Thank You and Please Come Again. | THANK YOU AND PLEASE COME AGAIN. | |
| | 2 | What are the visual characteristics of the text and background in the image? | The text is black and written in a sans-serif font that appears slightly pixelated, set against a plain white background. | _(unchanged)_ | |
|
|
|
|
| ### Example 3 — 3 turns on one image |
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|  |
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| | # | question | answer | |
| |---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | 560272 | |
| | 2 | What is the visual style and resolution of the text in the image? | The text is rendered in a low-resolution, pixelated font that resembles a dot-matrix print. | |
| | 3 | What are the colors of the text and the background? | The text is black (or dark gray) and the background is white. | |
|
|
|
|
| ### Example 4 — 3 turns on one image |
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|  |
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| | # | question | answer (cleaned) | org_answer (pre-judge) | |
| |---|---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | Invoice No.: | INVOICE NO.: | |
| | 2 | What is the visual quality or style of the text in the image? | The text is highly pixelated and appears to be low-resolution, resembling a digital display. | _(unchanged)_ | |
| | 3 | What are the colors of the text and the background? | The text is black and the background is white. | _(unchanged)_ | |
|
|
|
|
| ### Example 5 — 2 turns on one image |
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|  |
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| | # | question | answer | |
| |---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | S/O NO | |
| | 2 | What color is the text in the image? | Black | |
|
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|
|
| ### Example 6 — 3 turns on one image |
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|  |
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| | # | question | answer | |
| |---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | RM10.14 | |
| | 2 | What is the visual style of the font used in the image? | The font is pixelated and blocky, resembling a low-resolution digital display or dot-matrix print. | |
| | 3 | What are the colors of the text and the background? | The text is black and the background is white. | |
|
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|
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| ### Example 7 — 2 turns on one image |
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|  |
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| | # | question | answer | |
| |---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | OCEAN LC PACKAGING ENTERPRISE | |
| | 2 | What are the visual characteristics of the text in the image? | The text is black and written in a stencil-style font on a white background. | |
|
|
|
|
| ### Example 8 — 3 turns on one image |
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|  |
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| | # | question | answer (cleaned) | org_answer (pre-judge) | |
| |---|---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | Desc | DESC | |
| | 2 | What is the visual style of the font used in the image? | Pixelated or stencil-like font. | _(unchanged)_ | |
| | 3 | What are the colors of the text and the background? | Black text on a white background. | _(unchanged)_ | |
|
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|
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| ### Example 9 — 4 turns on one image |
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|  |
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| | # | question | answer | |
| |---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | TEO HENG STATIONERY & BOOKS | |
| | 2 | What is the color of the text? | Black | |
| | 3 | What style of font is used for the text? | Stencil | |
| | 4 | Is the text written in uppercase or lowercase? | Uppercase | |
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|
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| ### Example 10 — 3 turns on one image |
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|  |
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| | # | question | answer | |
| |---|---|---| |
| | 1 | What is written in the image? Answer this question using the text in the image directly. | GREEN BEAN | |
| | 2 | What is the visual style of the font used in the image? | The text is in a pixelated, blocky font that resembles dot-matrix printing. | |
| | 3 | What is the color of the text? | Black. | |
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