| --- |
| configs: |
| - config_name: combined_all |
| default: true |
| data_files: |
| - split: train |
| path: |
| - parquet/section1/train-*.parquet |
| - parquet/section2/train-*.parquet |
| - config_name: section1_all |
| data_files: |
| - split: train |
| path: parquet/section1/train-*.parquet |
| - config_name: section2_all |
| data_files: |
| - split: train |
| path: parquet/section2/train-*.parquet |
| task_categories: |
| - visual-question-answering |
| tags: |
| - synthetic |
| - visual-question-answering |
| - parquet |
| --- |
| |
| # BlindLoop Generations |
|
|
| A flat, general-purpose visual-question-answering dataset generated by the |
| BlindLoop paper experiments. Each row is one concrete question instance with a |
| native Hugging Face `Image` value, question, gold answer, answer choices, and |
| fully filterable generation provenance. |
|
|
| | Config | Rows | Tasks | Unique source images | |
| |---|---:|---:|---:| |
| | `section1_all` | 516,810 | 1,301 | 249,488 | |
| | `section2_all` | 335,271 | 875 | 166,364 | |
| | `combined_all` | 852,081 | 2,176 | 415,852 | |
|
|
| ## Main columns |
|
|
| - `image`: embedded image bytes decoded by Hugging Face as an image. |
| - `question`, `answer`, `answer_options`: the VQA instance and gold target. |
| - `profile`, `generator_model`, `generator_harness`, `reasoning_effort`: generation treatment. |
| - `campaign_id`, `candidate_id`, `record_id`, `example_id`, `prompt_family`: exact provenance. |
| - `margin`, `quarantined`, `mechanically_eligible`, `human_admission`: verification and review state. |
| - `pixel_support_scale`, `pixel_support_shape`, `pixel_diversity_cell`: Section 2 pixel-diversity descriptors; empty for Section 1. |
| - `image_sha256`, `sample_id`: immutable media and row identifiers. |
|
|
| This archive includes all verified replay outputs, including explicitly flagged |
| quarantined rows for auditability. Mechanical verification is not a substitute |
| for human admission; `human_admission` preserves the source status. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("taesiri/BlindLoop-Generations", "section1_all", split="train") |
| row = ds[0] |
| row["image"].show() |
| print(row["question"], row["answer"]) |
| ``` |
|
|
