--- dataset_info: - config_name: bm25 features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoBIRCOArguAna num_bytes: 1870651 num_examples: 98 - name: NanoBIRCOClinicalTrial num_bytes: 375683 num_examples: 50 - name: NanoBIRCODorisMae num_bytes: 349544 num_examples: 60 - name: NanoBIRCORelic num_bytes: 637332 num_examples: 100 - name: NanoBIRCOWTB num_bytes: 763230 num_examples: 100 download_size: 4001233 dataset_size: 3996440 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoBIRCOArguAna num_bytes: 3661676 num_examples: 3081 - name: NanoBIRCOClinicalTrial num_bytes: 4029445 num_examples: 3375 - name: NanoBIRCODorisMae num_bytes: 6851909 num_examples: 5544 - name: NanoBIRCORelic num_bytes: 2483080 num_examples: 5023 - name: NanoBIRCOWTB num_bytes: 1968736 num_examples: 1766 download_size: 9995334 dataset_size: 18994846 - config_name: harrier_oss_v1_270m features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoBIRCOArguAna num_bytes: 1846893 num_examples: 98 - name: NanoBIRCOClinicalTrial num_bytes: 375683 num_examples: 50 - name: NanoBIRCODorisMae num_bytes: 349899 num_examples: 60 - name: NanoBIRCORelic num_bytes: 638600 num_examples: 100 - name: NanoBIRCOWTB num_bytes: 762147 num_examples: 100 download_size: 3978069 dataset_size: 3973222 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: NanoBIRCOArguAna num_bytes: 6906 num_examples: 98 - name: NanoBIRCOClinicalTrial num_bytes: 25649 num_examples: 1042 - name: NanoBIRCODorisMae num_bytes: 30824 num_examples: 1569 - name: NanoBIRCORelic num_bytes: 2318 num_examples: 100 - name: NanoBIRCOWTB num_bytes: 3262 num_examples: 100 download_size: 34194 dataset_size: 68959 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoBIRCOArguAna num_bytes: 114584 num_examples: 98 - name: NanoBIRCOClinicalTrial num_bytes: 25534 num_examples: 50 - name: NanoBIRCODorisMae num_bytes: 60464 num_examples: 60 - name: NanoBIRCORelic num_bytes: 103108 num_examples: 100 - name: NanoBIRCOWTB num_bytes: 83407 num_examples: 100 download_size: 255948 dataset_size: 387097 - config_name: reranking_hybrid features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoBIRCOArguAna num_bytes: 373151 num_examples: 98 - name: NanoBIRCOClinicalTrial num_bytes: 75698 num_examples: 50 - name: NanoBIRCODorisMae num_bytes: 70586 num_examples: 60 - name: NanoBIRCORelic num_bytes: 129194 num_examples: 100 - name: NanoBIRCOWTB num_bytes: 154507 num_examples: 100 download_size: 807492 dataset_size: 803136 configs: - config_name: corpus data_files: - split: NanoBIRCOArguAna path: corpus/NanoBIRCOArguAna-00000-of-00001.parquet - split: NanoBIRCOClinicalTrial path: corpus/NanoBIRCOClinicalTrial-00000-of-00001.parquet - split: NanoBIRCODorisMae path: corpus/NanoBIRCODorisMae-00000-of-00001.parquet - split: NanoBIRCORelic path: corpus/NanoBIRCORelic-00000-of-00001.parquet - split: NanoBIRCOWTB path: corpus/NanoBIRCOWTB-00000-of-00001.parquet - config_name: queries data_files: - split: NanoBIRCOArguAna path: queries/NanoBIRCOArguAna-00000-of-00001.parquet - split: NanoBIRCOClinicalTrial path: queries/NanoBIRCOClinicalTrial-00000-of-00001.parquet - split: NanoBIRCODorisMae path: queries/NanoBIRCODorisMae-00000-of-00001.parquet - split: NanoBIRCORelic path: queries/NanoBIRCORelic-00000-of-00001.parquet - split: NanoBIRCOWTB path: queries/NanoBIRCOWTB-00000-of-00001.parquet default: true - config_name: qrels data_files: - split: NanoBIRCOArguAna path: qrels/NanoBIRCOArguAna-00000-of-00001.parquet - split: NanoBIRCOClinicalTrial path: qrels/NanoBIRCOClinicalTrial-00000-of-00001.parquet - split: NanoBIRCODorisMae path: qrels/NanoBIRCODorisMae-00000-of-00001.parquet - split: NanoBIRCORelic path: qrels/NanoBIRCORelic-00000-of-00001.parquet - split: NanoBIRCOWTB path: qrels/NanoBIRCOWTB-00000-of-00001.parquet - config_name: bm25 data_files: - split: NanoBIRCOArguAna path: