StackOverflow-QA (aka StackQA) is a retrieval benchmark constructed from Stack Overflow question/answer posts where both queries and candidate documents can contain long mixed content of natural language and code. It is provided in a retrieval format (queries, corpus, qrels/scores) and intended for code+text information retrieval evaluations (e.g., dense single-vector retrieval). The Hugging Face mirror (mteb/stackoverflow-qa) shows splits and typical fields (query-id, corpus-id, score) and sizes: ~15.9k default rows (train: ~14k, test: ~1.99k) with corpus/queries subsets (~19.9k). This dataset has been used in recent code-IR benchmarks (e.g., CoIR) and evaluated with metrics such as nDCG@10 for single-vector retrieval.
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