--- configs: - config_name: corpus data_files: - split: NanoIFIRAila path: corpus/NanoIFIRAila-00000-of-00001.parquet - split: NanoIFIRCds path: corpus/NanoIFIRCds-00000-of-00001.parquet - split: NanoIFIRFiQA path: corpus/NanoIFIRFiQA-00000-of-00001.parquet - split: NanoIFIRFire path: corpus/NanoIFIRFire-00000-of-00001.parquet - split: NanoIFIRNFCorpus path: corpus/NanoIFIRNFCorpus-00000-of-00001.parquet - split: NanoIFIRPm path: corpus/NanoIFIRPm-00000-of-00001.parquet - split: NanoIFIRScifact path: corpus/NanoIFIRScifact-00000-of-00001.parquet - config_name: queries data_files: - split: NanoIFIRAila path: queries/NanoIFIRAila-00000-of-00001.parquet - split: NanoIFIRCds path: queries/NanoIFIRCds-00000-of-00001.parquet - split: NanoIFIRFiQA path: queries/NanoIFIRFiQA-00000-of-00001.parquet - split: NanoIFIRFire path: queries/NanoIFIRFire-00000-of-00001.parquet - split: NanoIFIRNFCorpus path: queries/NanoIFIRNFCorpus-00000-of-00001.parquet - split: NanoIFIRPm path: queries/NanoIFIRPm-00000-of-00001.parquet - split: NanoIFIRScifact path: queries/NanoIFIRScifact-00000-of-00001.parquet default: true - config_name: qrels data_files: - split: NanoIFIRAila path: qrels/NanoIFIRAila-00000-of-00001.parquet - split: NanoIFIRCds path: qrels/NanoIFIRCds-00000-of-00001.parquet - split: NanoIFIRFiQA path: qrels/NanoIFIRFiQA-00000-of-00001.parquet - split: NanoIFIRFire path: qrels/NanoIFIRFire-00000-of-00001.parquet - split: NanoIFIRNFCorpus path: qrels/NanoIFIRNFCorpus-00000-of-00001.parquet - split: NanoIFIRPm path: qrels/NanoIFIRPm-00000-of-00001.parquet - split: NanoIFIRScifact path: qrels/NanoIFIRScifact-00000-of-00001.parquet - config_name: bm25 data_files: - split: NanoIFIRAila path: bm25/NanoIFIRAila-00000-of-00001.parquet - split: NanoIFIRCds path: bm25/NanoIFIRCds-00000-of-00001.parquet - split: NanoIFIRFiQA path: bm25/NanoIFIRFiQA-00000-of-00001.parquet - split: NanoIFIRFire path: bm25/NanoIFIRFire-00000-of-00001.parquet - split: NanoIFIRNFCorpus path: bm25/NanoIFIRNFCorpus-00000-of-00001.parquet - split: NanoIFIRPm path: bm25/NanoIFIRPm-00000-of-00001.parquet - split: NanoIFIRScifact path: bm25/NanoIFIRScifact-00000-of-00001.parquet - config_name: harrier_oss_v1_270m data_files: - split: NanoIFIRAila path: harrier_oss_v1_270m/NanoIFIRAila-00000-of-00001.parquet - split: NanoIFIRCds path: harrier_oss_v1_270m/NanoIFIRCds-00000-of-00001.parquet - split: NanoIFIRFiQA path: harrier_oss_v1_270m/NanoIFIRFiQA-00000-of-00001.parquet - split: NanoIFIRFire path: harrier_oss_v1_270m/NanoIFIRFire-00000-of-00001.parquet - split: NanoIFIRNFCorpus path: harrier_oss_v1_270m/NanoIFIRNFCorpus-00000-of-00001.parquet - split: NanoIFIRPm path: harrier_oss_v1_270m/NanoIFIRPm-00000-of-00001.parquet - split: NanoIFIRScifact path: harrier_oss_v1_270m/NanoIFIRScifact-00000-of-00001.parquet - config_name: reranking_hybrid data_files: - split: NanoIFIRAila path: reranking_hybrid/NanoIFIRAila-00000-of-00001.parquet - split: NanoIFIRCds path: reranking_hybrid/NanoIFIRCds-00000-of-00001.parquet - split: