Datasets:
File size: 1,747 Bytes
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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
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