| ---
|
| language:
|
| - en
|
| license: apache-2.0
|
| task_categories:
|
| - image-text-to-text
|
| pretty_name: ITT-Purpose
|
| size_categories:
|
| - 100K<n<1M
|
| tags:
|
| - multimodal
|
| - image-text-to-text
|
| - ocr
|
| - table-qa
|
| - latex
|
| - vlm
|
| - benchmark
|
| dataset_info:
|
| features:
|
| - name: id
|
| dtype: string
|
| - name: image
|
| dtype: image
|
| - name: prompt
|
| dtype: string
|
| - name: response
|
| dtype: string
|
| - name: style
|
| dtype: string
|
| splits:
|
| - name: train
|
| num_examples: 100
|
| config_name: default
|
| ---
|
|
|
| # ITT-Purpose
|
|
|
| **Author:** convence
|
|
|
| **ITT-Purpose** is a premium, hard, and clean benchmark dataset of **100** unique samples
|
| for training and evaluating image-to-text-to-text (Vision-Language) models.
|
|
|
| ## Dataset Structure
|
|
|
| Each sample contains:
|
| - `id`: A unique UUID string identifying the sample.
|
| - `image`: The rendered visual document containing styled text, code configs, or structured tables.
|
| - `prompt`: A high-difficulty instruction requesting visual layout parsing, math calculating, or semantic reasoning.
|
| - `response`: The clean, correct ground truth text.
|
| - `style`: One of three styles (`meaning`, `formatting`, `table`).
|
|
|
| ## Styles Covered
|
|
|
| 1. **Meaning**: Renders complex technical document segments with multi-hop semantic reasoning questions.
|
| 2. **Text Formatting**: Renders nested JSON, YAML configs, and Python functions, demanding code structure and detail extraction.
|
| 3. **Table**: Renders dense telemetry data tables with borders, demanding cell lookups, calculated aggregates, or full markdown table generation.
|
|
|
| ## Usage
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| ds = load_dataset("convence/ITT-Purpose", split="train")
|
| print(ds[0])
|
| ```
|
|
|
| ## License
|
|
|
| Apache 2.0
|
|
|