NanoIFIRFiQA path: reranking_hybrid/NanoIFIRFiQA-00000-of-00001.parquet - split: NanoIFIRFire path: reranking_hybrid/NanoIFIRFire-00000-of-00001.parquet - split: NanoIFIRNFCorpus path: reranking_hybrid/NanoIFIRNFCorpus-00000-of-00001.parquet - split: NanoIFIRPm path: reranking_hybrid/NanoIFIRPm-00000-of-00001.parquet - split: NanoIFIRScifact path: reranking_hybrid/NanoIFIRScifact-00000-of-00001.parquet language: - en tags: - information-retrieval - retrieval - nano - bm25 - dense-retrieval - reranking - hakari-bench dataset_info: - config_name: bm25 features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoIFIRAila num_bytes: 173262 num_examples: 40 - name: NanoIFIRCds num_bytes: 231700 num_examples: 42 - name: NanoIFIRFiQA num_bytes: 907601 num_examples: 200 - name: NanoIFIRFire num_bytes: 670505 num_examples: 167 - name: NanoIFIRNFCorpus num_bytes: 515648 num_examples: 86 - name: NanoIFIRPm num_bytes: 443554 num_examples: 59 - name: NanoIFIRScifact num_bytes: 213810 num_examples: 43 download_size: 3163052 dataset_size: 3156080 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoIFIRAila num_bytes: 58311115 num_examples: 2914 - name: NanoIFIRCds num_bytes: 16485735 num_examples: 10000 - name: NanoIFIRFiQA num_bytes: 8056651 num_examples: 10000 - name: NanoIFIRFire num_bytes: 47265438 num_examples: 1739 - name: NanoIFIRNFCorpus num_bytes: 5776745 num_examples: 3593 - name: NanoIFIRPm num_bytes: 22638705 num_examples: 10000 - name: NanoIFIRScifact num_bytes: 14684875 num_examples: 10000 download_size: 88122092 dataset_size: 173219264 - config_name: harrier_oss_v1_270m features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoIFIRAila num_bytes: 173683 num_examples: 40 - name: NanoIFIRCds num_bytes: 231689 num_examples: 42 - name: NanoIFIRFiQA num_bytes: 910347 num_examples: 200 - name: NanoIFIRFire num_bytes: 670505 num_examples: 167 - name: NanoIFIRNFCorpus num_bytes: 515404 num_examples: 86 - name: NanoIFIRPm num_bytes: 443554 num_examples: 59 - name: NanoIFIRScifact num_bytes: 213773 num_examples: 43 download_size: 3165784 dataset_size: 3158955 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: NanoIFIRAila num_bytes: 2653 num_examples: 119 - name: NanoIFIRCds num_bytes: 11477 num_examples: 466 - name: NanoIFIRFiQA num_bytes: 20948 num_examples: 1010 - name: NanoIFIRFire num_bytes: 10697 num_examples: 563 - name: NanoIFIRNFCorpus num_bytes: 6883 num_examples: 242 - name: NanoIFIRPm num_bytes: 35074 num_examples: 1217 - name: NanoIFIRScifact num_bytes: 5581 num_examples: 255 download_size: 41077 dataset_size: 93313 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoIFIRAila num_bytes: 116355 num_examples: 40 - name: NanoIFIRCds num_bytes: 10203 num_examples: 42 - name: NanoIFIRFiQA num_bytes: 16200 num_examples: 200 - name: NanoIFIRFire num_bytes: 550908 num_examples: 167 - name: NanoIFIRNFCorpus num_bytes: 5031 num_examples: 86 - name: NanoIFIRPm num_bytes: 9653 num_examples: 59 - name: NanoIFIRScifact num_bytes: 3777 num_examples: 43 download_size: 