NanoMTEB-Spanish is a compact Spanish retrieval benchmark aligned with MTEB-style retrieval task families. It includes Spanish Mintaka, MIRACL, passage retrieval, and cross-lingual XPQA retrieval splits.

Usage

from datasets import load_dataset

dataset_id = "hakari-bench/NanoMTEB-Spanish"
split = "mintaka_es"

queries = load_dataset(dataset_id, "queries", split=split)
corpus = load_dataset(dataset_id, "corpus", split=split)
qrels = load_dataset(dataset_id, "qrels", split=split)
reranking_candidates = load_dataset(dataset_id, "reranking_hybrid", split=split)

Data Layout

This dataset uses six Hugging Face Datasets configs:

corpus: documents with _id and text
queries: queries with _id and text
qrels: positive relevance labels with query-id and corpus-id
bm25: BM25 candidate lists with query-id and corpus-ids
harrier_oss_v1_270m: dense candidate lists from microsoft/harrier-oss-v1-270m
reranking_hybrid: RRF candidate lists built from bm25 and harrier_oss_v1_270m

Each config has the same Nano split names.

Candidate Construction

bm25: local BM25 top-500 with automatic language-aware tokenization. The resolved tokenizer is shown in the Candidate Quality table, for example wordseg@ja.
harrier_oss_v1_270m: dense top-500 from microsoft/harrier-oss-v1-270m. In tables this is shown as Dense; Dense means microsoft/harrier-oss-v1-270m with the web_search_query prompt for queries and cosine similarity over normalized embeddings.
reranking_hybrid: RRF over bm25 and harrier_oss_v1_270m using rrf_k=100, keeping the RRF top-100.

Safeguard means rank 101 is appended only when RRF top-100 contains no qrels-positive document.

Split Statistics

Length statistics are character counts computed with len(str(text)).

Nano split	Queries	Corpus	Qrels	Query chars avg	Query chars p50	Query chars p75	Doc chars avg	Doc chars p50	Doc chars p75
mintaka_es	200	1693	200	66.9	65.0	80.0	14.3	13.0	17.0
miracl_es	200	10000	934	47.6	47.0	55.0	555.0	469.0	730.0
spanish_passage_s2_p	167	7501	996	67.6	65.0	83.0	2710.8	2164.0	3619.0
spanish_passage_s2_s	167	250	1228	67.6	65.0	83.0	442.4	305.0	538.8
xpqa_eng_spa	200	1936	491	45.2	40.0	57.0	123.4	94.0	153.0
xpqa_spa_eng	200	1941	469	47.4	41.0	57.0	68.3	67.0	81.0
xpqa_spa_spa	200	1941	488	45.2	40.0	57.0	68.3	67.0	81.0

Candidate Quality

nDCG@10 and Recall@100 are computed from the included candidate rankings against the included qrels, then reported as 0-100 scores such as 52.45. Recall@100 uses only the top 100 candidates; an optional rank-101 safeguard positive is not counted in Recall@100.

Dense means microsoft/harrier-oss-v1-270m with the web_search_query prompt and cosine similarity.

Nano split	BM25 tokenizer	BM25 nDCG@10	Dense nDCG@10	Hybrid nDCG@10	BM25 Recall@100	Dense Recall@100	Hybrid Recall@100	Hybrid candidates	Safeguard positives
Mean	-	36.79	51.22	43.96	63.14	85.15	83.07	-	181
mintaka_es	stemmer@spanish	25.02	36.14	27.21	35.00	75.00	62.00	100-101	76
miracl_es	stemmer@spanish	56.20	74.81	70.42	97.62	93.23	99.94	100	0
spanish_passage_s2_p	stemmer@spanish	51.29	47.19	62.20	91.97	86.63	97.31	100-101	1
spanish_passage_s2_s	stemmer@spanish	54.58	63.98	63.33	94.44	98.39	99.32	100	0
xpqa_eng_spa	stemmer@spanish	9.86	31.04	14.28	24.96	72.56	62.67	100-101	49
xpqa_spa_eng	english_porter_stop	12.27	48.72	14.44	22.07	84.09	75.51	100-101	35
xpqa_spa_spa	stemmer@spanish	48.29	56.67	55.82	75.90	86.17	84.77	100-101	20

Hybrid Safeguard Summary

Safeguard positives: 181
Rows limited by corpus size: 0
Metadata file: reranking_hybrid_metadata.json

Source Links

License

NanoMTEB-Spanish is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream MTEB task sources and their original datasets.

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