Restore NanoNFCorpus full qrels and rebuild candidates

README.md

@@ -209,7 +209,7 @@ dataset_info:
     num_bytes: 273333
     num_examples: 50
   - name: NanoNFCorpus
-    num_bytes: 300176
+    num_bytes: 300195
     num_examples: 50
   - name: NanoNQ
     num_bytes: 336389
@@ -353,8 +353,8 @@ dataset_info:
     num_bytes: 1065
     num_examples: 50
   - name: NanoNFCorpus
-    num_bytes: 42309
-    num_examples: 1651
+    num_bytes: 64851
+    num_examples: 2518
   - name: NanoNQ
     num_bytes: 1340
     num_examples: 57
@@ -449,7 +449,7 @@ dataset_info:
     num_bytes: 55163
     num_examples: 50
   - name: NanoNFCorpus
-    num_bytes: 60687
+    num_bytes: 60686
     num_examples: 50
   - name: NanoNQ
     num_bytes: 67880
@@ -500,15 +500,15 @@ This dataset uses six Hugging Face Datasets configs:
 - `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.
+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 language-aware tokenization. The resolved tokenizer is shown in the Candidate Quality table, for example `wordseg@ja`.
+- `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.
+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
 
@@ -523,7 +523,7 @@ Length statistics are character counts computed with `len(str(text))`.
 | NanoFiQA2018 | 50 | 4598 | 123 | 29.6 | 29.5 | 36.8 | 490.3 | 361.0 | 622.8 |
 | NanoHotpotQA | 50 | 5090 | 100 | 49.5 | 46.0 | 59.8 | 197.1 | 170.0 | 269.0 |
 | NanoMSMARCO | 50 | 5043 | 50 | 19.1 | 17.0 | 23.8 | 169.2 | 152.0 | 202.5 |
-| NanoNFCorpus | 50 | 2953 | 1651 | 10.8 | 10.0 | 14.8 | 752.7 | 758.0 | 895.0 |
+| NanoNFCorpus | 50 | 2953 | 2518 | 10.8 | 10.0 | 14.8 | 752.7 | 758.0 | 895.0 |
 | NanoNQ | 50 | 5035 | 57 | 29.3 | 25.0 | 31.8 | 274.2 | 231.0 | 394.5 |
 | NanoQuoraRetrieval | 50 | 5046 | 70 | 28.7 | 28.0 | 32.0 | 32.8 | 28.0 | 37.0 |
 | NanoSCIDOCS | 50 | 2210 | 244 | 32.1 | 30.0 | 39.0 | 452.8 | 444.0 | 609.8 |
@@ -538,7 +538,7 @@ Dense means `microsoft/harrier-oss-v1-270m` with the `web_search_query` prompt a
 
 | 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 | - | 44.80 | 49.88 | 50.23 | 74.94 | 78.51 | 81.26 | - | 31 |
+| Mean | - | 44.99 | 50.02 | 50.47 | 74.95 | 78.50 | 81.34 | - | 31 |
 | NanoArguAna | wordseg@ko | 36.61 | 40.82 | 42.17 | 90.00 | 94.00 | 96.00 | 100-101 | 2 |
 | NanoClimateFEVER | wordseg@ko | 24.57 | 30.03 | 29.83 | 63.87 | 68.30 | 66.10 | 100-101 | 3 |
 | NanoDBPedia | wordseg@ko | 53.22 | 59.28 | 57.87 | 72.53 | 76.53 | 79.55 | 100 | 0 |
@@ -546,7 +546,7 @@ Dense means `microsoft/harrier-oss-v1-270m` with the `web_search_query` prompt a
 | NanoFiQA2018 | wordseg@ko | 34.15 | 37.13 | 42.91 | 60.57 | 74.03 | 73.29 | 100-101 | 7 |
 | NanoHotpotQA | wordseg@ko | 59.66 | 62.69 | 63.16 | 87.00 | 84.00 | 93.00 | 100-101 | 2 |
 | NanoMSMARCO | wordseg@ko | 33.20 | 41.64 | 43.71 | 88.00 | 96.00 | 96.00 | 100-101 | 2 |
-| NanoNFCorpus | wordseg@ko | 24.66 | 23.32 | 24.40 | 17.26 | 20.15 | 22.32 | 100-101 | 9 |
+| NanoNFCorpus | wordseg@ko | 27.19 | 25.15 | 27.45 | 17.45 | 20.08 | 23.38 | 100-101 | 9 |
 | NanoNQ | wordseg@ko | 43.01 | 58.05 | 50.33 | 78.00 | 93.00 | 99.00 | 100 | 0 |
 | NanoQuoraRetrieval | wordseg@ko | 70.62 | 81.33 | 76.32 | 97.33 | 96.00 | 100.00 | 100 | 0 |
 | NanoSCIDOCS | wordseg@ko | 26.73 | 33.10 | 33.80 | 60.93 | 64.17 | 64.27 | 100-101 | 1 |

