--- dataset_info: - config_name: bm25 features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoBIRCOArguAna num_bytes: 376097 num_examples: 98 - name: NanoBIRCOClinicalTrial num_bytes: 75683 num_examples: 50 - name: NanoBIRCODorisMae num_bytes: 70510 num_examples: 60 - name: NanoBIRCORelic num_bytes: 128362 num_examples: 100 - name: NanoBIRCOWTB num_bytes: 154452 num_examples: 100 download_size: 164516 dataset_size: 805104 - 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: 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 configs: - config_name: bm25 data_files: - split: NanoBIRCOArguAna path: bm25/NanoBIRCOArguAna-* - split: NanoBIRCOClinicalTrial path: bm25/NanoBIRCOClinicalTrial-* - split: NanoBIRCODorisMae path: bm25/NanoBIRCODorisMae-* - split: NanoBIRCORelic path: bm25/NanoBIRCORelic-* - split: NanoBIRCOWTB path: bm25/NanoBIRCOWTB-* - config_name: corpus data_files: - split: NanoBIRCOArguAna path: corpus/NanoBIRCOArguAna-* - split: NanoBIRCOClinicalTrial path: corpus/NanoBIRCOClinicalTrial-* - split: NanoBIRCODorisMae path: corpus/NanoBIRCODorisMae-* - split: NanoBIRCORelic path: corpus/NanoBIRCORelic-* - split: NanoBIRCOWTB path: corpus/NanoBIRCOWTB-* - config_name: qrels data_files: - split: NanoBIRCOArguAna path: qrels/NanoBIRCOArguAna-* - split: NanoBIRCOClinicalTrial path: qrels/NanoBIRCOClinicalTrial-* - split: NanoBIRCODorisMae path: qrels/NanoBIRCODorisMae-* - split: NanoBIRCORelic path: qrels/NanoBIRCORelic-* - split: NanoBIRCOWTB path: qrels/NanoBIRCOWTB-* - config_name: queries data_files: - split: NanoBIRCOArguAna path: queries/NanoBIRCOArguAna-* - split: NanoBIRCOClinicalTrial path: queries/NanoBIRCOClinicalTrial-* - split: NanoBIRCODorisMae path: queries/NanoBIRCODorisMae-* - split: NanoBIRCORelic path: queries/NanoBIRCORelic-* - split: NanoBIRCOWTB path: queries/NanoBIRCOWTB-* default: true --- # NanoBIRCO This dataset is a Nano-style retrieval dataset. Nano-series evaluation can be run easily with [HAKARI-Bench](https://github.com/hotchpotch/hakari-bench). NanoBIRCO is derived from BIRCO. It follows the Hugging Face Datasets layout convention used by [sentence-transformers/NanoBEIR-en](https://huggingface.co/datasets/sentence-transformers/NanoBEIR-en): each Nano split has separate `corpus`, `queries`, and `qrels` tables, and BM25 candidates are provided separately in a `bm25` table. This layout follows the NanoBEIR-style evaluation approach summarized in [NanoBEIR](https://huggingface.co/blog/sionic-ai/eval-sionic-nano-beir). 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. ## 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 ## Data Layout This dataset uses four 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` Each config uses the same Nano split names. If the actual generated dataset uses a different schema, config name, path layout, or field name, revise this section before publishing the README. ## Construction Steps This dataset was built as follows. If the actual generation procedure differs, revise this section before publishing the README. 1. Use BIRCO as the upstream benchmark or dataset family. 2. Load the source datasets recorded in `manifest.json` and per-split metadata files. 3. Use the evaluation splits selected by `scripts/create_mteb_nano_series_local.py` as the source evaluation split policy. 4. Create one Nano split for each selected source retrieval task. 5. Keep up to 200 eligible queries per Nano split. 6. Include qrels-positive documents for the selected queries. For graded qrels, only rows with `score > 0` are treated as positive. 7. Fill the corpus from source corpus order up to 10000 documents. 8. Remove exact duplicate document text within each split. If a removed duplicate was referenced by qrels, rewrite qrels to the kept document id when the generator records that policy. 9. Store document title and body as a single `text` field when the source provides both. 10. Generate BM25 top-100 candidates with the tokenization policy recorded per split. 11. If a qrels-positive document is missing from the raw BM25 result, insert it into the final `bm25` candidate list by replacing a tail non-positive candidate. Some source queries had more positives than the BM25 candidate cap. For those cases, the Nano qrels keep the first available positive document so BM25 top-k remains comparable; 44 selected queries were capped and 4851 source positive qrels were omitted from this Nano slice. ## BM25 Subset Policy The `bm25` config is a candidate subset for first-stage retrieval and reranking. It is not a separate source dataset. Each row contains one query id and a ranked list of corpus ids. BM25 candidates are generated from the selected corpus for each split. The configured candidate cap is top-100. When a qrels-positive document is not present in the raw BM25 result, the missing positive is forced into the final candidate list by replacing a tail candidate that is not positive for that query. Candidate ids are kept unique after replacement. ## Split Mapping | Nano split | Source task | Source dataset | Queries | Corpus | Qrels | |---|---|---|---:|---:|---:| | `NanoBIRCOArguAna` | `BIRCO-ArguAna` | `mteb/BIRCO-ArguAna-Test` | 98 | 3081 | 98 | | `NanoBIRCOClinicalTrial` | `BIRCO-ClinicalTrial` | `mteb/BIRCO-ClinicalTrial-Test` | 50 | 3375 | 1042 | | `NanoBIRCODorisMae` | `BIRCO-DorisMae` | `mteb/BIRCO-DorisMae-Test` | 60 | 5544 | 1569 | | `NanoBIRCORelic` | `BIRCO-Relic` | `mteb/BIRCO-Relic-Test` | 100 | 5023 | 100 | | `NanoBIRCOWTB` | `BIRCO-WTB` | `mteb/BIRCO-WTB-Test` | 100 | 1766 | 100 | ## BM25 nDCG@10 `nDCG@10` is computed from the included BM25 ranking against the included qrels. Tokenizer policy summary: `stemmer:en`. | Nano split | Tokenizer | Forced BM25 positives | BM25 nDCG@10 | |---|---|---:|---:| | `NanoBIRCOArguAna` | `stemmer:en` | 6 | 0.4051 | | `NanoBIRCOClinicalTrial` | `stemmer:en` | 819 | 0.1194 | | `NanoBIRCODorisMae` | `stemmer:en` | 942 | 0.2469 | | `NanoBIRCORelic` | `stemmer:en` | 62 | 0.0633 | | `NanoBIRCOWTB` | `stemmer:en` | 65 | 0.0751 | ## Skipped Tasks No source tasks were skipped. ## License NanoBIRCO is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets and benchmarks.