| --- |
| dataset_info: |
| - config_name: de |
| features: |
| - name: image_url |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: question |
| dtype: string |
| - name: answer |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 28205120 |
| num_examples: 55486 |
| download_size: 16155243 |
| dataset_size: 28205120 |
| - config_name: en |
| default: true |
| features: |
| - name: image_url |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: question |
| dtype: string |
| - name: answer |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 48459165 |
| num_examples: 103737 |
| download_size: 26655761 |
| dataset_size: 48459165 |
| - config_name: es |
| features: |
| - name: image_url |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: question |
| dtype: string |
| - name: answer |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 29158196 |
| num_examples: 55486 |
| download_size: 16351717 |
| dataset_size: 29158196 |
| - config_name: fr |
| features: |
| - name: image_url |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: question |
| dtype: string |
| - name: answer |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 30578897 |
| num_examples: 55486 |
| download_size: 17218972 |
| dataset_size: 30578897 |
| - config_name: it |
| features: |
| - name: image_url |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: question |
| dtype: string |
| - name: answer |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 30283178 |
| num_examples: 55486 |
| download_size: 17197560 |
| dataset_size: 30283178 |
| configs: |
| - config_name: de |
| data_files: |
| - split: train |
| path: de/train-* |
| - config_name: en |
| data_files: |
| - split: train |
| path: en/train-* |
| - config_name: es |
| data_files: |
| - split: train |
| path: es/train-* |
| - config_name: fr |
| data_files: |
| - split: train |
| path: fr/train-* |
| - config_name: it |
| data_files: |
| - split: train |
| path: it/train-* |
| license: odc-by |
| language: |
| - en |
| - fr |
| - it |
| - de |
| - es |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Multi-PixMo-Cap-QA |
|
|
| ## Overview |
| **Multi-PixMo-Cap-QA** is a **multilingual** version of [Multi-PixMo-Cap](https://huggingface.co/datasets/VillanovaAI/multi-pixmo-cap) dataset by Villanova. |
| Both datasets are based on the PixMo series developed by AllenAI. |
|
|
| The original PixMo-Cap dataset was created by recording annotators speaking freely about an image for **60–90 seconds**, then transforming the resulting audio transcripts into detailed captions using **Claude** (see the [PixMo paper](https://arxiv.org/html/2409.17146)). |
|
|
| **Multi-PixMo-Cap** followed the same multimodal concept, but all examples were **re-generated from human captions** using a **permissively licensed** model: **Qwen3-VL-235B-A22B-Instruct**. |
| This results in a multilingual dataset that mirrors the PixMo-Cap style while being fully based on open-source generation methods. |
|
|
| **Multi-PixMoCap-QA** dataset used the same images and captions of the Multi-PixMo-Cap dataset and used them to generate a Q&A set for each sample. The model was provided with the image, the caption and a predefined list of question types. After viewing the image, the model selected the most suitable question type from the list and generated a corresponding Q&A pair. |
|
|
| ## Model: |
| The dataset was generated using the multimodal, open LLM **Qwen/Qwen3.5-122B-A10B-FP8**. |
|
|
|
|
| ## Language Distribution |
| **Multi-PixMo-Cap-QA** contains **five subsets**, one per **language**, distributed as follows: |
|
|
| | Language | Subset Name | Percentage | Number of Examples | |
| |----------|------------|------------|--------------------| |
| | English | en | 32% | 103737 | |
| | Italian | it | 17% | 55486 | |
| | French | fr | 17% | 55486 | |
| | Spanish | es | 17% | 55486 | |
| | German | de | 17% | 55486 | |
|
|
| English accounts for 32% of all examples, while the other four languages collectively make up **68%**, distributed evenly. |
|
|
| ## Question type distribution |
| Since the model autonomously selected the most fitting task type for each image, the distribution of question types across the dataset is uneven. Below is a table showing the percentage distribution for each task. |
|
|
|
|
| #### Total dataset entries: 325681 |
|
|
| | Question type | Count | Percentage | |
| |----------|------------|------------| |
| text_recognition | 76076 | 23.36% |
| attribute_and_properties | 70747 | 21.72% |
| formatting_task |53797 | 16.52% |
| aesthetic_judgement | 41040 | 12.60% |
| creative_writing |29637 | 9.10% |
| emotion_interpretation |29410 | 9.03% |
| actions_and_events |14386 | 4.42% |
| comparative_reasoning |3930 | 1.21% |
| count_measure |1889 | 0.58% |
| identify_object |1646 | 0.51% |
| scene_and_context |1633 | 0.50% |
| common-sense_world-knowledge |1486 | 0.46% |
| categorization_tagging |4 | 0.00% |
|
|
|
|
| ## Intended Use |
| This dataset aims to provide **multilingual question-answer pairs** suitable for training multimodal models. |
|
|
| It also offers a Q&A corpus **produced by a permissively licensed model to ensure that the resulting dataset can be freely reused**, including for the development and training of new models. |
|
|
| ### Dataset Structure |
| Each subset contains the following **fields**: |
| | Field | Type | |
| |-------|------| |
| | `image_url` | string | |
| | `question_type` | string | |
| | `question` | string |
| | `answer` | string |
|
|
| ## Licensing |
| The dataset is distributed under the **ODC-BY-1.0** license. |
| Users must adhere to the attribution and usage requirements outlined under this license. |