bm25/NanoNFCorpus-00000-of-00001.parquet

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manifest.json

@@ -76,9 +76,9 @@
       "split_name": "NanoNFCorpus",
       "queries": 50,
       "corpus": 2953,
-      "qrels": 1651,
-      "forced_doc_count": 1420,
-      "bm25_ndcg_at_10": 0.31121510686206527,
+      "qrels": 2518,
+      "forced_doc_count": 1835,
+      "bm25_ndcg_at_10": 0.2719115991151203,
       "bm25_query_coverage": 1.0,
       "bm25_relevant_coverage": 1.0
     },

metadata/NanoNFCorpus.json

@@ -2,12 +2,14 @@
   "split_name": "NanoNFCorpus",
   "queries": 50,
   "corpus": 2953,
-  "qrels": 1651,
+  "qrels": 2518,
   "source_non_positive_qrels": 0,
   "qrels_selection": {
-    "qrels_per_query_cap": 100,
-    "removed_qrels_over_cap": 0,
-    "qrels_cap_policy": "positive qrels are capped per query to the BM25 candidate top_k"
+    "qrels_policy": "full positive qrels preserved",
+    "restored_qrels_from": "sentence-transformers/NanoBEIR-en@beb106fbcfaa599c508c667041bf8c85fd78736b",
+    "previous_hakari_qrels": 1651,
+    "restored_qrels": 867,
+    "max_positive_qrels_per_query": 463
   },
   "bm25": {
     "config": {
@@ -17,10 +19,10 @@
       "tokenizer": "wordseg",
       "tokenizer_name": "ko",
       "stemmer_algorithm": "english",
-      "top_k": 100,
+      "top_k": 500,
       "k1": 1.5,
       "b": 0.75,
-      "show_progress": true,
+      "show_progress": false,
       "auto_selected": true,
       "auto_detected_language": "ko",
       "auto_detection_language_counts": {
@@ -28,21 +30,55 @@
       },
       "auto_detection_sample_size": 10
     },
-    "ndcg_at_10": 0.31121510686206527,
+    "ndcg_at_10": 0.2719115991151203,
+    "recall_at_100": 0.1744638467802311,
     "candidate_coverage": {
-      "top_k": 100,
+      "top_k": 500,
       "query_count": 50,
       "query_with_relevance_count": 50,
       "covered_query_count": 50,
       "query_coverage": 1.0,
-      "relevant_count": 1651,
-      "covered_relevant_count": 1651,
+      "relevant_count": 2518,
+      "covered_relevant_count": 2518,
       "relevant_coverage": 1.0
     },
-    "rebuild_policy": "BM25 candidates were recomputed locally and qrels-positive documents missing from the raw top-k were forced into the final candidate list.",
-    "forced_queries": 48,
-    "forced_doc_count": 1420,
+    "rebuild_policy": "BM25 top-500 was recomputed locally; full qrels were preserved; qrels-positive documents were forced into the top-500 candidate list when missing.",
+    "forced_queries": 47,
+    "forced_doc_count": 1835,
     "missing_positive_doc_count_after_forcing": 0,
-    "rebuilt_at_utc": "2026-05-19T01:33:03.431442+00:00"
+    "rebuilt_at_utc": "2026-06-07T22:41:04.970324+00:00"
+  },
+  "harrier_oss_v1_270m": {
+    "source": "copied from current hakari-bench dataset; dense top-500 is qrels-independent",
+    "ndcg_at_10": 0.2514797539527374,
+    "recall_at_100": 0.20075794670983677,
+    "candidate_coverage": {
+      "top_k": 500,
+      "query_count": 50,
+      "query_with_relevance_count": 50,
+      "covered_query_count": 47,
+      "query_coverage": 0.94,
+      "relevant_count": 2518,
+      "covered_relevant_count": 870,
+      "relevant_coverage": 0.3455123113582208
+    }
+  },
+  "reranking_hybrid": {
+    "source": "rebuilt from BM25 top-500 and harrier_oss_v1_270m top-500 with RRF",
+    "rrf_k": 100,
+    "top_k": 100,
+    "ndcg_at_10": 0.2745446781196811,
+    "recall_at_100": 0.23381299050070026,
+    "candidate_coverage": {
+      "top_k": 100,
+      "query_count": 50,
+      "query_with_relevance_count": 50,
+      "covered_query_count": 41,
+      "query_coverage": 0.82,
+      "relevant_count": 2518,
+      "covered_relevant_count": 408,
+      "relevant_coverage": 0.16203335980937253
+    },
+    "safeguard_positive_count": 9
   }
 }

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