--- 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: 64108 num_examples: 50 - name: bn num_bytes: 59593 num_examples: 50 - name: de num_bytes: 66037 num_examples: 50 - name: en num_bytes: 68425 num_examples: 50 - name: es num_bytes: 65342 num_examples: 50 - name: fa num_bytes: 64110 num_examples: 50 - name: fi num_bytes: 61539 num_examples: 50 - name: fr num_bytes: 66315 num_examples: 50 - name: hi num_bytes: 61116 num_examples: 50 - name: id num_bytes: 62417 num_examples: 50 - name: ja num_bytes: 64423 num_examples: 50 - name: ko num_bytes: 61653 num_examples: 50 - name: ru num_bytes: 65196 num_examples: 50 - name: sw num_bytes: 55926 num_examples: 50 - name: te num_bytes: 59001 num_examples: 50 - name: th num_bytes: 60002 num_examples: 50 - name: yo num_bytes: 55885 num_examples: 50 - name: zh num_bytes: 64621 num_examples: 50 download_size: 606080 dataset_size: 1125709 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: ar num_bytes: 6209693 num_examples: 10000 - name: bn num_bytes: 10663914 num_examples: 10000 - name: de num_bytes: 4513915 num_examples: 10000 - name: en num_bytes: 4427839 num_examples: 10000 - name: es num_bytes: 4436644 num_examples: 10000 - name: fa num_bytes: 5169516 num_examples: 10000 - name: fi num_bytes: 3990049 num_examples: 10000 - name: fr num_bytes: 3817835 num_examples: 10000 - name: hi num_bytes: 9955101 num_examples: 10000 - name: id num_bytes: 3775104 num_examples: 10000 - name: ja num_bytes: 4473484 num_examples: 10000 - name: ko num_bytes: 4377975 num_examples: 10000 - name: ru num_bytes: 6468313 num_examples: 10000 - name: sw num_bytes: 2634380 num_examples: 10000 - name: te num_bytes: 10601667 num_examples: 10000 - name: th num_bytes: 10329258 num_examples: 10000 - name: yo num_bytes: 2208863 num_examples: 10000 - name: zh num_bytes: 3579613 num_examples: 10000 download_size: 53886936 dataset_size: 101633163 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: ar num_bytes: 1022 num_examples: 50 - name: bn num_bytes: 938 num_examples: 50 - name: de num_bytes: 1260 num_examples: 50 - name: en num_bytes: 1025 num_examples: 50 - name: es num_bytes: 1261 num_examples: 50 - name: fa num_bytes: 1229 num_examples: 50 - name: fi num_bytes: 971 num_examples: 50 - name: fr num_bytes: 1271 num_examples: 50 - name: hi num_bytes: 1190 num_examples: 50 - name: id num_bytes: 973 num_examples: 50 - name: ja num_bytes: 999 num_examples: 50 - name: ko num_bytes: 937 num_examples: 50 - name: ru num_bytes: 1002 num_examples: 50 - name: sw num_bytes: 918 num_examples: 50 - name: te num_bytes: 943 num_examples: 50 - name: th num_bytes: 958 num_examples: 50 - name: yo num_bytes: 1097 num_examples: 50 - name: zh num_bytes: 1237 num_examples: 50 download_size: 39198 dataset_size: 19231 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: ar num_bytes: 3229 num_examples: 50 - name: bn num_bytes: 6915 num_examples: 50 - name: de num_bytes: 3145 num_examples: 50 - name: en num_bytes: 2507 num_examples: 50 - name: es num_bytes: 3309 num_examples: 50 - name: fa num_bytes: 4570 num_examples: 50 - name: fi num_bytes: 2508 num_examples: 50 - name: fr num_bytes: 3127 num_examples: 50 - name: hi num_bytes: 8105 num_examples: 50 - name: id num_bytes: 2505 num_examples: 50 - name: ja num_bytes: 3190 num_examples: 50 - name: ko num_bytes: 2906 num_examples: 50 - name: ru num_bytes: 4585 num_examples: 50 - name: sw num_bytes: 2504 num_examples: 50 - name: te num_bytes: 5670 num_examples: 50 - name: th num_bytes: 6190 num_examples: 50 - name: yo num_bytes: 2811 num_examples: 50 - name: zh num_bytes: 