FlagEval

What is FlagEval

FlagEval (Libra) is a scientific, fair, and open large model evaluation system and open platform launched by the Beijing Academy of Artificial Intelligence (BAAI), providing researchers with tools and methods to comprehensively evaluate the performance of foundation models and training algorithms. FlagEval adopts a three-dimensional evaluation framework of "capability-task-metric", evaluating the cognitive abilities of large models from multiple dimensions, covering various application scenarios such as dialogue, question answering, and sentiment analysis, and providing more than 22 datasets and 80,000 evaluation questions. The platform supports multimodal model evaluation, covering various data types such as text, images, and video, and is compatible with multiple AI frameworks and hardware architectures. FlagEval provides an automated evaluation mechanism, supporting a fully automated pipeline for subjective and objective evaluation, helping researchers efficiently and accurately understand model performance and promoting the development of large model technology.

Main features of FlagEval

• Multidimensional evaluation framework: Adopts a three-dimensional evaluation framework of "capability-task-metric", comprehensively evaluating the cognitive abilities of large models from multiple dimensions, covering various application scenarios such as dialogue, question answering, and sentiment analysis.
• Rich evaluation datasets: Provides more than 22 datasets and 80,000 evaluation questions, covering different application scenarios, difficulty levels, and language types, ensuring the comprehensiveness and accuracy of evaluation.
• Multimodal support: Supports model evaluation across multiple modalities such as text, images, and video, meeting the evaluation needs of different types of models.
• Automated evaluation mechanism: Implements a fully automated pipeline for subjective and objective evaluation, supports an adaptive evaluation mechanism, and allows users to choose evaluation strategies based on model type and status, improving evaluation efficiency.
• Broad model coverage: Covers more than 800 open-source and closed-source models, supports multiple AI frameworks (such as PyTorch and MindSpore) and hardware architectures (such as NVIDIA, Ascend, Cambricon, and Kunlunxin, etc.).
• Leaderboard and result display: Provides detailed evaluation data tables and leaderboards, displaying evaluation results of different models, helping researchers intuitively understand model performance.
• Community participation and continuous updates: Encourages community participation, welcomes researchers and developers to contribute evaluation datasets and models, and continuously updates evaluation content, ensuring the timeliness and comprehensiveness of evaluation.

How to use FlagEval

• Registration and login: Visit the FlagEval official website, register and log in to a user account.
• Prepare model and code: According to the requirements of the FlagEval platform, prepare the model files, inference code, and related configuration files to be evaluated. For example, in the field of computer vision (CV), basic information about the model needs to be provided, such as preprocessing parameters for input images and task-related batch size, etc.
• Install the FlagEval-Serving tool: By installing the FlagEval-Serving tool, users can upload files such as models, code, and data to be evaluated.
• Upload model and code: On the FlagEval platform, click "Upload Model & Code" to obtain the required tokenfor uploading, then use the command-line tool to upload the model files and code.
• Create evaluation task: On the evaluation task list page, click "Create Evaluation" and fill in the relevant parameters, including evaluation domain, model name, description, evaluation task, image selection, card type selection, etc.
• Submit the evaluation task: After completing the above settings, submit the evaluation task, and the platform will automatically run the evaluation process.
• View evaluation results: After the evaluation is completed, users can view detailed evaluation results on the platform, including performance metrics, visualization charts, etc.
• Notes：
  • Data preparation: Ensure the quality and relevance of the evaluation task data to obtain accurate results.
  • Model consistency: The same evaluation task needs to be compared under the same model version to avoid interference.
  • Parameter settings: Adjust evaluation parameters reasonably, such as sample size and running time, to ensure fairness.
  • Result interpretation: Pay attention to confidence intervals and statistical significance to avoid misinterpreting differences in small samples.

Application scenarios of FlagEval

• Academic research and model development: FlagEval provides researchers with standardized evaluation tools and datasets, helping them deeply analyze model performance across different tasks and scenarios, and optimize research directions and model architectures. Researchers can use FlagEval to evaluate the performance of different models, diagnose the strengths and weaknesses of models, and guide further model optimization.
• Industrial applications and enterprise decision-making: Enterprises can use FlagEval to evaluate internally developed models or models provided by third parties, supporting business decisions and product selection.
• Multimodal and cross-domain applications: FlagEval supports the evaluation of multimodal models, including tasks across multiple modalities such as text, images, video, and audio. For example, when developing text-to-image generation models, FlagEval can help developers gain deep insight into the model's performance at different levels of detail and optimize the generation logic.
• Education and talent cultivation: Educational institutions can use FlagEval for teaching and research, helping students and researchers master the methods and techniques of AI model evaluation and cultivate professionals in the AI field.
• International model comparison and ecosystem building: FlagEval covers more than 800 open-source and closed-source models worldwide, supports performance comparison of models across countries, helps users understand the gaps between domestic and international models, and promotes international exchange and cooperation in AI technology.