Datasets:

Convence
/

ITT-Purpose

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

Meaning: Renders complex technical document segments with multi-hop semantic reasoning questions.
Text Formatting: Renders nested JSON, YAML configs, and Python functions, demanding code structure and detail extraction.
Table: Renders dense telemetry data tables with borders, demanding cell lookups, calculated aggregates, or full markdown table generation.

Usage

from datasets import load_dataset

ds = load_dataset("convence/ITT-Purpose", split="train")
print(ds[0])

License

Apache 2.0