2425 num_examples: 50 download_size: 71280 dataset_size: 70201 --- 🚧 This dataset is currently under construction. Specifications may change. # NanoMIRACL (with bm25 subset) A lightweight, evaluation-ready subset of [MIRACL](https://huggingface.co/datasets/miracl/miracl), designed for fast multilingual IR benchmarking. ## What this dataset is - A multilingual IR benchmark with 18 language splits: `ar`, `bn`, `de`, `en`, `es`, `fa`, `fi`, `fr`, `hi`, `id`, `ja`, `ko`, `ru`, `sw`, `te`, `th`, `yo`, `zh`. - Each split contains 50 queries and a corpus of 10,000 documents. - Built with deterministic sampling for reproducible benchmarking. ## Data structure - `corpus`: `_id`, `text` - `queries`: `_id`, `text` - `qrels`: `query-id`, `corpus-id`, `score` - `bm25`: `query-id`, `corpus-ids` (A bm25 subset is included for first-stage retrieval and reranking experiments.) ## Performance Comparison Across Models | Model | avg | ar | bn | de | en | es | fa | fi | fr | hi | id | ja | ko | ru | sw | te | th | yo | zh | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| | bm25 | 0.5138 | 0.5363 | 0.4760 | 0.4724 | 0.5831 | 0.5582 | 0.4837 | 0.6503 | 0.4241 | 0.5988 | 0.5178 | 0.5569 | 0.4627 | 0.4829 | 0.4472 | 0.5884 | 0.5798 | 0.4664 | 0.3632 | | e5-small | 0.7181 | 0.7463 | 0.7127 | 0.6579 | 0.6968 | 0.7278 | 0.7203 | 0.7808 | 0.6339 | 0.6612 | 0.6261 | 0.7044 | 0.6730 | 0.7271 | 0.7025 | 0.9779 | 0.8896 | 0.6130 | 0.6737 | | e5-large | 0.7791 | 0.8186 | 0.7920 | 0.7925 | 0.7386 | 0.8026 | 0.7522 | 0.8347 | 0.6710 | 0.7586 | 0.6229 | 0.7449 | 0.6907 | 0.7927 | 0.7540 | 0.9779 | 0.9174 | 0.8168 | 0.7465 | | bge-m3 | 0.7873 | 0.8193 | 0.8417 | 0.7337 | 0.7406 | 0.7419 | 0.7733 | 0.7977 | 0.7355 | 0.7402 | 0.6636 | 0.7975 | 0.7266 | 0.7993 | 0.7635 | 0.9926 | 0.9090 | 0.8547 | 0.7402 | ## Subset names - Split names: `ar`, `bn`, `de`, `en`, `es`, `fa`, `fi`, `fr`, `hi`, `id`, `ja`, `ko`, `ru`, `sw`, `te`, `th`, `yo`, `zh` - Config names: `corpus`, `queries`, `qrels`, `bm25` ## BM25 tokenization strategy - `bm25` was generated with `--auto-select-best-splitter`. - Candidate strategies: `transformer`, `stemmer`, `wordseg`, `nltk_stem`, `nltk_stem_stop`, `english_regex`, `english_porter`, `english_porter_stop`, `whitespace`. - Selection metric: best `nDCG@100` per split. Selected strategy by split: | Split | Strategy | Details | |---|---|---| | `ar` | `nltk_stem` | NLTK stemmer | | `bn` | `whitespace` | `str.split()` | | `de` | `nltk_stem_stop` | NLTK stemmer + stopword removal | | `en` | `english_regex` | regex tokenization (no stemming) | | `es` | `nltk_stem` | NLTK stemmer | | `fa` | `whitespace` | `str.split()` | | `fi` | `nltk_stem_stop` | NLTK stemmer + stopword removal | | `fr` | `nltk_stem_stop` | NLTK stemmer + stopword removal | | `hi` | `stemmer` | PyStemmer (`hindi`) | | `id` | `transformer` | tokenizer: `Qwen/Qwen3-0.6B` | | `ja` | `wordseg` | ja (fugashi + unidic-lite) | | `ko` | `transformer` | tokenizer: `Qwen/Qwen3-0.6B` | | `ru` | `nltk_stem` | NLTK stemmer | | `sw` | `whitespace` | `str.split()` | | `te` | `whitespace` | `str.split()` | | `th` | `wordseg` | th (pythainlp newmm) | | `yo` | `transformer` | tokenizer: `Qwen/Qwen3-0.6B` | | `zh` | `transformer` | tokenizer: `Qwen/Qwen3-0.6B` | ## Upstream source - Original dataset: [MIRACL](https://huggingface.co/datasets/miracl/miracl) - Base processed source: [hotchpotch/miracl-hf-unified](https://huggingface.co/datasets/hotchpotch/miracl-hf-unified) ## License Other. This dataset is derived from MIRACL and follows upstream licensing and attribution requirements.