--- configs: - config_name: bm25 data_files: - split: ar path: bm25/ar-* - split: bn path: bm25/bn-* - split: de path: bm25/de-* - split: en path: bm25/en-* - split: es path: bm25/es-* - split: fa path: bm25/fa-* - split: fi path: bm25/fi-* - split: fr path: bm25/fr-* - split: hi path: bm25/hi-* - split: id path: bm25/id-* - split: ja path: bm25/ja-* - split: ko path: bm25/ko-* - split: ru path: bm25/ru-* - split: sw path: bm25/sw-* - split: te path: bm25/te-* - split: th path: bm25/th-* - split: yo path: bm25/yo-* - split: zh path: bm25/zh-* - config_name: corpus data_files: - split: ar path: corpus/ar-* - split: bn path: corpus/bn-* - split: de path: corpus/de-* - split: en path: corpus/en-* - split: es path: corpus/es-* - split: fa path: corpus/fa-* - split: fi path: corpus/fi-* - split: fr path: corpus/fr-* - split: hi path: corpus/hi-* - split: id path: corpus/id-* - split: ja path: corpus/ja-* - split: ko path: corpus/ko-* - split: ru path: corpus/ru-* - split: sw path: corpus/sw-* - split: te path: corpus/te-* - split: th path: corpus/th-* - split: yo path: corpus/yo-* - split: zh path: corpus/zh-* - config_name: qrels data_files: - split: ar path: qrels/ar-* - split: bn path: qrels/bn-* - split: de path: qrels/de-* - split: en path: qrels/en-* - split: es path: qrels/es-* - split: fa path: qrels/fa-* - split: fi path: qrels/fi-* - split: fr path: qrels/fr-* - split: hi path: qrels/hi-* - split: id path: qrels/id-* - split: ja path: qrels/ja-* - split: ko path: qrels/ko-* - split: ru path: qrels/ru-* - split: sw path: qrels/sw-* - split: te path: qrels/te-* - split: th path: qrels/th-* - split: yo path: qrels/yo-* - split: zh path: qrels/zh-* - config_name: queries data_files: - split: ar path: queries/ar-* - split: bn path: queries/bn-* - split: de path: queries/de-* - split: en path: queries/en-* - split: es path: queries/es-* - split: fa path: queries/fa-* - split: fi path: queries/fi-* - split: fr path: queries/fr-* - split: hi path: queries/hi-* - split: id path: queries/id-* - split: ja path: queries/ja-* - split: ko path: queries/ko-* - split: ru path: queries/ru-* - split: sw path: queries/sw-* - split: te path: queries/te-* - split: th path: queries/th-* - split: yo path: queries/yo-* - split: zh path: queries/zh-* license: other dataset_info: - config_name: bm25 features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: ar num_bytes: 248341 num_examples: 200 - name: bn num_bytes: 233471 num_examples: 200 - name: de num_bytes: 256575 num_examples: 200 - name: en num_bytes: 269340 num_examples: 200 - name: es num_bytes: 255281 num_examples: 200 - name: fa num_bytes: 249507 num_examples: 200 - name: fi num_bytes: 242201 num_examples: 200 - name: fr num_bytes: 259969 num_examples: 200 - name: hi num_bytes: 240592 num_examples: 200 - name: id num_bytes: 232601 num_examples: 200 - name: ja num_bytes: 251987 num_examples: 200 - name: ko num_bytes: 241907 num_examples: 200 - name: ru num_bytes: 257759 num_examples: 200 - name: sw num_bytes: 222259 num_examples: 200 - name: te num_bytes: 95444 num_examples: 84 - name: th num_bytes: 238733 num_examples: 200 - name: yo num_bytes: 132768 num_examples: 119 - name: zh num_bytes: 247634 num_examples: 200 download_size: 740288 dataset_size: 4176369 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: ar num_bytes: 2294341 num_examples: 1854 - name: bn num_bytes: 3347585 num_examples: 1731 - name: de num_bytes: 1149758 num_examples: 1748 - name: en num_bytes: 