Upload Dataset

README.md

@@ -1,3 +1,69 @@
----
-license: apache-2.0
----
+---
+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

train/dataset_info.json

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+{
+  "builder_name": "ITT-Purpose",
+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "ITT-Purpose",
+  "dataset_size": 100,
+  "description": "ITT-Purpose is a premium, high-quality, hard image-to-text-to-text benchmark dataset designed to evaluate Vision-Language Models (VLM) on document parsing, data extraction, and complex visual reasoning tasks across three styles: Meaning, Text Formatting, and Table.",
+  "features": {
+    "id": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "image": {
+      "_type": "Image"
+    },
+    "prompt": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "response": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "style": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": "apache-2.0",
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_examples": 100,
+      "dataset_name": "ITT-Purpose"
+    }
+  },
+  "version": {
+    "version_str": "0.1.0",
+    "major": 0,
+    "minor": 1,
+    "patch": 0
+  },
+  "task_categories": [
+    "image-text-to-text",
+    "visual-question-answering"
+  ],
+  "language": [
+    "en"
+  ],
+  "pretty_name": "ITT-Purpose",
+  "size_categories": [
+    "100K<n<1M"
+  ],
+  "tags": [
+    "multimodal",
+    "image-text-to-text",
+    "ocr",
+    "table-qa",
+    "latex",
+    "vlm",
+    "benchmark"
+  ],
+  "author": "convence"
+}

train/train-00000-of-99999.parquet

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