BalancedCommitBench: A Language-Balanced Commit Messages Subset Extracted from CommitBench:We base our experiments on CommitBench, a large-scale benchmark for commit message generation introduced by Schall et al.(2024) . CommitBench aggregates more than one million real commits collected from thousands of open-source repositories across six major programming languages: Python, Java, JavaScript, Go, PHP, and Ruby. Each instance contains a git diff, the corresponding human-written commit message, and metadata such as author, timestamp, and repository name.From this corpus, we derive BalancedCommitBench, a language-balanced subset obtained through systematic preprocessing. The dataset is cleaned via language normalization, de-duplication, removal of bot-generated and low-information commits, and length-based filtering, followed by uniform sampling across the six languages to ensure balanced representation. M. Schall, T. Czinczoll and G. De Melo, "CommitBench: A Benchmark for Commit Message Generation," 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Rovaniemi, Finland, 2024, pp. 728-739, doi: 10.1109/SANER60148.2024.00080.

BalancedCommitBench: A Language-Balanced Commit Messages Subset Extracted from CommitBench:We base our experiments on CommitBench, a large-scale benchmark for commit message generation introduced by Schall et al.(2024) . CommitBench aggregates more than one million real commits collected from thousands of open-source repositories across six major programming languages: Python, Java, JavaScript, Go, PHP, and Ruby. Each instance contains a git diff, the corresponding human-written commit message, and metadata such as author, timestamp, and repository name.From this corpus, we derive BalancedCommitBench, a language-balanced subset obtained through systematic preprocessing. The dataset is cleaned via language normalization, de-duplication, removal of bot-generated and low-information commits, and length-based filtering, followed by uniform sampling across the six languages to ensure balanced representation. M. Schall, T. Czinczoll and G. De Melo, "CommitBench: A Benchmark for Commit Message Generation," 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Rovaniemi, Finland, 2024, pp. 728-739, doi: 10.1109/SANER60148.2024.00080.

<p><strong>Dataset Description</strong></p><p>This dataset is a collection of recited Arabic poetry designed for speech-based poetry recognition and meter classification tasks. It comprises <strong>3,805 annotated audio recordings</strong>, where each sample represents a single <em>Bait</em> (verse) from classical Arabic poetry. Each verse is labeled according to one of the <strong>16 canonical Arabic poetic meters (Al-Buḥūr)</strong>.</p><p>All recordings were captured under controlled laboratory conditions using professional-grade audio equipment to ensure high-quality acoustic signals. This makes the dataset well-suited for applications in speech processing, machine learning, and computational analysis of Arabic prosody.</p><p>The dataset contains approximately <strong>9 hours of audio data</strong>, with an average duration of around <strong>9 seconds per sample</strong>. It is intended to support research in areas such as automatic poetry recognition, meter classification, speech recognition for structured language, and Arabic linguistic analysis.</p><hr><p><strong>License and Usage</strong></p><p><strong>Important Disclaimer</strong><br>This dataset is published strictly for academic and research purposes.</p><p><strong>Commercial Use</strong><br>Commercial use of this dataset requires explicit permission from the dataset owner.</p><p><strong>Licensing Contact</strong></p><ul> <li><strong>Name:</strong> Dr. Abdul Kareem Saleh Al-Zahrani</li> <li><strong>Email:</strong> <a href="mailto:[email protected]">[email protected]</a></li> <li><strong>Faculty Page:</strong> <a href="https://faculty.kfupm.edu.sa/ias/akareem/index.htm" target="_blank"> https://faculty.kfupm.edu.sa/ias/akareem/index.htm</a> </li></ul><p>Unauthorized commercial use of this dataset is strictly prohibited and may result in legal consequences.</p>