1290954 num_examples: 1657 - name: es num_bytes: 842327 num_examples: 1312 - name: fa num_bytes: 1678106 num_examples: 1858 - name: fi num_bytes: 1268275 num_examples: 1828 - name: fr num_bytes: 1052962 num_examples: 1777 - name: hi num_bytes: 2633668 num_examples: 1748 - name: id num_bytes: 1054118 num_examples: 1520 - name: ja num_bytes: 1584153 num_examples: 1846 - name: ko num_bytes: 1673878 num_examples: 2419 - name: ru num_bytes: 2483294 num_examples: 1727 - name: sw num_bytes: 877891 num_examples: 1600 - name: te num_bytes: 1555923 num_examples: 754 - name: th num_bytes: 3206876 num_examples: 1897 - name: yo num_bytes: 456903 num_examples: 921 - name: zh num_bytes: 882899 num_examples: 1700 download_size: 14460682 dataset_size: 29333911 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: ar num_bytes: 3754 num_examples: 200 - name: bn num_bytes: 3620 num_examples: 200 - name: de num_bytes: 5041 num_examples: 200 - name: en num_bytes: 4056 num_examples: 200 - name: es num_bytes: 5054 num_examples: 200 - name: fa num_bytes: 4925 num_examples: 200 - name: fi num_bytes: 3811 num_examples: 200 - name: fr num_bytes: 5075 num_examples: 200 - name: hi num_bytes: 4720 num_examples: 200 - name: id num_bytes: 3699 num_examples: 200 - name: ja num_bytes: 3839 num_examples: 200 - name: ko num_bytes: 3777 num_examples: 200 - name: ru num_bytes: 3864 num_examples: 200 - name: sw num_bytes: 3550 num_examples: 200 - name: te num_bytes: 1592 num_examples: 84 - name: th num_bytes: 3685 num_examples: 200 - name: yo num_bytes: 2600 num_examples: 119 - name: zh num_bytes: 4939 num_examples: 200 download_size: 79062 dataset_size: 71601 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: ar num_bytes: 13271 num_examples: 200 - name: bn num_bytes: 27235 num_examples: 200 - name: de num_bytes: 12670 num_examples: 200 - name: en num_bytes: 10242 num_examples: 200 - name: es num_bytes: 13604 num_examples: 200 - name: fa num_bytes: 17958 num_examples: 200 - name: fi num_bytes: 9987 num_examples: 200 - name: fr num_bytes: 12361 num_examples: 200 - name: hi num_bytes: 31659 num_examples: 200 - name: id num_bytes: 9919 num_examples: 200 - name: ja num_bytes: 12551 num_examples: 200 - name: ko num_bytes: 13045 num_examples: 200 - name: ru num_bytes: 18950 num_examples: 200 - name: sw num_bytes: 9853 num_examples: 200 - name: te num_bytes: 9661 num_examples: 84 - name: th num_bytes: 27231 num_examples: 200 - name: yo num_bytes: 6697 num_examples: 119 - name: zh num_bytes: 9826 num_examples: 200 download_size: 179110 dataset_size: 266720 --- # NanoMIRACL This dataset is a Nano-style retrieval dataset. Nano-series evaluation can be run easily with the [HAKARI Benchmark](https://github.com/hotchpotch/hakari-bench). NanoMIRACL is derived from hotchpotch/miracl-hf-unified. 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). NanoMIRACL contains multilingual retrieval splits derived from `hotchpotch/miracl-hf-unified`. The `testB` query split is used when available, with a fallback recorded in split metadata when the requested split is not usable. ## Source Links - [MIRACL unified source dataset](https://huggingface.co/datasets/hotchpotch/miracl-hf-unified) ## 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 has the same Nano split names. The exact parquet paths are defined in the dataset card metadata above. If a regenerated 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. - For each selected query, one positive document is kept and negatives are sampled from that query's source negatives before corpus fill. 1. Use hotchpotch/miracl-hf-unified as the upstream benchmark or dataset family. 