365390 dataset_size: 712127 - config_name: reranking_hybrid features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoIFIRAila num_bytes: 35559 num_examples: 40 - name: NanoIFIRCds num_bytes: 46968 num_examples: 42 - name: NanoIFIRFiQA num_bytes: 185023 num_examples: 200 - name: NanoIFIRFire num_bytes: 136201 num_examples: 167 - name: NanoIFIRNFCorpus num_bytes: 104647 num_examples: 86 - name: NanoIFIRPm num_bytes: 89569 num_examples: 59 - name: NanoIFIRScifact num_bytes: 43800 num_examples: 43 download_size: 648423 dataset_size: 641767 --- # NanoIFIR This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). NanoIFIR contains 7 Nano retrieval splits derived from IFIR. 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/NanoIFIR" split = "NanoIFIRAila" 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 | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | NanoIFIRAila | 40 | 2914 | 119 | 2890.1 | 3012.0 | 3581.5 | 19998.1 | 15303.0 | 24733.2 | | NanoIFIRCds | 42 | 10000 | 466 | 225.2 | 213.0 | 260.2 | 1630.2 | 1604.0 | 1983.0 | | NanoIFIRFiQA | 200 | 10000 | 1010 | 65.8 | 64.5 | 78.2 | 791.9 | 541.0 | 975.0 | | NanoIFIRFire | 167 | 1739 | 563 | 3283.8 | 3242.0 | 3574.5 | 27167.7 | 24688.0 | 36394.5 | | NanoIFIRNFCorpus | 86 | 3593 | 242 | 37.8 | 37.0 | 44.8 | 1589.5 | 1612.0 | 1868.0 | | NanoIFIRPm | 59 | 10000 | 1217 | 145.7 | 146.0 | 157.0 | 2244.9 | 1651.5 | 2792.5 | | NanoIFIRScifact | 43 | 10000 | 255 | 73.6 | 73.0 | 90.5 | 1452.6 | 1454.0 | 1819.0 | ## 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 | - | 37.84 | 46.06 | 44.97 | 59.74 | 72.00 | 72.71 | - | 49 | | NanoIFIRAila | english_porter_stop | 9.88 | 8.78 | 7.98 | 28.30 | 35.19 | 35.35 | 100-101 | 21 | | NanoIFIRCds | english_porter_stop | 22.58 | 40.73 | 33.76 | 35.88 | 72.48 | 67.06 | 100-101 | 3 | | NanoIFIRFiQA | english_porter_stop | 34.22 | 53.28 | 46.78 | 62.14 | 79.86 | 77.92 | 100-101 | 3 | | NanoIFIRFire | english_porter_stop | 35.66 | 34.21 | 39.96 | 74.36 | 69.22 | 76.63 | 100-101 | 12 | | NanoIFIRNFCorpus | english_porter_stop | 33.38 | 45.80 | 41.08 | 63.58 | 83.40 | 78.27 | 100-101 | 9 | | NanoIFIRPm | english_porter_stop | 42.32 | 54.48 | 54.68 | 55.85 | 68.25 | 74.18 | 100-101 | 1 | | NanoIFIRScifact | english_porter_stop | 86.82 | 85.16 | 90.55 | 98.10 | 95.56 | 99.57 | 100 | 0 | ## Hybrid Safeguard Summary - Safeguard positives: 49 - Rows limited by corpus size: 0 - Metadata file: `reranking_hybrid_metadata.json` ## Source Links - Source benchmark: `IFIR` - `if-ir/aila`: https://huggingface.co/datasets/if-ir/aila - `if-ir/cds`: https://huggingface.co/datasets/if-ir/cds - `if-ir/fiqa`: https://huggingface.co/datasets/if-ir/fiqa - `if-ir/fire`: https://huggingface.co/datasets/if-ir/fire - `if-ir/nfcorpus`: https://huggingface.co/datasets/if-ir/nfcorpus - `if-ir/pm`: https://huggingface.co/datasets/if-ir/pm - `if-ir/scifact_open`: https://huggingface.co/datasets/if-ir/scifact_open ## License NanoIFIR is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets and benchmarks.