| --- |
| configs: |
| - config_name: corpus |
| data_files: |
| - split: NanoArguAna |
| path: corpus/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: corpus/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: corpus/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: corpus/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: corpus/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: corpus/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: corpus/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: corpus/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: corpus/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: corpus/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: corpus/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: corpus/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: corpus/NanoTouche2020-00000-of-00001.parquet |
| - config_name: queries |
| data_files: |
| - split: NanoArguAna |
| path: queries/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: queries/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: queries/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: queries/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: queries/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: queries/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: queries/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: queries/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: queries/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: queries/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: queries/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: queries/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: queries/NanoTouche2020-00000-of-00001.parquet |
| default: true |
| - config_name: qrels |
| data_files: |
| - split: NanoArguAna |
| path: qrels/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: qrels/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: qrels/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: qrels/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: qrels/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: qrels/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: qrels/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: qrels/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: qrels/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: qrels/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: qrels/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: qrels/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: qrels/NanoTouche2020-00000-of-00001.parquet |
| - config_name: bm25 |
| data_files: |
| - split: NanoArguAna |
| path: bm25/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: bm25/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: bm25/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: bm25/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: bm25/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: bm25/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: bm25/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: bm25/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: bm25/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: bm25/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: bm25/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: bm25/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: bm25/NanoTouche2020-00000-of-00001.parquet |
| - config_name: harrier_oss_v1_270m |
| data_files: |
| - split: NanoArguAna |
| path: harrier_oss_v1_270m/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: harrier_oss_v1_270m/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: harrier_oss_v1_270m/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: harrier_oss_v1_270m/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: harrier_oss_v1_270m/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: harrier_oss_v1_270m/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: harrier_oss_v1_270m/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: harrier_oss_v1_270m/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: harrier_oss_v1_270m/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: harrier_oss_v1_270m/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: harrier_oss_v1_270m/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: harrier_oss_v1_270m/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: harrier_oss_v1_270m/NanoTouche2020-00000-of-00001.parquet |