2. Load source datasets from `hotchpotch/miracl-hf-unified`. 3. Use `testB` queries when usable, with recorded per-language fallback when necessary. 4. Create one Nano split for each selected source retrieval task. 5. Keep up to 200 eligible queries per Nano split. 6. Include all qrels-positive documents for the selected queries. 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 retained document id. 9. Store corpus text in the generated document `text` field. 10. Generate BM25 top-100 candidates with `per-split auto` tokenization, or the per-split tokenizer shown below. 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. ## 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 up to 100 corpus ids. BM25 candidates are generated from the selected corpus for each split. When a qrels-positive document is not present in the raw BM25 top-100 results, 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 | |---|---|---|---:|---:|---:| | `ar` | ar_queries | hotchpotch/miracl-hf-unified | 200 | 1854 | 200 | | `bn` | bn_queries | hotchpotch/miracl-hf-unified | 200 | 1731 | 200 | | `de` | de_queries | hotchpotch/miracl-hf-unified | 200 | 1748 | 200 | | `en` | en_queries | hotchpotch/miracl-hf-unified | 200 | 1657 | 200 | | `es` | es_queries | hotchpotch/miracl-hf-unified | 200 | 1312 | 200 | | `fa` | fa_queries | hotchpotch/miracl-hf-unified | 200 | 1858 | 200 | | `fi` | fi_queries | hotchpotch/miracl-hf-unified | 200 | 1828 | 200 | | `fr` | fr_queries | hotchpotch/miracl-hf-unified | 200 | 1777 | 200 | | `hi` | hi_queries | hotchpotch/miracl-hf-unified | 200 | 1748 | 200 | | `id` | id_queries | hotchpotch/miracl-hf-unified | 200 | 1520 | 200 | | `ja` | ja_queries | hotchpotch/miracl-hf-unified | 200 | 1846 | 200 | | `ko` | ko_queries | hotchpotch/miracl-hf-unified | 200 | 2419 | 200 | | `ru` | ru_queries | hotchpotch/miracl-hf-unified | 200 | 1727 | 200 | | `sw` | sw_queries | hotchpotch/miracl-hf-unified | 200 | 1600 | 200 | | `te` | te_queries | hotchpotch/miracl-hf-unified | 84 | 754 | 84 | | `th` | th_queries | hotchpotch/miracl-hf-unified | 200 | 1897 | 200 | | `yo` | yo_queries | hotchpotch/miracl-hf-unified | 119 | 921 | 119 | | `zh` | zh_queries | hotchpotch/miracl-hf-unified | 200 | 1700 | 200 | ## BM25 nDCG@10 `nDCG@10` is computed from the included BM25 ranking against the included qrels. | Nano split | Tokenizer | Forced BM25 positives | BM25 nDCG@10 | |---|---|---:|---:| | `ar` | `stemmer:ar/arabic` | 19 | 0.5445 | | `bn` | `whitespace:bn` | 9 | 0.5103 | | `de` | `stemmer:de/german` | 39 | 0.3665 | | `en` | `stemmer:en/english` | 6 | 0.5432 | | `es` | `stemmer:es/spanish` | 16 | 0.5110 | | `fa` | `whitespace:fa` | 9 | 0.5337 | | `fi` | `stemmer:fi/finnish` | 14 | 0.6240 | | `fr` | `stemmer:fr/french` | 55 | 0.3034 | | `hi` | `stemmer:hi/hindi` | 6 | 0.5497 | | `id` | `stemmer:id/indonesian` | 16 | 0.5705 | | `ja` | `wordseg:ja` | 3 | 0.5956 | | `ko` | `wordseg:ko` | 5 | 0.5090 | | `ru` | `stemmer:ru/russian` | 31 | 0.4457 | | `sw` | `whitespace:sw` | 9 | 0.5782 | | `te` | `whitespace:te` | 8 | 0.6044 | | `th` | `wordseg:th` | 7 | 0.6475 | | `yo` | `whitespace:yo` | 13 | 0.5323 | | `zh` | `wordseg:zh` | 18 | 0.4466 | ## Skipped Tasks None. ## License NanoMIRACL is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets listed above.