README.md

metadata

pretty_name: Punjabi Multimodal Visual Reasoning (PuMVR)
tags:
  - multimodal
  - visual-question-answering
  - multi-script
  - low-resource-language
  - punjabi
  - image-to-text
  - multiple-choice
language:
  - pa
  - en
language_bcp47:
  - pa-Guru
  - pa-Arab
  - pa-Latn
task_categories:
  - visual-question-answering
  - image-to-text
  - multiple-choice
  - question-answering
license: cc-by-4.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
multilinguality: multi-script
annotations_creators:
  - human
language_creators:
  - native-speakers
size_categories:
  - 100M<X<1B
dataset_info:
  features:
    - name: id
      dtype: string
    - name: image
      dtype: image
    - name: reasoning
      dtype: string
    - name: scripts_gurmukhi_question
      dtype: string
    - name: scripts_gurmukhi_options
      list: string
    - name: scripts_gurmukhi_answer
      dtype: string
    - name: scripts_shahmukhi_question
      dtype: string
    - name: scripts_shahmukhi_options
      list: string
    - name: scripts_shahmukhi_answer
      dtype: string
    - name: scripts_roman_question
      dtype: string
    - name: scripts_roman_options
      list: string
    - name: scripts_roman_answer
      dtype: string
  splits:
    - name: train
      num_bytes: 784495831
      num_examples: 1000
  download_size: 1568664333
  dataset_size: 784495831

PuMVR: Punjabi Multimodal Visual Reasoning Benchmark

Paper

This dataset accompanies the paper: PuMVR

🌟 Dataset Overview

PuMVR (Punjabi Multimodal Visual Reasoning) is a novel benchmark designed to evaluate script-dependent performance biases in Vision-Language Models (VLMs). It addresses the critical gap that current VLM evaluations fail to test whether models are truly multi-script, a distinction vital for languages like Punjabi which are actively written in multiple scripts.

The dataset features 1000 unique image-text reasoning tasks focused on Punjabi culture, history, and daily life. All instances are translated and rigorously validated across the three active Punjabi writing systems: Gurmukhi (pa-Guru), Shahmukhi (pa-Arab), and Roman (pa-Latn).

• Total Instances: 1000
• Total Size: 749 MB
• Language: Punjabi (pa) with three distinct script variants.
• Target Models: State-of-the-art VLMs

---

📊 Dataset Structure and Statistics

The dataset is organized into a single split (train) and is composed of image data and corresponding textual annotations stored in a JSON file.

Data Fields

The dataset schema contains all necessary components for running multiple-choice VQA across three scripts:

Field Name	Data Type	Description
id	string	Unique identifier (e.g., I_1.png).
image	Image	The associated visual input (decoded from the file path).
reasoning	string	Human-written explanation for the ground truth answer (in English).
scripts_[script]_question	string	The reasoning question in the specified script.
scripts_[script]_options	list[string]	4 multiple-choice options in the specified script.
scripts_[script]_answer	string	The single correct option in the specified script.

(The [script] placeholder is one of: gurmukhi, shahmukhi, or roman.)

---

⚖️ Ethical and Legal Considerations

Licenses

• Data: The PuMVR dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
• Images: Majority (approximately 95%) of the images are AI-generated (synthetic data) to ensure cultural specificity and clear licensing. The remaining images are sourced from public domain, Wikimedia Commons, and original photography.

Data Creation and Validation

The textual data was created and rigorously validated by a team of native speakers across both Indian and Pakistani Punjabi contexts to ensure semantic equivalence and cultural appropriateness across the Gurmukhi, Shahmukhi, and Roman scripts.

Limitations

The dataset is highly focused on Punjabi culture, which introduces a domain-specific bias. The Romanization used reflects common digital usage but is not strictly standardized, mirroring real-world multi-script challenges.