--- configs: - config_name: corpus data_files: - split: miracl_ru path: corpus/miracl_ru-00000-of-00001.parquet - split: ria_news path: corpus/ria_news-00000-of-00001.parquet - split: ru_bq path: corpus/ru_bq-00000-of-00001.parquet - config_name: queries data_files: - split: miracl_ru path: queries/miracl_ru-00000-of-00001.parquet - split: ria_news path: queries/ria_news-00000-of-00001.parquet - split: ru_bq path: queries/ru_bq-00000-of-00001.parquet default: true - config_name: qrels data_files: - split: miracl_ru path: qrels/miracl_ru-00000-of-00001.parquet - split: ria_news path: qrels/ria_news-00000-of-00001.parquet - split: ru_bq path: qrels/ru_bq-00000-of-00001.parquet - config_name: bm25 data_files: - split: miracl_ru path: bm25/miracl_ru-00000-of-00001.parquet - split: ria_news path: bm25/ria_news-00000-of-00001.parquet - split: ru_bq path: bm25/ru_bq-00000-of-00001.parquet - config_name: harrier_oss_v1_270m data_files: - split: miracl_ru path: harrier_oss_v1_270m/miracl_ru-00000-of-00001.parquet - split: ria_news path: harrier_oss_v1_270m/ria_news-00000-of-00001.parquet - split: ru_bq path: harrier_oss_v1_270m/ru_bq-00000-of-00001.parquet - config_name: reranking_hybrid data_files: - split: miracl_ru path: reranking_hybrid/miracl_ru-00000-of-00001.parquet - split: ria_news path: reranking_hybrid/ria_news-00000-of-00001.parquet - split: ru_bq path: reranking_hybrid/ru_bq-00000-of-00001.parquet tags: - information-retrieval - retrieval - nano - bm25 - hakari-bench - dense-retrieval - reranking dataset_info: - config_name: bm25 features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: miracl_ru num_bytes: 1309531 num_examples: 200 - name: ria_news num_bytes: 875282 num_examples: 200 - name: ru_bq num_bytes: 794539 num_examples: 200 download_size: 2983442 dataset_size: 2979352 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: miracl_ru num_bytes: 9516564 num_examples: 10000 - name: ria_news num_bytes: 20907129 num_examples: 10000 - name: ru_bq num_bytes: 8863270 num_examples: 10000 download_size: 19550800 dataset_size: 39286963 - config_name: harrier_oss_v1_270m features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: miracl_ru num_bytes: 1316756 num_examples: 200 - name: ria_news num_bytes: 874810 num_examples: 200 - name: ru_bq num_bytes: 796197 num_examples: 200 download_size: 2991933 dataset_size: 2987763 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: miracl_ru num_bytes: 11397 num_examples: 579 - name: ria_news num_bytes: 3438 num_examples: 200 - name: ru_bq num_bytes: 5181 num_examples: 334 download_size: 16487 dataset_size: 20016 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: miracl_ru num_bytes: 18927 num_examples: 200 - name: ria_news num_bytes: 25008 num_examples: 200 - name: ru_bq num_bytes: 21344 num_examples: 200 download_size: 42640 dataset_size: 65279 - config_name: reranking_hybrid features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: miracl_ru num_bytes: 262804 num_examples: 200 - name: ria_news num_bytes: 177127 num_examples: 200 - name: ru_bq num_bytes: 161386 num_examples: 200 download_size: 605175 dataset_size: 601317 --- # NanoRuMTEB This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). NanoRuMTEB is a compact Russian retrieval benchmark assembled from ruMTEB retrieval tasks. It includes Russian MIRACL, RIA News retrieval, and RuBQ retrieval; RuSciBench retrieval tasks are grouped separately under NanoMTEB-Misc. ## Usage ```python from datasets import load_dataset dataset_id = "hakari-bench/NanoRuMTEB" split = "miracl_ru" 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 | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | miracl_ru | 200 | 10000 | 579 | 45.4 | 41.0 | 53.2 | 517.3 | 404.0 | 685.2 | | ria_news | 200 | 10000 | 200 | 62.0 | 65.0 | 68.0 | 1145.3 | 1169.5 | 1523.0 | | ru_bq | 200 | 10000 | 334 | 52.2 | 48.0 | 60.2 | 484.5 | 380.0 | 645.0 | ## 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 | - | 70.89 | 87.18 | 78.95 | 91.83 | 95.94 | 98.73 | - | 5 | | miracl_ru | stemmer@russian | 51.54 | 79.38 | 66.46 | 89.09 | 95.78 | 99.26 | 100-101 | 1 | | ria_news | stemmer@russian | 91.35 | 94.78 | 92.72 | 97.50 | 97.50 | 99.00 | 100-101 | 2 | | ru_bq | stemmer@russian | 69.79 | 87.39 | 77.67 | 88.92 | 94.54 | 97.92 | 100-101 | 2 | ## Hybrid Safeguard Summary - Safeguard positives: 5 - Rows limited by corpus size: 0 - Metadata file: `reranking_hybrid_metadata.json` ## Source Links - [mteb/MIRACLRetrievalHardNegatives](https://huggingface.co/datasets/mteb/MIRACLRetrievalHardNegatives) - [mteb/RiaNewsRetrieval_test_top_250_only_w_correct-v2](https://huggingface.co/datasets/mteb/RiaNewsRetrieval_test_top_250_only_w_correct-v2) - [ai-forever/rubq-retrieval](https://huggingface.co/datasets/ai-forever/rubq-retrieval) ## License NanoRuMTEB is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream MTEB task sources and their original datasets.