bm25/NanoBIRCOArguAna-00000-of-00001.parquet - split: NanoBIRCOClinicalTrial path: bm25/NanoBIRCOClinicalTrial-00000-of-00001.parquet - split: NanoBIRCODorisMae path: bm25/NanoBIRCODorisMae-00000-of-00001.parquet - split: NanoBIRCORelic path: bm25/NanoBIRCORelic-00000-of-00001.parquet - split: NanoBIRCOWTB path: bm25/NanoBIRCOWTB-00000-of-00001.parquet - config_name: harrier_oss_v1_270m data_files: - split: NanoBIRCOArguAna path: harrier_oss_v1_270m/NanoBIRCOArguAna-00000-of-00001.parquet - split: NanoBIRCOClinicalTrial path: harrier_oss_v1_270m/NanoBIRCOClinicalTrial-00000-of-00001.parquet - split: NanoBIRCODorisMae path: harrier_oss_v1_270m/NanoBIRCODorisMae-00000-of-00001.parquet - split: NanoBIRCORelic path: harrier_oss_v1_270m/NanoBIRCORelic-00000-of-00001.parquet - split: NanoBIRCOWTB path: harrier_oss_v1_270m/NanoBIRCOWTB-00000-of-00001.parquet - config_name: reranking_hybrid data_files: - split: NanoBIRCOArguAna path: reranking_hybrid/NanoBIRCOArguAna-00000-of-00001.parquet - split: NanoBIRCOClinicalTrial path: reranking_hybrid/NanoBIRCOClinicalTrial-00000-of-00001.parquet - split: NanoBIRCODorisMae path: reranking_hybrid/NanoBIRCODorisMae-00000-of-00001.parquet - split: NanoBIRCORelic path: reranking_hybrid/NanoBIRCORelic-00000-of-00001.parquet - split: NanoBIRCOWTB path: reranking_hybrid/NanoBIRCOWTB-00000-of-00001.parquet tags: - information-retrieval - retrieval - nano - bm25 - dense-retrieval - reranking - hakari-bench --- # NanoBIRCO This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). NanoBIRCO contains 5 Nano retrieval splits derived from BIRCO. Each split keeps up to 200 eligible queries and up to 10000 corpus documents, with exact duplicate query and document text removed where the generator records that policy. ## Usage ```python from datasets import load_dataset dataset_id = "hakari-bench/NanoBIRCO" split = "NanoBIRCOArguAna" 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 | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | NanoBIRCOArguAna | 98 | 3081 | 98 | 1124.0 | 1101.5 | 1410.2 | 1140.1 | 1082.0 | 1445.0 | | NanoBIRCOClinicalTrial | 50 | 3375 | 1042 | 497.0 | 438.5 | 558.8 | 1174.3 | 1421.0 | 1579.0 | | NanoBIRCODorisMae | 60 | 5544 | 1569 | 995.5 | 993.5 | 1095.5 | 1220.3 | 1208.5 | 1431.0 | | NanoBIRCORelic | 100 | 5023 | 100 | 1016.3 | 1054.0 | 1144.5 | 477.3 | 438.0 | 627.0 | | NanoBIRCOWTB | 100 | 1766 | 100 | 811.3 | 788.5 | 954.8 | 1091.2 | 1108.0 | 1269.8 | ## 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 | - | 26.93 | 29.59 | 31.11 | 63.53 | 70.96 | 73.38 | - | 66 | | NanoBIRCOArguAna | english_porter_stop | 42.93 | 50.62 | 49.32 | 97.96 | 97.96 | 100.00 | 100 | 0 | | NanoBIRCOClinicalTrial | english_porter_stop | 13.22 | 21.52 | 19.59 | 31.03 | 48.52 | 45.61 | 100-101 | 1 | | NanoBIRCODorisMae | english_porter_stop | 38.66 | 41.40 | 40.12 | 70.68 | 72.34 | 80.30 | 100-101 | 6 | | NanoBIRCORelic | english_porter_stop | 13.14 | 7.25 | 12.76 | 59.00 | 66.00 | 69.00 | 100-101 | 31 | | NanoBIRCOWTB | english_porter_stop | 26.69 | 27.14 | 33.76 | 59.00 | 70.00 | 72.00 | 100-101 | 28 | ## Hybrid Safeguard Summary - Safeguard positives: 66 - Rows limited by corpus size: 0 - Metadata file: `reranking_hybrid_metadata.json` ## Source Links - Source benchmark: `BIRCO` - Source benchmark repository: https://github.com/embeddings-benchmark/mteb - `mteb/BIRCO-ArguAna-Test`: https://huggingface.co/datasets/mteb/BIRCO-ArguAna-Test - `mteb/BIRCO-ClinicalTrial-Test`: https://huggingface.co/datasets/mteb/BIRCO-ClinicalTrial-Test - `mteb/BIRCO-DorisMae-Test`: https://huggingface.co/datasets/mteb/BIRCO-DorisMae-Test - `mteb/BIRCO-Relic-Test`: https://huggingface.co/datasets/mteb/BIRCO-Relic-Test - `mteb/BIRCO-WTB-Test`: https://huggingface.co/datasets/mteb/BIRCO-WTB-Test ## License NanoBIRCO is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets and benchmarks.