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Source note2 min read

Industry Classification of GitHub Repositories Using the North American Industry Classification System (NAICS)

Source

arXiv

arxiv.org/abs/2607.06505v1

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Plain-Language Brief

Researchers have developed NAICS-GH, a corpus of 6,588 GitHub repositories labeled with industry sectors according to the North American Industry Classification System (NAICS). The labels are generated using a pipeline that combines AI models and attain high precision. The dataset is publicly available under CC-BY-4.0 and MIT licenses.

What the Document Says

  • The study presents NAICS-GH, a collection of 6588 GitHub repositories labeled with 2-digit industry sectors using the North American Industry Classification System (NAICS 2022).
  • The labels are produced by a pipeline involving BAAI/bge-large-en embeddings, FAISS retrieval, GPT-4.1 rubric scoring, and narrows about 1.37 million source repositories to 6588 high-confidence labels with score at least 8.
  • On a human-validated random sample of 2421 repositories, the released labels have 96.98% precision with a Wilson 95 percent confidence interval of [96.23, 97.59].
  • The study benchmarks six pretrained encoders on the released corpus, with RoBERTa-large achieving 86.45% F1 and 86.35% accuracy on a held-out test set.
  • The dataset, code, metadata, prompts, and fine-tuned checkpoint are made publicly available under CC-BY-4.0 and MIT licenses.

Key Concepts Mentioned

  • GitHub
  • NAICS
  • North American Industry Classification System
  • AI models
  • labeling
  • dataset
  • precision

Claims Made by the Document

  • The NAICS-GH corpus accurately labels GitHub repositories with industry sectors.
  • The labeling pipeline is effective and reproduces the candidate set to within 0.03 percent.

Evidence Used

  • The study presents a pipeline that generates high-confidence labels for 6588 repositories.
  • The precision of the released labels on a human-validated sample is reported as 96.98%.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.