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README.md

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+
+## Dataset Card for m-WildVision
+
+<img src="mwildvision2.png" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+### Dataset Details
+
+The m-WildVision dataset is a multilingual multimodal LLM evaluation set covering **23 languages**.  It was created by translating prompts from the original English-only [WildVision (vision_bench_0617)](https://arxiv.org/abs/2406.11069) test set. 
+The original prompts, developed by [Lu et al. (2024)](https://arxiv.org/abs/2406.11069) , consist of 500 challenging user queries sourced from the WildVision-Arena platform. 
+The authors demonstrated that these prompts enable automatic LLM judge evaluations, which strongly correlate with WildVision-Arena rankings. 
+
+### Languages:
+
+To ensure multilingual coverage, the non-English portion of the dataset was generated by translating the English subset into 22 additional languages using Google Translate API v3. The dataset includes a diverse range of language families (such as Latin-based languages like French, German, Arabic families like Persian and Arabic, and East Asian languages like Chinese, Korean, and Japanese) and scripts, ensuring a comprehensive evaluation of model generalizability and robustness.
+
+The languages included are: Arabic (arb_Arab), Chinese (zho_Hans), Czech (ces_Latn), Dutch (nld_Latn), English (eng_Latn), French (fra_Latn), German (deu_Latn), Greek (ell_Grek), Hebrew (heb_Hebr), Hindi (hin_Deva), Indonesian (ind_Latn), Italian (ita_Latn), Japanese (jpn_Jpan), Korean (kor_Hang), Persian (fas_Arab), Polish (pol_Latn), Portuguese (por_Latn), Romanian (ron_Latn), Russian (rus_Cyrl), Spanish (spa_Latn), Turkish (tur_Latn), Ukrainian (ukr_Cyrl), and Vietnamese (vie_Latn).
+
+By incorporating languages from different families and scripts, this benchmark enables a **comprehensive assessment of vision-language models**, particularly their ability to generalize across diverse languages.
+
+### Load with Datasets
+
+To load this dataset with Datasets, you'll need to install Datasets as `pip install datasets --upgrade` and then use the following code:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("CohereForAI/m-WildVision", "eng_Latn")
+```
+
+The above code block will load only the English subset of the entire dataset. You can load other subsets by specifying other supported languages of interest or the entire dataset by leaving that argument as blank.
+
+
+### Dataset Structure
+
+An instance of the data from the English subset looks as follows:
+
+```python
+{'question_id': a711a80b19c040c2a98364b5e181b020,
+ 'language': 'eng_Latn',
+ 'question': 'How many workers are working in the construction site? Are all of them wearing the safety equipments? If no, who are not wearing them?'
+'image': [PIL.Image],
+}
+```
+
+### Dataset Fields
+
+The following are the fields in the dataset:
+
+- question_id: a unique ID for the example
+- language: The language of the sample, indicating the subset to which it belongs.
+- instruction: text of the prompt (question or instruction)
+- image: The raw image data in .jpg format.
+
+  
+All language subsets of the dataset share the same fields as above.
+
+### Authorship
+
+- Publishing Organization: [Cohere For AI](https://cohere.com/research)
+- Industry Type: Not-for-profit - Tech
+- Contact Details: https://cohere.com/research/aya
+
+### Licensing Information
+
+This dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License.