FullStack Bench
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Fullstack Bench
Overview :
FullStack Bench is a multi-language full-stack programming benchmark that spans a wide array of application domains and includes 3,000 test samples across 16 programming languages, significantly advancing the capabilities of code language models in real-world code development scenarios. This product represents the application of programming language models in the full-stack development field, where its significance lies in its ability to evaluate and enhance model performance in practical programming tasks, making it a valuable resource for developers and AI researchers.
Target Users :
The target audience includes developers, AI researchers, and enterprises needing to evaluate the performance of programming models. FullStack Bench offers a standardized testing platform that assists them in assessing and enhancing model performance on real-world programming tasks, which is crucial for improving development efficiency and model accuracy.
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Top Region: US(17.94%)
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Use Cases
Used to evaluate the performance of specific programming language models on particular programming tasks.
Serves as a teaching tool to help students understand the strengths and weaknesses of different programming language models.
Provides a reference for enterprises in selecting programming models that fit their development needs.
Features
Covers 16 programming languages and 3,000 test samples for comprehensive model assessment.
Supports multiple languages, suitable for developers and researchers across different programming languages.
Provides standardized data formats for convenient evaluation of various programming tasks.
Offers services through a unified HTTP API for easy integration and usage.
Combines over 10 programming-related evaluation datasets, providing diverse testing scenarios.
Enhances the relevant capabilities of code language models in realistic code development contexts.
How to Use
1. Visit the FullStack Bench GitHub page to access the code and datasets.
2. Install the necessary dependencies and environment according to the guidelines.
3. Start the SandboxFusion sandbox server to execute code evaluations.
4. Run benchmark tests and modify model configurations as needed.
5. Analyze the testing results and assess model performance across various programming tasks.
6. Optimize the model or adjust development strategies based on the testing results.
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