| --- |
| language: |
| - en |
| - hi |
| license: cc-by-nc-sa-4.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - table-question-answering |
| - visual-question-answering |
| - image-text-to-text |
| tags: |
| - cricket |
| configs: |
| - config_name: default |
| data_files: |
| - split: test_single |
| path: data/test_single-* |
| - split: test_multi |
| path: data/test_multi-* |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: images |
| sequence: image |
| - name: question |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: category |
| dtype: string |
| - name: subset |
| dtype: string |
| splits: |
| - name: test_single |
| num_bytes: 976385438.0 |
| num_examples: 2000 |
| - name: test_multi |
| num_bytes: 904538778.0 |
| num_examples: 997 |
| download_size: 1573738795 |
| dataset_size: 1880924216.0 |
| --- |
| |
| # MMCricBench 🏏 |
| **Multimodal Cricket Scorecard Benchmark for VQA** |
|
|
| 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). |
|
|
| 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. |
|
|
| --- |
|
|
| ## Overview |
| - **Images:** 1,463 synthetic scorecards (PNG) |
| - 822 single-image scorecards |
| - 641 multi-image scorecards |
| - **QA pairs:** 1,500 (English) |
| - **Reasoning categories:** |
| - **C1** – Direct retrieval & simple inference |
| - **C2** – Basic arithmetic & conditional logic |
| - **C3** – Multi-step quantitative reasoning (often across images) |
|
|
| --- |
|
|
| ## Files / Splits |
| We provide two evaluation splits: |
| - `test_single` — single-image questions |
| - `test_multi` — multi-image questions |
|
|
| > 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. |
| |
| --- |
| |
| ## Data Schema |
| Each row is a JSON object: |
| |
| | Field | Type | Description | |
| |------------|---------------------|----------------------------------------------| |
| | `id` | `string` | Unique identifier | |
| | `images` | `list[string]` | Paths to one or more scorecard images | |
| | `question` | `string` | Question text (English) | |
| | `answer` | `string` | Ground-truth answer (canonicalized) | |
| | `category` | `string` (`C1/C2/C3`)| Reasoning category | |
| | `subset`* | `string` (`single/multi`) | Optional convenience field | |
| |
| **Example (single-image):** |
| ```json |
| {"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"} |
| ``` |
| |
| ## Loading & Preview |
| |
| ### Load from the Hub (two-split layout) |
| ```python |
| from datasets import load_dataset |
|
|
| # Loads: DatasetDict({'test_single': ..., 'test_multi': ...}) |
| ds = load_dataset("DIALab/MMCricBench") |
| print(ds) |
| |
| # Peek a single-image example |
| ex = ds["test_single"][0] |
| print(ex["id"]) |
| print(ex["question"], "->", ex["answer"]) |
| |
| # Preview images (each example stores a list of PIL images) |
| from IPython.display import display |
| for img in ex["images"]: |
| display(img) |
| ``` |
| |
| ## Baseline Results (from the paper) |
| |
| Accuracy (%) on MMCricBench by split and language. |
| |
| | Model | #Params | Single-EN (Avg) | Single-HI (Avg) | Multi-EN (Avg) | Multi-HI (Avg) | |
| |-------------------|:------:|:---------------:|:---------------:|:--------------:|:--------------:| |
| | SmolVLM | 500M | 19.2 | 19.0 | 11.8 | 11.6 | |
| | Qwen2.5VL | 3B | 40.2 | 33.3 | 31.2 | 22.0 | |
| | LLaVA-NeXT | 7B | 28.3 | 26.6 | 16.2 | 14.8 | |
| | mPLUG-DocOwl2 | 8B | 20.7 | 19.9 | 15.2 | 14.4 | |
| | Qwen2.5VL | 7B | 49.1 | 42.6 | 37.0 | 32.2 | |
| | InternVL-2 | 8B | 29.4 | 23.4 | 18.6 | 18.2 | |
| | Llama-3.2-V | 11B | 27.3 | 24.8 | 26.2 | 20.4 | |
| | **GPT-4o** | — | **57.3** | **45.1** | **50.6** | **43.6** | |
| |
| *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.* |
| |
| ## Contact |
| For questions or issues, please open a discussion on the dataset page or email **Abhirama Subramanyam** at penamakuri.1@iitj.ac.in |
