Hyukkyu/train-searchqa

SearchQA — Training, unified schema

A normalised copy of the dataset behind the mteb task SearchQA, a retrieval training set built from sentence-transformers/embedding-training-data. Same queries, documents and relevance judgements as the benchmark evaluates — reshaped into one strict schema shared by every dataset in this collection.

Source	sentence-transformers/embedding-training-data @ 03987af07011 (the revision pinned in mteb)
Domain · languages	Jeopardy QA / web snippets · eng
Queries / documents / qrels	117,220 / 578,015 / 581,652
Qrels per query	min 1 · mean 4.962 · max 10
Score values	1 ×581,652
Layout	queries · corpus · qrels · hard-negatives · teacher-scores, split train
Splits	corpus: train · hard-negatives: train · qrels: train · queries: train · teacher-scores: train
Hard negatives	none yet — config present with 0 rows
Teacher scores	none yet — config present with 0 rows
Reading empty configs	datasets cannot return a 0-example split (load_dataset raises "corresponds to no data"); until rows exist, read the Parquet directly with pyarrow/polars/pandas. The schema is declared in the file and in configs: above
Ids	sha1(text)[:20]; identical texts collapse to one document (3,637 collapsed)
Pair recovery	581,652 of 581,652 source pairs reconstructed from queries × qrels × corpus with byte-equal text
License	unspecified

Schema

config	columns	rules
queries	id: string, text: string	ids unique and non-empty; every query has ≥ 1 qrel
corpus	id: string, title: string, text: string	title is always present ("" when the source has none)
qrels	query-id: string, corpus-id: string, score: int32	referential integrity to both tables; no duplicate pairs; no floats
hard-negatives	query-id: string, corpus-id: string, rank: int32, source: string	one row per mined negative; (query-id, corpus-id, source) unique; never a labelled positive of the same query
teacher-scores	query-id: string, corpus-id: string, teacher: string, score: float32	one row per scored pair (positives included); a row means scored — never a placeholder

Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is enforced by a validator before publishing; provenance.json records the source file hashes, what changed, and the output file hashes.

What changed from the source

• byte-preserved all text — no whitespace, newline, or control-character normalisation
• kept 1 documents whose title and text are both empty — they are in the source and the benchmark's corpus

Load it

from datasets import load_dataset
queries = load_dataset("Hyukkyu/train-searchqa", "queries", split="train")
corpus  = load_dataset("Hyukkyu/train-searchqa", "corpus", split="train")
qrels   = load_dataset("Hyukkyu/train-searchqa", "qrels", split="train")

License and attribution

The data is redistributed under the source's terms — unspecified. All credit belongs to the original authors; see the source repository and the references in mteb's task metadata (https://huggingface.co/datasets/sentence-transformers/embedding-training-data). This repository is an independent repackaging and is not affiliated with the RTEB or MTEB maintainers.

No license declared upstream. Neither the source repo nor its origin states redistribution terms. This copy is published for reproducibility of the RTEB benchmark only; check with the original authors before any other use.