Datasets:

Elliot-Data
/

wordart_cleaned

---
license: other
task_categories:
- visual-question-answering
language:
- en
---
# wordart_cleaned
The **wordart** family of the ElliotVL supervised-fine-tuning pool, **after VLM cleaning**.
| | |
|---|---|
| images | 4,765 |
| QA turns | 12,859 |
| answers rewritten by the cleaning pass | 41 |
| QA created by the cleaning pass (`new_qa`) | 12,738 (99.1%) |
| shards | 2 |

## 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.



## 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 — 3 turns on one image

![case1](samples/case1.png)

| # | question | answer |
|---|---|---|
| 1 | What is the shape of the blue emblem? | Oval |
| 2 | What is visible directly below the emblem? | A black car grille |
| 3 | What is the background color of the emblem? | Dark blue |


### Example 2 — 4 turns on one image

![case2](samples/case2.png)

| # | question | answer |
|---|---|---|
| 1 | What words can you see in these images in sequence, separated by a semicolon? | RACCOON; e |
| 2 | What animal graphic is partially visible above the word 'RACCOON'? | A raccoon's face, specifically showing the left eye and forehead area. |
| 3 | What visual effect is present on the white print? | The print has a holographic or iridescent sheen. |
| 4 | Describe the font style of the word 'RACCOON'. | It is a white, arched, serif font with a drop shadow or outline effect. |


### Example 3 — 3 turns on one image

![case3](samples/case3.png)

| # | question | answer |
|---|---|---|
| 1 | What is the color of the text? | Dark red |
| 2 | What is the background color? | Beige |
| 3 | What style of font is used for the word? | Cursive script |


### Example 4 — 3 turns on one image

![case4](samples/case4.png)

| # | question | answer |
|---|---|---|
| 1 | What color are the letters on the sign? | Pink with a gold outline |
| 2 | Describe the background surface behind the sign. | Reddish-brown horizontal siding |
| 3 | What is the only complete word visible in the image? | Red |


### Example 5 — 2 turns on one image

![case5](samples/case5.png)

| # | question | answer |
|---|---|---|
| 1 | What font style is used for the text in the image? | Serif |
| 2 | What is the color of the text? | Dark brown |


### Example 6 — 3 turns on one image

![case6](samples/case6.png)

| # | question | answer |
|---|---|---|
| 1 | What is the main text written in the large, silver font? | Day! |
| 2 | What is the texture and color of the lettering? | Silver and glittery |
| 3 | What animal feature is partially visible at the bottom center of the image? | Reindeer antlers |


### Example 7 — 4 turns on one image

![case7](samples/case7.png)

| # | question | answer |
|---|---|---|
| 1 | What color are the letters in the image? | Maroon |
| 2 | What style of font is used for the word 'you'? | Cursive script |
| 3 | What is the background color and texture? | Brown textured surface |
| 4 | What visual effect gives the text a 3D appearance? | Drop shadow |


### Example 8 — 3 turns on one image

![case8](samples/case8.png)

| # | question | answer |
|---|---|---|
| 1 | What is the single word written in the image? | Boldly |
| 2 | What is the background color of the image? | Light blue |
| 3 | Describe the font style used for the text. | Black brush script |


### Example 9 — 2 turns on one image

![case9](samples/case9.png)

| # | question | answer |
|---|---|---|
| 1 | What is the background color of the image? | Salmon pink |
| 2 | What style of font is used for the text? | Handwritten cursive script |


### Example 10 — 3 turns on one image

![case10](samples/case10.png)

| # | question | answer |
|---|---|---|
| 1 | What sequence of uppercase letters is visible in the image? | ABCDEFGHIJKLMN |
| 2 | What are the colors of the text and the background? | The text is red or orange-red, and the background is a light beige or cream color. |
| 3 | Describe the style of the font used for the letters. | The letters are tall, condensed, bold, and sans-serif. |

	---
	license: other
	task_categories:
	- visual-question-answering
	language:
	- en
	---

	# wordart_cleaned

	The wordart family of the ElliotVL supervised-fine-tuning pool, after VLM cleaning.

	\| \| \|
	\|---\|---\|
	\| images \| 4,765 \|
	\| QA turns \| 12,859 \|
	\| answers rewritten by the cleaning pass \| 41 \|
	\| QA created by the cleaning pass (`new_qa`) \| 12,738 (99.1%) \|
	\| shards \| 2 \|

	## 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.



	## 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 — 3 turns on one image

	![case1](samples/case1.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What is the shape of the blue emblem? \| Oval \|
	\| 2 \| What is visible directly below the emblem? \| A black car grille \|
	\| 3 \| What is the background color of the emblem? \| Dark blue \|


	### Example 2 — 4 turns on one image

	![case2](samples/case2.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What words can you see in these images in sequence, separated by a semicolon? \| RACCOON; e \|
	\| 2 \| What animal graphic is partially visible above the word 'RACCOON'? \| A raccoon's face, specifically showing the left eye and forehead area. \|
	\| 3 \| What visual effect is present on the white print? \| The print has a holographic or iridescent sheen. \|
	\| 4 \| Describe the font style of the word 'RACCOON'. \| It is a white, arched, serif font with a drop shadow or outline effect. \|


	### Example 3 — 3 turns on one image

	![case3](samples/case3.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What is the color of the text? \| Dark red \|
	\| 2 \| What is the background color? \| Beige \|
	\| 3 \| What style of font is used for the word? \| Cursive script \|


	### Example 4 — 3 turns on one image

	![case4](samples/case4.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What color are the letters on the sign? \| Pink with a gold outline \|
	\| 2 \| Describe the background surface behind the sign. \| Reddish-brown horizontal siding \|
	\| 3 \| What is the only complete word visible in the image? \| Red \|


	### Example 5 — 2 turns on one image

	![case5](samples/case5.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What font style is used for the text in the image? \| Serif \|
	\| 2 \| What is the color of the text? \| Dark brown \|


	### Example 6 — 3 turns on one image

	![case6](samples/case6.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What is the main text written in the large, silver font? \| Day! \|
	\| 2 \| What is the texture and color of the lettering? \| Silver and glittery \|
	\| 3 \| What animal feature is partially visible at the bottom center of the image? \| Reindeer antlers \|


	### Example 7 — 4 turns on one image

	![case7](samples/case7.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What color are the letters in the image? \| Maroon \|
	\| 2 \| What style of font is used for the word 'you'? \| Cursive script \|
	\| 3 \| What is the background color and texture? \| Brown textured surface \|
	\| 4 \| What visual effect gives the text a 3D appearance? \| Drop shadow \|


	### Example 8 — 3 turns on one image

	![case8](samples/case8.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What is the single word written in the image? \| Boldly \|
	\| 2 \| What is the background color of the image? \| Light blue \|
	\| 3 \| Describe the font style used for the text. \| Black brush script \|


	### Example 9 — 2 turns on one image

	![case9](samples/case9.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What is the background color of the image? \| Salmon pink \|
	\| 2 \| What style of font is used for the text? \| Handwritten cursive script \|


	### Example 10 — 3 turns on one image

	![case10](samples/case10.png)

	\| # \| question \| answer \|
	\|---\|---\|---\|
	\| 1 \| What sequence of uppercase letters is visible in the image? \| ABCDEFGHIJKLMN \|
	\| 2 \| What are the colors of the text and the background? \| The text is red or orange-red, and the background is a light beige or cream color. \|
	\| 3 \| Describe the style of the font used for the letters. \| The letters are tall, condensed, bold, and sans-serif. \|