| - config_name: reranking_hybrid |
| data_files: |
| - split: NanoArguAna |
| path: reranking_hybrid/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: reranking_hybrid/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: reranking_hybrid/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: reranking_hybrid/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: reranking_hybrid/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: reranking_hybrid/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: reranking_hybrid/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: reranking_hybrid/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: reranking_hybrid/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: reranking_hybrid/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: reranking_hybrid/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: reranking_hybrid/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: reranking_hybrid/NanoTouche2020-00000-of-00001.parquet |
| language: |
| - multilingual |
| tags: |
| - information-retrieval |
| - retrieval |
| - nano |
| - bm25 |
| - hakari-bench |
| - dense-retrieval |
| - reranking |
| dataset_info: |
| - config_name: bm25 |
| features: |
| - name: query-id |
| dtype: string |
| - name: corpus-ids |
| list: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 968906 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 597462 |
| num_examples: 50 |
| - name: NanoDBPedia |
| num_bytes: 870382 |
| num_examples: 50 |
| - name: NanoFEVER |
| num_bytes: 592217 |
| num_examples: 50 |
| - name: NanoFiQA2018 |
| num_bytes: 246065 |
| num_examples: 50 |
| - name: NanoHotpotQA |
| num_bytes: 289045 |
| num_examples: 50 |
| - name: NanoMSMARCO |
| num_bytes: 273112 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 300043 |
| num_examples: 50 |
| - name: NanoNQ |
| num_bytes: 336367 |
| num_examples: 50 |
| - name: NanoQuoraRetrieval |
| num_bytes: 244341 |
| num_examples: 50 |
| - name: NanoSCIDOCS |
| num_bytes: 1102400 |
| num_examples: 50 |
| - name: NanoSciFact |
| num_bytes: 290835 |
| num_examples: 50 |
| - name: NanoTouche2020 |
| num_bytes: 1052551 |
| num_examples: 49 |
| download_size: 7181981 |
| dataset_size: 7163682 |
| - config_name: corpus |
| features: |
| - name: _id |
| dtype: string |
| - name: text |
| dtype: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 9139312 |
| num_examples: 3635 |
| - name: NanoClimateFEVER |
| num_bytes: 13592150 |
| num_examples: 3408 |
| - name: NanoDBPedia |
| num_bytes: 5364479 |
| num_examples: 6045 |
| - name: NanoFEVER |
| num_bytes: 14691753 |
| num_examples: 4996 |
| - name: NanoFiQA2018 |
| num_bytes: 10215813 |
| num_examples: 4598 |
| - name: NanoHotpotQA |
| num_bytes: 4491693 |
| num_examples: 5090 |
| - name: NanoMSMARCO |
| num_bytes: 4234692 |
| num_examples: 5043 |
| - name: NanoNFCorpus |
| num_bytes: 11342560 |
| num_examples: 2953 |
| - name: NanoNQ |
| num_bytes: 6658157 |
| num_examples: 5035 |
| - name: NanoQuoraRetrieval |
| num_bytes: 843078 |
| num_examples: 5046 |
| - name: NanoSCIDOCS |
| num_bytes: 5238345 |
| num_examples: 2210 |
| - name: NanoSciFact |
| num_bytes: 10695449 |
| num_examples: 2919 |
| - name: NanoTouche2020 |
| num_bytes: 23907038 |
| num_examples: 5745 |
| download_size: 44434242 |
| dataset_size: 120414519 |
| - config_name: harrier_oss_v1_270m |
| features: |
| - name: query-id |
| dtype: string |
| - name: corpus-ids |
| list: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 968772 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 639562 |
