https://www.swebench.com/
SWE-bench is a comprehensive platform designed to evaluate language models on real-world GitHub issues. It offers a dataset of 2,294 issue-pull request pairs from 12 popular Python repositories, enabling users to assess and improve AI systems' capabilities in resolving software development challenges. The platform provides various resources, including pre-processed datasets for fine-tuning models, a leaderboard showcasing top-performing models, and specialized subsets like SWE-bench Multimodal and SWE-bench Verified for targeted evaluations. By utilizing SWE-bench, developers and researchers can benchmark and enhance their language models' performance in practical software engineering tasks.
SWE-bench is a comprehensive platform designed to evaluate language models on real-world GitHub issues. It offers a dataset of 2,294 issue-pull request pairs from 12 popular Python repositories, enabling users to assess and improve AI systems' capabilities in resolving software development challenges. The platform provides various resources, including pre-processed datasets for fine-tuning models, a leaderboard showcasing top-performing models, and specialized subsets like SWE-bench Multimodal and SWE-bench Verified for targeted evaluations. By utilizing SWE-bench, developers and researchers can benchmark and enhance their language models' performance in practical software engineering tasks.
https://www.swebench.com/
SWE-bench is a comprehensive platform designed to evaluate language models on real-world GitHub issues. It offers a dataset of 2,294 issue-pull request pairs from 12 popular Python repositories, enabling users to assess and improve AI systems' capabilities in resolving software development challenges. The platform provides various resources, including pre-processed datasets for fine-tuning models, a leaderboard showcasing top-performing models, and specialized subsets like SWE-bench Multimodal and SWE-bench Verified for targeted evaluations. By utilizing SWE-bench, developers and researchers can benchmark and enhance their language models' performance in practical software engineering tasks.
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