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Card: Success@5/Recall@1000 columns, mean query length, truncation counts, self-match note only where it applies

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  1. README.md +11 -11
README.md CHANGED
@@ -36,8 +36,8 @@ respectively), see [Encoding](#encoding).
36
  | `qrels.test.tsv` | text | 66,336 rows | TREC qrels, `qid \t 0 \t docid \t relevance`, no header |
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  | `gt_top100.tsv` | text | 5,000 rows | exact MaxSim top-100, see below |
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- All positional indices (`gt_top100.tsv`, and the row order of every `.npy` file) refer to the order
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- of `doc_ids.npy` and `queries_ids.npy`. Reordering either file invalidates `gt_top100.tsv`.
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  ## Statistics
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@@ -47,7 +47,7 @@ of `doc_ids.npy` and `queries_ids.npy`. Reordering either file invalidates `gt_t
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  | document vectors | 29,313,062 |
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  | vectors per document (min / median / mean / max) | 3 / 210 / 171.1 / 299 |
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  | queries | 50 |
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- | vectors per query (min / median / max) | 10 / 17 / 32 |
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  | queries with at least one qrel | 50 |
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  | qrels rows | 66,336 |
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  | embedding dimension | 128 |
@@ -64,7 +64,7 @@ of `doc_ids.npy` and `queries_ids.npy`. Reordering either file invalidates `gt_t
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  | query compute dtype | float32 (model weights loaded at this dtype for the query pass) |
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  | query storage dtype | fp32 |
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  | normalization | L2, by the model's own `Normalize` module, before the storage cast |
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- | document truncation | 300 tokens (the checkpoint's `document_length`), before the skiplist; longest document here 299 vectors |
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  | query truncation | 32 tokens (the checkpoint's `query_length`) |
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  | document skiplist | 32 words removed: ['!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', '`', '{', '|', '}', '~'] |
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  | document input | `title + "\n\n" + text` when the corpus has a title, else `text`; stripped |
@@ -77,6 +77,7 @@ of `doc_ids.npy` and `queries_ids.npy`. Reordering either file invalidates `gt_t
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  ## Ground truth: `gt_top100.tsv`
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  Exact brute-force MaxSim top-100 per query over the full corpus, from the vectors in this repo.
 
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  No header; tab-separated `qidx docidx rank score`:
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  - `qidx`: 0-based row into `queries_ids.npy` / `queries.npy`
@@ -86,16 +87,14 @@ No header; tab-separated `qidx docidx rank score`:
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  dot product`, computed in fp32 with the fp16 document vectors upcast to fp32. Expansion vectors
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  are included in the sum. Printed to 6 decimals.
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- No query id appears as a document id, so there are no self-matches.
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-
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  ## Retrieval quality
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- Sanity check of the vectors, not a leaderboard number: exact MaxSim over the full corpus scored
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- against `qrels.test.tsv` with ir_measures.
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- | nDCG@10 | Recall@100 | MRR@10 | MAP |
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- |---|---|---|---|
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- | 0.8390 | 0.1604 | 0.9750 | 0.1311 |
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  ## Loading
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@@ -152,3 +151,4 @@ Checks run by the exporter on the files exactly as written here:
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  |---|---|
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  | exported | 2026-09-25 |
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  | hardware | Tesla V100S-PCIE-32GB |
 
 
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  | `qrels.test.tsv` | text | 66,336 rows | TREC qrels, `qid \t 0 \t docid \t relevance`, no header |
37
  | `gt_top100.tsv` | text | 5,000 rows | exact MaxSim top-100, see below |
38
 
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+ All positional indices (the `gt_top*.tsv` files, and the row order of every `.npy` file) refer to the
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+ order of `doc_ids.npy` and `queries_ids.npy`. Reordering either file invalidates the ground truth.
41
 
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  ## Statistics
43
 
 
47
  | document vectors | 29,313,062 |
48
  | vectors per document (min / median / mean / max) | 3 / 210 / 171.1 / 299 |
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  | queries | 50 |
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+ | vectors per query (min / median / mean / max) | 10 / 17 / 17.4 / 32 |
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  | queries with at least one qrel | 50 |
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  | qrels rows | 66,336 |
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  | embedding dimension | 128 |
 
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  | query compute dtype | float32 (model weights loaded at this dtype for the query pass) |
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  | query storage dtype | fp32 |
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  | normalization | L2, by the model's own `Normalize` module, before the storage cast |
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+ | document truncation | 300 tokens (the checkpoint's `document_length`), applied before the skiplist. 60,950 of 171,332 documents (36%) were longer and were cut to it; longest here 299 vectors |
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  | query truncation | 32 tokens (the checkpoint's `query_length`) |
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  | document skiplist | 32 words removed: ['!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', '`', '{', '|', '}', '~'] |
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  | document input | `title + "\n\n" + text` when the corpus has a title, else `text`; stripped |
 
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  ## Ground truth: `gt_top100.tsv`
78
 
79
  Exact brute-force MaxSim top-100 per query over the full corpus, from the vectors in this repo.
80
+
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  No header; tab-separated `qidx docidx rank score`:
82
 
83
  - `qidx`: 0-based row into `queries_ids.npy` / `queries.npy`
 
87
  dot product`, computed in fp32 with the fp16 document vectors upcast to fp32. Expansion vectors
88
  are included in the sum. Printed to 6 decimals.
89
 
 
 
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  ## Retrieval quality
91
 
92
+ Sanity check of the vectors, not a leaderboard number: `gt_top100.tsv` (exact MaxSim over the full
93
+ corpus) scored against `qrels.test.tsv` with ir_measures.
94
 
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+ | nDCG@10 | MRR@10 | Success@5 | Recall@100 | Recall@1000 | MAP@100 |
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+ |---|---|---|---|---|---|
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+ | 0.8390 | 0.9750 | 1.0000 | 0.1604 | n/a (gt is top-100) | 0.1311 |
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  ## Loading
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  |---|---|
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  | exported | 2026-09-25 |
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  | hardware | Tesla V100S-PCIE-32GB |
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+ | revised | 2026-09-28: card regenerated; every other file unchanged |