--- configs: - config_name: corpus data_files: - split: Nano2WikiMultihopQA path: corpus/Nano2WikiMultihopQA-00000-of-00001.parquet - split: NanoNarrativeQA path: corpus/NanoNarrativeQA-00000-of-00001.parquet - split: NanoNeedle path: corpus/NanoNeedle-00000-of-00001.parquet - split: NanoPasskey path: corpus/NanoPasskey-00000-of-00001.parquet - split: NanoQMSum path: corpus/NanoQMSum-00000-of-00001.parquet - split: NanoSummScreenFD path: corpus/NanoSummScreenFD-00000-of-00001.parquet - config_name: queries data_files: - split: Nano2WikiMultihopQA path: queries/Nano2WikiMultihopQA-00000-of-00001.parquet - split: NanoNarrativeQA path: queries/NanoNarrativeQA-00000-of-00001.parquet - split: NanoNeedle path: queries/NanoNeedle-00000-of-00001.parquet - split: NanoPasskey path: queries/NanoPasskey-00000-of-00001.parquet - split: NanoQMSum path: queries/NanoQMSum-00000-of-00001.parquet - split: NanoSummScreenFD path: queries/NanoSummScreenFD-00000-of-00001.parquet default: true - config_name: qrels data_files: - split: Nano2WikiMultihopQA path: qrels/Nano2WikiMultihopQA-00000-of-00001.parquet - split: NanoNarrativeQA path: qrels/NanoNarrativeQA-00000-of-00001.parquet - split: NanoNeedle path: qrels/NanoNeedle-00000-of-00001.parquet - split: NanoPasskey path: qrels/NanoPasskey-00000-of-00001.parquet - split: NanoQMSum path: qrels/NanoQMSum-00000-of-00001.parquet - split: NanoSummScreenFD path: qrels/NanoSummScreenFD-00000-of-00001.parquet - config_name: bm25 data_files: - split: Nano2WikiMultihopQA path: bm25/Nano2WikiMultihopQA-00000-of-00001.parquet - split: NanoNarrativeQA path: bm25/NanoNarrativeQA-00000-of-00001.parquet - split: NanoNeedle path: bm25/NanoNeedle-00000-of-00001.parquet - split: NanoPasskey path: bm25/NanoPasskey-00000-of-00001.parquet - split: NanoQMSum path: bm25/NanoQMSum-00000-of-00001.parquet - split: NanoSummScreenFD path: bm25/NanoSummScreenFD-00000-of-00001.parquet - config_name: harrier_oss_v1_270m data_files: - split: Nano2WikiMultihopQA path: harrier_oss_v1_270m/Nano2WikiMultihopQA-00000-of-00001.parquet - split: NanoNarrativeQA path: harrier_oss_v1_270m/NanoNarrativeQA-00000-of-00001.parquet - split: NanoNeedle path: harrier_oss_v1_270m/NanoNeedle-00000-of-00001.parquet - split: NanoPasskey path: harrier_oss_v1_270m/NanoPasskey-00000-of-00001.parquet - split: NanoQMSum path: harrier_oss_v1_270m/NanoQMSum-00000-of-00001.parquet - split: NanoSummScreenFD path: harrier_oss_v1_270m/NanoSummScreenFD-00000-of-00001.parquet - config_name: reranking_hybrid data_files: - split: Nano2WikiMultihopQA path: reranking_hybrid/Nano2WikiMultihopQA-00000-of-00001.parquet - split: NanoNarrativeQA path: reranking_hybrid/NanoNarrativeQA-00000-of-00001.parquet - split: NanoNeedle path: reranking_hybrid/NanoNeedle-00000-of-00001.parquet - split: NanoPasskey path: reranking_hybrid/NanoPasskey-00000-of-00001.parquet - split: NanoQMSum path: reranking_hybrid/NanoQMSum-00000-of-00001.parquet - split: NanoSummScreenFD path: reranking_hybrid/NanoSummScreenFD-00000-of-00001.parquet language: - en tags: - Long Context - retrieval - nano - information-retrieval - bm25 - dense-retrieval - reranking - hakari-bench dataset_info: - config_name: bm25 features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: Nano2WikiMultihopQA num_bytes: 641290 num_examples: 200 - name: NanoNarrativeQA num_bytes: 762290 num_examples: 200 - name: NanoNeedle num_bytes: 832482 num_examples: 98 - name: NanoPasskey num_bytes: 837165 num_examples: 100 - name: NanoQMSum num_bytes: 414690 num_examples: 200 - name: NanoSummScreenFD num_bytes: 720490 num_examples: 200 download_size: 4210841 dataset_size: 4208407 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: Nano2WikiMultihopQA num_bytes: 11283128 num_examples: 300 - name: NanoNarrativeQA num_bytes: 116191265 num_examples: 355 - name: NanoNeedle num_bytes: 28226538 num_examples: 800 - name: NanoPasskey num_bytes: 23182064 num_examples: 800 - name: NanoQMSum num_bytes: 