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--- |
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language: |
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- en |
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- hi |
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license: cc-by-nc-sa-4.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- table-question-answering |
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- visual-question-answering |
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- image-text-to-text |
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tags: |
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- cricket |
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configs: |
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- config_name: default |
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data_files: |
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- split: test_single |
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path: data/test_single-* |
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- split: test_multi |
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path: data/test_multi-* |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: images |
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sequence: image |
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- name: question |
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dtype: string |
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- name: answer |
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dtype: string |
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- name: category |
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dtype: string |
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- name: subset |
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dtype: string |
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splits: |
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- name: test_single |
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num_bytes: 976385438.0 |
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num_examples: 2000 |
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- name: test_multi |
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num_bytes: 904538778.0 |
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num_examples: 997 |
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download_size: 1573738795 |
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dataset_size: 1880924216.0 |
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--- |
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# MMCricBench 🏏 |
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**Multimodal Cricket Scorecard Benchmark for VQA** |
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This repository contains the dataset for the paper [Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs](https://huggingface.co/papers/2508.17334). |
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MMCricBench evaluates **Large Vision-Language Models (LVLMs)** on **numerical reasoning**, **cross-lingual understanding**, and **multi-image reasoning** over semi-structured cricket scorecard images. It includes English and Hindi scorecards; all questions/answers are in English. |
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--- |
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## Overview |
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- **Images:** 1,463 synthetic scorecards (PNG) |
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- 822 single-image scorecards |
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- 641 multi-image scorecards |
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- **QA pairs:** 1,500 (English) |
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- **Reasoning categories:** |
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- **C1** – Direct retrieval & simple inference |
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- **C2** – Basic arithmetic & conditional logic |
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- **C3** – Multi-step quantitative reasoning (often across images) |
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--- |
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## Files / Splits |
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We provide two evaluation splits: |
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- `test_single` — single-image questions |
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- `test_multi` — multi-image questions |
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> If you keep a single JSONL (e.g., `test_all.jsonl`), use a **list** for `images` in every row. Single-image rows should have a one-element list. On the Hub, we expose two test splits. |
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--- |
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## Data Schema |
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Each row is a JSON object: |
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| Field | Type | Description | |
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|------------|---------------------|----------------------------------------------| |
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| `id` | `string` | Unique identifier | |
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| `images` | `list[string]` | Paths to one or more scorecard images | |
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| `question` | `string` | Question text (English) | |
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| `answer` | `string` | Ground-truth answer (canonicalized) | |
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| `category` | `string` (`C1/C2/C3`)| Reasoning category | |
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| `subset`* | `string` (`single/multi`) | Optional convenience field | |
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**Example (single-image):** |
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```json |
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{"id":"english-single-9","images":["English-apr/single_image/1198246_2innings_with_color1.png"],"question":"Which bowler has conceded the most extras?","answer":"Wahab Riaz","category":"C2","subset":"single"} |
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``` |
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## Loading & Preview |
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### Load from the Hub (two-split layout) |
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```python |
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from datasets import load_dataset |
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# Loads: DatasetDict({'test_single': ..., 'test_multi': ...}) |
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ds = load_dataset("DIALab/MMCricBench") |
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print(ds) |
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# Peek a single-image example |
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ex = ds["test_single"][0] |
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print(ex["id"]) |
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print(ex["question"], "->", ex["answer"]) |
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# Preview images (each example stores a list of PIL images) |
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from IPython.display import display |
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for img in ex["images"]: |
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display(img) |
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``` |
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## Baseline Results (from the paper) |
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Accuracy (%) on MMCricBench by split and language. |
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| Model | #Params | Single-EN (Avg) | Single-HI (Avg) | Multi-EN (Avg) | Multi-HI (Avg) | |
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|-------------------|:------:|:---------------:|:---------------:|:--------------:|:--------------:| |
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| SmolVLM | 500M | 19.2 | 19.0 | 11.8 | 11.6 | |
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| Qwen2.5VL | 3B | 40.2 | 33.3 | 31.2 | 22.0 | |
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| LLaVA-NeXT | 7B | 28.3 | 26.6 | 16.2 | 14.8 | |
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| mPLUG-DocOwl2 | 8B | 20.7 | 19.9 | 15.2 | 14.4 | |
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| Qwen2.5VL | 7B | 49.1 | 42.6 | 37.0 | 32.2 | |
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| InternVL-2 | 8B | 29.4 | 23.4 | 18.6 | 18.2 | |
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| Llama-3.2-V | 11B | 27.3 | 24.8 | 26.2 | 20.4 | |
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| **GPT-4o** | — | **57.3** | **45.1** | **50.6** | **43.6** | |
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*Numbers are exact-match accuracy (higher is better). For C1/C2/C3 breakdowns, see Table 3 (single-image) and Table 5 (multi-image) in the paper.* |
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## Contact |
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For questions or issues, please open a discussion on the dataset page or email **Abhirama Subramanyam** at [email protected] |
