想给GitHub仓库自动打行业标签？这篇论文发布的数据集和管道精度高达97%，直接用RoBERTa或GPT-4.1就能复现，省去手动标注的麻烦。
论文发布NAICS-GH数据集，包含6588个GitHub仓库，每个标注了NAICS 2022的2位数行业代码。管道结合BAAI/bge-large-en嵌入、FAISS检索和GPT-4.1评分，从约137万仓库中筛选出31798个候选对，最终保留6588个高置信度标签（分数≥8）。在2421个人工验证样本上，标签精度达96.98%，Wilson 95%置信区间[96.23,97.59]。基准测试中RoBERTa-large在20%测试集上达到86.45% F1和86.35%准确率。数据集和代码以CC-BY-4.0和MIT许可证开源。
Industry Classification of GitHub Repositories Using the North American Industry Classification System (NAICS)
GitHub hosts hundreds of millions of public repositories, but the platform exposes no native mapping from repositories to standardized industry sectors. This gap limits empirical work on the geography of innovation, the industrial composition of open-source production, and the diffusion of new technologies across economic sectors. We present NAICS-GH, a publicly released corpus of 6,588 GitHub repositories drawn from source pools covering the United States, the European Union, and Australia, each labeled with a 2-digit sector from the North American Industry Classification System (NAICS 2022). Labels are produced by a retrieve-and-verify pipeline that combines BAAI/bge-large-en embeddings, FAISS retrieval, and GPT-4.1 rubric scoring. The pipeline narrows about 1.37 million source repositories to 31,178 candidate repository-sector pairs and retains 6,588 high-confidence labels with score at least 8. Re-running the retrieval pipeline end to end reproduces the candidate set to within 0.03 percent. On a 2,421-repository human-validated random sample, the released labels attain 96.98 percent precision, with Wilson 95 percent confidence interval [96.23, 97.59]. We benchmark six pretrained encoders on the released corpus; RoBERTa-large reaches 86.45 percent F1 and 86.35 percent accuracy on a held-out 20 percent test set. The dataset, Croissant metadata, pipeline code, prompts, and fine-tuned checkpoint are released under CC-BY-4.0 and MIT licenses.