10515610 num_examples: 197 - name: NanoSummScreenFD num_bytes: 10373940 num_examples: 336 download_size: 95954462 dataset_size: 199772545 - config_name: harrier_oss_v1_270m features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: Nano2WikiMultihopQA num_bytes: 641290 num_examples: 200 - name: NanoNarrativeQA num_bytes: 762290 num_examples: 200 - name: NanoNeedle num_bytes: 842815 num_examples: 98 - name: NanoPasskey num_bytes: 831334 num_examples: 100 - name: NanoQMSum num_bytes: 414690 num_examples: 200 - name: NanoSummScreenFD num_bytes: 720490 num_examples: 200 download_size: 4215430 dataset_size: 4212909 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: Nano2WikiMultihopQA num_bytes: 4614 num_examples: 200 - name: NanoNarrativeQA num_bytes: 4616 num_examples: 200 - name: NanoNeedle num_bytes: 3362 num_examples: 98 - name: NanoPasskey num_bytes: 3434 num_examples: 100 - name: NanoQMSum num_bytes: 4576 num_examples: 200 - name: NanoSummScreenFD num_bytes: 4631 num_examples: 200 download_size: 18531 dataset_size: 25233 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: Nano2WikiMultihopQA num_bytes: 16812 num_examples: 200 - name: NanoNarrativeQA num_bytes: 13155 num_examples: 200 - name: NanoNeedle num_bytes: 7953 num_examples: 98 - name: NanoPasskey num_bytes: 5997 num_examples: 100 - name: NanoQMSum num_bytes: 92566 num_examples: 200 - name: NanoSummScreenFD num_bytes: 123428 num_examples: 200 download_size: 165197 dataset_size: 259911 - config_name: reranking_hybrid features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: Nano2WikiMultihopQA num_bytes: 215691 num_examples: 200 - name: NanoNarrativeQA num_bytes: 217014 num_examples: 200 - name: NanoNeedle num_bytes: 170732 num_examples: 98 - name: NanoPasskey num_bytes: 167789 num_examples: 100 - name: NanoQMSum num_bytes: 211758 num_examples: 200 - name: NanoSummScreenFD num_bytes: 216907 num_examples: 200 download_size: 1202006 dataset_size: 1199891 --- # NanoLongEmbed This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). NanoLongEmbed contains Nano-style long-context retrieval splits derived from LongEmbed tasks. ## Usage ```python from datasets import load_dataset dataset_id = "hakari-bench/NanoLongEmbed" split = "Nano2WikiMultihopQA" 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 | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | Nano2WikiMultihopQA | 200 | 300 | 200 | 67.5 | 65.5 | 79.2 | 37445.6 | 31645.5 | 55443.0 | | NanoNarrativeQA | 200 | 355 | 200 | 49.3 | 47.5 | 60.0 | 326753.0 | 252633.0 | 371460.0 | | NanoNeedle | 98 | 800 | 98 | 59.0 | 60.5 | 67.0 | 35246.1 | 12934.5 | 44460.5 | | NanoPasskey | 100 | 800 | 100 | 37.8 | 38.0 | 39.0 | 28956.7 | 10883.0 | 36355.8 | | NanoQMSum | 200 | 197 | 200 | 446.3 | 410.5 | 577.2 | 53335.8 | 50063.0 | 67575.0 | | NanoSummScreenFD | 200 | 336 | 200 | 600.7 | 507.0 | 776.2 | 30854.3 | 29210.0 | 37969.8 | ## 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 | - | 82.17 | 61.91 | 73.15 | 97.66 | 93.40 | 98.83 | - | 13 | | Nano2WikiMultihopQA | english_porter_stop | 95.03 | 84.00 | 91.11 | 99.00 | 96.50 | 100.00 | 100 | 0 | | NanoNarrativeQA | english_porter_stop | 76.19 | 33.15 | 51.20 | 90.00 | 75.00 | 94.50 | 100-101 | 11 | | NanoNeedle | english_porter_stop | 72.07 | 60.99 | 68.23 | 96.94 | 95.92 | 98.98 | 100-101 | 1 | | NanoPasskey | english_porter_stop | 77.17 | 64.73 | 72.94 | 100.00 | 100.00 | 100.00 | 100 | 0 | | NanoQMSum | english_porter_stop | 74.40 | 36.60 | 60.97 | 100.00 | 96.00 | 99.50 | 100-101 | 1 | | NanoSummScreenFD | english_porter_stop | 98.13 | 91.98 | 94.43 | 100.00 | 97.00 | 100.00 | 100 | 0 | ## Hybrid Safeguard Summary - Safeguard positives: 13 - Rows limited by corpus size: 0 - Metadata file: `reranking_hybrid_metadata.json` ## Source Links - [LongEmbed source dataset](https://huggingface.co/datasets/dwzhu/LongEmbed) ## License NanoLongEmbed is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets listed above.