unimer_train_cleaned

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

images	542,449
QA turns	1,070,876
answers rewritten by the cleaning pass	138,530
QA created by the cleaning pass (new_qa)	528,261 (49.3%)
shards	3

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.

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 — 2 turns on one image

#	question	answer
1	Recognize the mathematical expression in the image and output LaTeX.	n \to \infty
2	What are the visual characteristics of the text in the image?	The text is written in a black serif font on a white background.

Example 2

#	question	answer
1	Recognize the mathematical expression in the image and output LaTeX.	\begin{array} { r l } { \langle u | u | ^ { 2 } , b _ { j , 1 } \rangle } & { = \sum _ { k , l , m } \frac { A _ { k } A _ { l } A _ { m } } { L _ { k } L _ { l } L _ { m } } \left\langle e ^ { i \Gamma _ { k } + i \Gamma _ { l } - i \Gamma _ { m } } e ^ { - \frac { | y _ { k } | ^ { 2 } + | y _ { l } | ^ { 2 } + | y _ { m } | ^ { 2 } } { 2 } } , e ^ { i \Gamma _ { j } } e ^ { - \frac { 1 } { 2 } | y _ { j } | ^ { 2 } } \right\rangle } \end{array}

Example 3

#	question	answer
1	Recognize the mathematical expression in the image and output LaTeX.	\begin{array} { r l r } { V ( x , t ) } & { = } & { \frac { 1 } { | \sqrt { 2 \lambda } \alpha _ { 0 } | ^ { 2 q } } \sum _ { m = 0 } ^ { q } ( - 1 ) ^ { m + q } \frac { ( 2 ^ { m } q ! ) ^ { 2 } } { ( 2 m ) ! ( q - m ) ! } \lambda ^ { q - m } x ^ { 2 m } } \ & { } & { - \frac { ( - i ) ^ { q } } { | \sqrt { 2 \lambda } \alpha _ { 0 } | ^ { 2 q } } \Big [ \Big ( e ^ { - i \tau } \alpha _ { 0 } \sqrt { 2 \lambda } \Big ) ^ { q } + \Big ( e ^ { i \tau } \alpha _ { 0 } ^ { * } \sqrt { 2 \lambda } \Big ) ^ { q } \Big ] } \ & { } & { \times \Big ( [ 1 + ( - 1 ) ^ { q } ] \frac { \Gamma ( 1 + q ) \Gamma ( \frac { 1 } { 2 } ) } { \Gamma ( \frac { 1 } { 2 } + \frac { q } { 2 } ) } \frac { ( - 1 ) ^ { \frac { q } { 2 } } } { 2 } + i [ 1 - ( - 1 ) ^ { q } ] \frac { \Gamma ( q + 1 ) \Gamma ( \frac { 3 } { 2 } ) } { \Gamma ( \frac { q } { 2 } + 1 ) } ( - 1 ) ^ { \frac { q - 1 } { 2 } } \Big ) x ^ { q } } \ & { } & { + 1 . } \end{array}

Example 4

#	question	answer
1	Recognize the mathematical expression in the image and output LaTeX.	S = \textsf { S t a b s } ( \Pi ) = { P _ { i } }

Example 5

#	question	answer
1	Recognize the mathematical expression in the image and output LaTeX.	\langle u _ { 0 } , u _ { 1 } , u _ { 3 } , u _ { 0 } \rangle

Example 6 — 3 turns on one image

#	question	answer
1	Recognize the mathematical expression in the image and output LaTeX.	\omega = 2 , 5
2	What is the name of the Greek letter on the left side of the equation?	omega
3	What character is used as the decimal separator in the number?	comma

Example 7 — 2 turns on one image

#	question	answer
1	Recognize the mathematical expression in the image and output LaTeX.	I _ { 1 }
2	What are the visual characteristics of the text in the image?	The text is black on a white background, and the capital letter 'I' is italicized.

Example 8 — 3 turns on one image

#	question	answer (cleaned)	org_answer (pre-judge)
1	Recognize the mathematical expression in the image and output LaTeX.	8.38 \times 10^5	8 . 3 8 \times 1 0 ^ { 5 }
2	What is the font style of the numbers in the image?	Serif	(unchanged)
3	What are the colors of the text and the background?	Black text on a white background	(unchanged)

Example 9 — 2 turns on one image

#	question	answer (cleaned)	org_answer (pre-judge)
1	Recognize the mathematical expression in the image and output LaTeX.	\int_{0}^{1} dx , x , f(X) = \frac{1}{2} \int_{0}^{1} dx , f(X).	\int _ { 0 } ^ { 1 } d x , x , f ( X ) = \frac { 1 } { 2 } \int _ { 0 } ^ { 1 } d x , f ( X ) .
2	What are the colors of the text and the background in the image?	The text is black and the background is white.	(unchanged)

Example 10

#	question	answer (cleaned)	org_answer (pre-judge)
1	Recognize the mathematical expression in the image and output LaTeX.	L = L_0 + L_I = \left{ \frac{1}{2} (\partial \Phi)^2 - \frac{1}{2} m^2 \Phi^2 \right} + \left{ - \frac{\lambda}{4!} \Phi^4 + L_{\text{ct}} \right} ,	{ \cal L } = { \cal L } _ { 0 } + { \cal L } _ { I } = \left{ \frac { 1 } { 2 } , ( \partial \Phi ) ^ { 2 } - \frac { 1 } { 2 } , m ^ { 2 } \Phi ^ { 2 } \right} + \left{ - \frac { \lambda } { 4 ! } , \Phi ^ { 4 } + { \cal L } _ { \mathrm { c t } } \right} ,