| num_examples: 50 |
| - name: NanoDBPedia |
| num_bytes: 883056 |
| num_examples: 50 |
| - name: NanoFEVER |
| num_bytes: 596735 |
| num_examples: 50 |
| - name: NanoFiQA2018 |
| num_bytes: 245484 |
| num_examples: 50 |
| - name: NanoHotpotQA |
| num_bytes: 290059 |
| num_examples: 50 |
| - name: NanoMSMARCO |
| num_bytes: 272620 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 299810 |
| num_examples: 50 |
| - name: NanoNQ |
| num_bytes: 336931 |
| num_examples: 50 |
| - name: NanoQuoraRetrieval |
| num_bytes: 243866 |
| num_examples: 50 |
| - name: NanoSCIDOCS |
| num_bytes: 1102400 |
| num_examples: 50 |
| - name: NanoSciFact |
| num_bytes: 290959 |
| num_examples: 50 |
| - name: NanoTouche2020 |
| num_bytes: 1052575 |
| num_examples: 49 |
| download_size: 7241287 |
| dataset_size: 7222829 |
| - config_name: qrels |
| features: |
| - name: query-id |
| dtype: string |
| - name: corpus-id |
| dtype: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 3496 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 4361 |
| num_examples: 148 |
| - name: NanoDBPedia |
| num_bytes: 60640 |
| num_examples: 1158 |
| - name: NanoFEVER |
| num_bytes: 1630 |
| num_examples: 57 |
| - name: NanoFiQA2018 |
| num_bytes: 2200 |
| num_examples: 123 |
| - name: NanoHotpotQA |
| num_bytes: 3885 |
| num_examples: 100 |
| - name: NanoMSMARCO |
| num_bytes: 1065 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 64851 |
| num_examples: 2518 |
| - name: NanoNQ |
| num_bytes: 1340 |
| num_examples: 57 |
| - name: NanoQuoraRetrieval |
| num_bytes: 1359 |
| num_examples: 70 |
| - name: NanoSCIDOCS |
| num_bytes: 21472 |
| num_examples: 244 |
| - name: NanoSciFact |
| num_bytes: 1054 |
| num_examples: 56 |
| - name: NanoTouche2020 |
| num_bytes: 45452 |
| num_examples: 932 |
| download_size: 88208 |
| dataset_size: 190263 |
| - config_name: queries |
| features: |
| - name: _id |
| dtype: string |
| - name: text |
| dtype: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 121065 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 17656 |
| num_examples: 50 |
| - name: NanoDBPedia |
| num_bytes: 5544 |
| num_examples: 50 |
| - name: NanoFEVER |
| num_bytes: 6997 |
| num_examples: 50 |
| - name: NanoFiQA2018 |
| num_bytes: 8592 |
| num_examples: 50 |
| - name: NanoHotpotQA |
| num_bytes: 12023 |
| num_examples: 50 |
| - name: NanoMSMARCO |
| num_bytes: 5476 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 4186 |
| num_examples: 50 |
| - name: NanoNQ |
| num_bytes: 6555 |
| num_examples: 50 |
| - name: NanoQuoraRetrieval |
| num_bytes: 7358 |
| num_examples: 50 |
| - name: NanoSCIDOCS |
| num_bytes: 12272 |
| num_examples: 50 |
| - name: NanoSciFact |
| num_bytes: 13793 |
| num_examples: 50 |
| - name: NanoTouche2020 |
| num_bytes: 7181 |
| num_examples: 49 |
| download_size: 131628 |
| dataset_size: 228698 |
| - config_name: reranking_hybrid |
| features: |
| - name: query-id |
| dtype: string |
| - name: corpus-ids |
| list: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 193665 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 131972 |
| num_examples: 50 |
| - name: NanoDBPedia |
| num_bytes: 179470 |
| num_examples: 50 |
| - name: NanoFEVER |
| num_bytes: 120590 |
| num_examples: 50 |
| - name: NanoFiQA2018 |
| num_bytes: 49646 |
| num_examples: 50 |
| - name: NanoHotpotQA |
| num_bytes: 59208 |
| num_examples: 50 |
| - name: NanoMSMARCO |
| num_bytes: 55165 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 60735 |
| num_examples: 50 |
| - name: NanoNQ |
| num_bytes: 67935 |
| num_examples: 50 |
| - name: NanoQuoraRetrieval |
| num_bytes: 49386 |
| num_examples: 50 |
| - name: NanoSCIDOCS |
| num_bytes: 222488 |
| num_examples: 50 |
| - name: NanoSciFact |
| num_bytes: 58571 |
| num_examples: 50 |
| - name: NanoTouche2020 |
| num_bytes: 210909 |
| num_examples: 49 |
| download_size: 1477385 |
| dataset_size: 1459764 |
| --- |
| # NanoBEIR-th |
|
|
| This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). |
|
|
