

P MMEval
Overview :
P-MMEval is a multilingual benchmark that encompasses datasets focused on foundational and capability specialization. It extends existing benchmarks to ensure consistency in language coverage and provides parallel samples across various languages, supporting up to 10 languages from 8 language families. P-MMEval facilitates comprehensive assessment of multilingual capabilities and comparative analysis of cross-language transferability.
Target Users :
The target audience consists of researchers, developers, and educational institutions that need to evaluate and compare the performance and capabilities of different language models in multilingual contexts. P-MMEval provides a standardized testing platform that enables cross-language and cross-model comparisons.
Use Cases
Researchers use P-MMEval to assess the performance of various language models on specific tasks.
Educational institutions utilize P-MMEval to compare the teaching effectiveness of different language models.
Developers leverage P-MMEval to optimize and tune their language models for multilingual environments.
Features
Supports up to 10 languages including English, Chinese, Arabic, Spanish, French, Japanese, Korean, Portuguese, Thai, and Vietnamese.
Provides parallel samples to support evaluations and comparative analyses of cross-language capabilities.
Covers foundational and capability-specialized datasets suitable for comprehensive assessment of multilingual skills.
Facilitates performance comparisons between closed-source and open-source models.
Offers data previews, dataset file downloads, and quick usage guides.
Supports the use of OpenCompass for LLM evaluations.
Provides accelerated evaluations with vllm (vllm installation is required).
How to Use
1. Visit the ModelScope page for P-MMEval.
2. Read the dataset introduction to understand the background and purpose of P-MMEval.
3. Preview the data to view the samples included in P-MMEval.
4. Download the dataset files and prepare for model evaluation.
5. Configure OpenCompass and vllm for model evaluation according to the quick start guide.
6. Initiate the evaluation process using CLI commands or Python scripts.
7. Analyze the evaluation results to compare the performance of different models.
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