Datasets:
File size: 7,762 Bytes
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"name": "OTel-Reranker",
"description": "OTel-Reranker is a pointwise reranking dataset for training cross-encoder models that score the relevance of a passage to a given query in the telecommunications domain.",
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"rai:dataLimitations": "The dataset covers telecommunications standards and specifications only. It is English-only and reflects standards available at the time of collection. Performance of models trained on this data should not be generalized beyond the telecom domain.",
"rai:dataBiases": "The dataset may overrepresent certain telecom subdomains (e.g., 3GPP, O-RAN) relative to others due to source availability. Widely documented standards topics are better represented than niche or emerging telecom areas.",
"rai:personalSensitiveInformation": "The dataset does not contain personal or sensitive information. All source material is drawn from publicly available technical standards, specifications, and academic publications.",
"rai:dataUseCases": "Training, Fine Tuning",
"rai:dataSocialImpact": "The dataset supports open and reproducible telecom AI research, reducing barriers to domain-specific model development. The primary risk is potential misuse of domain-specialized models to generate misleading telecom documentation.",
"rai:hasSyntheticData": "The training data includes synthetically generated question-answer pairs derived from source documents through automated pipelines, including LLM-assisted answer recreation and fact-grounded verification.",
"rai:dataCollection": "Source datasets and collection methodology: the OTel corpus is derived from publicly available telecom sources, including 3GPP technical specifications, GSMA Permanent Reference Documents (PRDs) and Discover portal materials, O-RAN Alliance specifications across working groups 1, 2, 4, 5, 6, 7, 8, 9, and 10, IETF Request for Comments (RFC) series, telecom academic papers from arXiv, telecom Wikipedia articles, telecom-related Common Crawl pages, and industry whitepapers. The OTel datasets release only derived question-answer pairs and contrastive examples, not the raw source documents. Data was collected by 100+ domain experts across industry and academia and processed through a six-phase enrichment and four-stage cleaning pipeline.",
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"name": "OTel Data Curation Pipeline",
"description": "A six-phase enrichment and four-stage cleaning pipeline curated by 100+ domain experts from AT&T, AMD, GSMA, Microsoft, RelationalAI, MLCommons, Mantis NLP, Essential AI, Yale University, Khalifa University, University of Leeds, The University of Texas at Dallas, Purdue University, and NetoAI. The raw corpus of approximately 1.1M samples was cleaned to approximately 326K samples through heuristic filtering, cross-encoder reranking filtering, embedding-based semantic filtering, and deduplication."
}
} |