| NanoBEIR-th is the Thai language-specific component of MNanoBEIR. It groups compact BEIR-derived retrieval tasks for efficient evaluation of document ranking in that language. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset_id = "hakari-bench/NanoBEIR-th" |
| split = "NanoArguAna" |
| |
| 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. `NanoNFCorpus` includes the full positive qrels (2,518 rows); qrels are not capped to the top-100 reranking depth. |
|
|
| ## Candidate Construction |
|
|
| - `bm25`: local BM25 top-500 with automatic tokenizer selection. Auto mode uses `wordseg` for `ja`, `zh`, `th`, `ko`, and `vi`, and `regex` otherwise. The resolved tokenizer is shown for each split in the Candidate Quality table. |
| - `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. Qrels are not capped to fit the top-100 reranking depth. For `NanoNFCorpus`, some queries have more than 100 positive qrels, so top-100 hybrid candidate coverage is expected to be below 100%; this is a candidate-list diagnostic, not a qrels filtering rule. |
|
|
| ## 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 | |
| |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| |
| | NanoArguAna | 50 | 3635 | 50 | 820.6 | 881.5 | 913.0 | 860.1 | 771.0 | 1073.0 | |
| | NanoClimateFEVER | 50 | 3408 | 148 | 118.6 | 113.0 | 151.2 | 1395.4 | 1263.5 | 1799.2 | |
| | NanoDBPedia | 50 | 6045 | 1158 | 30.9 | 28.5 | 42.5 | 316.4 | 345.0 | 414.0 | |
| | NanoFEVER | 50 | 4996 | 57 | 46.9 | 45.5 | 57.0 | 1084.7 | 930.5 | 1455.2 | |
| | NanoFiQA2018 | 50 | 4598 | 123 | 55.2 | 51.5 | 70.0 | 779.2 | 574.5 | 999.8 | |
| | NanoHotpotQA | 50 | 5090 | 100 | 79.7 | 74.0 | 99.8 | 330.7 | 287.0 | 449.0 | |
| | NanoMSMARCO | 50 | 5043 | 50 | 32.1 | 29.0 | 38.5 | 293.9 | 266.0 | 339.0 | |
| | NanoNFCorpus | 50 | 2953 | 2518 | 22.6 | 20.5 | 31.0 | 1387.4 | 1409.0 | 1647.0 | |
| | NanoNQ | 50 | 5035 | 57 | 40.8 | 39.0 | 44.8 | 473.6 | 402.0 | 673.0 | |
| | NanoQuoraRetrieval | 50 | 5046 | 70 | 46.9 | 42.0 | 56.0 | 53.7 | 47.0 | 62.0 | |
| | NanoSCIDOCS | 50 | 2210 | 244 | 69.1 | 68.5 | 82.0 | 820.4 | 814.5 | 1100.0 | |
| | NanoSciFact | 50 | 2919 | 56 | 92.7 | 88.5 | 117.8 | 1328.8 | 1245.0 | 1617.0 | |
| | NanoTouche2020 | 49 | 5745 | 932 | 46.3 | 42.0 | 55.0 | 1438.1 | 886.0 | 2677.0 | |
|
|
| ## 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 | - | 43.71 | 51.02 | 49.61 | 74.11 | 78.86 | 81.39 | - | 31 | |
| | NanoArguAna | wordseg@th | 40.51 | 37.21 | 43.49 | 94.00 | 90.00 | 92.00 | 100-101 | 4 | |
| | NanoClimateFEVER | wordseg@th | 23.68 | 34.44 | 30.15 | 58.00 | 69.53 | 71.27 | 100-101 | 2 | |
| | NanoDBPedia | wordseg@th | 50.43 | 54.68 | 54.82 | 68.43 | 74.92 | 78.09 | 100 | 0 | |
| | NanoFEVER | wordseg@th | 70.01 | 86.63 | 77.68 | 95.00 | 99.00 | 98.00 | 100-101 | 1 | |
| | NanoFiQA2018 | wordseg@th | 27.26 | 40.85 | 39.11 | 65.29 | 70.98 | 73.74 | 100-101 | 6 | |
| | NanoHotpotQA | wordseg@th | 55.23 | 68.80 | 66.52 | 86.00 | 95.00 | 96.00 | 100 | 0 | |
| | NanoMSMARCO | wordseg@th | 29.07 | 42.65 | 36.53 | 80.00 | 92.00 | 94.00 | 100-101 | 3 | |
| | NanoNFCorpus | wordseg@th | 26.63 | 24.09 | 27.43 | 18.36 | 21.06 | 25.49 | 100-101 | 6 | |
| | NanoNQ | wordseg@th | 31.91 | 53.67 | 42.46 | 84.00 | 92.00 | 93.00 | 100-101 | 3 | |
| | NanoQuoraRetrieval | wordseg@th | 72.67 | 88.59 | 79.28 | 96.00 | 100.00 | 100.00 | 100 | 0 | |
| | NanoSCIDOCS | wordseg@th | 26.41 | 29.15 | 31.65 | 55.97 | 60.07 | 62.47 | 100-101 | 2 | |
| | NanoSciFact | wordseg@th | 63.34 | 57.13 | 62.06 | 85.00 | 84.00 | 92.00 | 100-101 | 4 | |
| | NanoTouche2020 | wordseg@th | 51.08 | 45.34 | 53.80 | 77.38 | 76.65 | 82.07 | 100 | 0 | |
|
|
| ## Hybrid Safeguard Summary |
|
|
| - Safeguard positives: 31 |
| - Rows limited by corpus size: 0 |
| - Metadata file: `reranking_hybrid_metadata.json` |
|
|
| ## Source Links |
|
|
| - Final dataset: [hakari-bench/NanoBEIR-th](https://huggingface.co/datasets/hakari-bench/NanoBEIR-th) |
|
|
| ## License |
|
|
| NanoBEIR-th is a derived dataset. Users must comply with the licenses, |
| terms, and attribution requirements of the upstream source datasets. |
|
|
