NanoMTEB-v2 is a compact English MTEB-family retrieval benchmark. It includes BEIR-style and MTEB retrieval tasks such as ArguAna, Climate-FEVER, CQADupStack, FEVER, FiQA, HotpotQA, SCIDOCS, Touché, and TREC-COVID in the Nano query-corpus-qrels format.

Usage

from datasets import load_dataset

dataset_id = "hakari-bench/NanoMTEB-v2"
split = "argu_ana"

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
argu_ana	199	8626	199	1199.8	1002.0	1359.5	1029.6	927.0	1263.8
climate_fever	200	10000	621	115.0	109.0	141.5	1115.9	921.5	1610.2
cqadupstack_gaming	200	10000	415	47.6	45.0	58.2	481.1	387.0	574.0
cqadupstack_unix	200	10000	486	49.2	46.0	60.2	969.1	602.5	1021.0
fever	200	10000	229	50.6	46.0	59.2	566.0	373.0	774.0
fi_qa2018	200	10000	534	61.7	63.0	76.0	780.4	531.0	953.2
hotpot_qa	200	10000	400	95.8	89.5	112.0	421.2	378.0	552.0
scidocs	200	10000	986	69.8	67.0	80.2	1202.7	1102.0	1415.0
touche2020_v3	49	10000	1704	43.4	40.0	57.0	2386.2	1198.5	3502.0
treccovid	50	10000	4584	69.2	64.5	76.8	1326.6	1365.0	1818.0

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	-	50.28	58.63	56.98	72.80	78.98	80.61	-	72
argu_ana	english_porter_stop	34.64	40.92	37.75	95.48	95.98	98.99	100-101	2
climate_fever	english_porter_stop	17.19	32.76	27.94	53.88	69.39	71.09	100-101	17
cqadupstack_gaming	english_porter_stop	50.73	63.75	59.70	82.93	88.17	91.33	100-101	10
cqadupstack_unix	english_porter_stop	40.01	50.95	46.58	66.71	78.57	83.84	100-101	14
fever	english_porter_stop	88.93	96.52	94.50	99.25	98.00	99.25	100	0
fi_qa2018	english_porter_stop	37.99	54.94	52.58	72.68	84.25	83.47	100-101	17
hotpot_qa	english_porter_stop	89.50	89.04	91.56	97.25	97.00	99.75	100	0
scidocs	english_porter_stop	20.67	27.57	25.65	42.32	57.49	53.39	100-101	12
touche2020_v3	english_porter_stop	84.24	88.10	88.35	93.44	93.42	95.27	100	0
treccovid	english_porter_stop	38.93	41.77	45.21	24.04	27.56	29.75	100	0

Hybrid Safeguard Summary

Safeguard positives: 72
Rows limited by corpus size: 0
Metadata file: reranking_hybrid_metadata.json

Source Links

License

NanoMTEB-v2 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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