kind stringclasses 3
values | candidate_idx int64 0 19 ⌀ | score float64 0.42 0.78 ⌀ | embedding listlengths 46 779 |
|---|---|---|---|
query_text | null | null | [
[
0.0238037109375,
-0.0849609375,
-0.076171875,
0.1318359375,
-0.07568359375,
-0.062255859375,
0.03173828125,
0.1962890625,
-0.005889892578125,
0.0223388671875,
-0.123046875,
0.060302734375,
-0.00543212890625,
0.025390625,
0.041748046875,
-0.1044921875... |
query_image | null | null | [[0.0203857421875,-0.08544921875,-0.07177734375,0.1220703125,-0.07421875,-0.041015625,0.026123046875(...TRUNCATED) |
candidate | 0 | 0.466475 | [[0.0206298828125,-0.08642578125,-0.0712890625,0.1220703125,-0.076171875,-0.041259765625,0.025268554(...TRUNCATED) |
candidate | 1 | 0.459239 | [[0.0206298828125,-0.08642578125,-0.0712890625,0.1220703125,-0.076171875,-0.041259765625,0.025268554(...TRUNCATED) |
candidate | 2 | 0.456392 | [[0.0206298828125,-0.08642578125,-0.0712890625,0.1220703125,-0.076171875,-0.041259765625,0.025268554(...TRUNCATED) |
candidate | 3 | 0.447216 | [[0.0206298828125,-0.08642578125,-0.0712890625,0.1220703125,-0.076171875,-0.041259765625,0.025268554(...TRUNCATED) |
candidate | 4 | 0.441792 | [[0.0206298828125,-0.08642578125,-0.0712890625,0.1220703125,-0.076171875,-0.041259765625,0.025268554(...TRUNCATED) |
candidate | 5 | 0.439896 | [[0.0206298828125,-0.08642578125,-0.0712890625,0.1220703125,-0.076171875,-0.041259765625,0.025268554(...TRUNCATED) |
candidate | 6 | 0.437075 | [[0.0206298828125,-0.08642578125,-0.0712890625,0.1220703125,-0.076171875,-0.041259765625,0.025268554(...TRUNCATED) |
candidate | 7 | 0.433155 | [[0.0206298828125,-0.08642578125,-0.0712890625,0.1220703125,-0.076171875,-0.041259765625,0.025268554(...TRUNCATED) |
End of preview. Expand in Data Studio
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Check out the documentation for more information.
Pathology Retrieval Benchmark — Rerank Cache
Privacy-safe embedding cache for rerank-only reproduction (no document IDs or file paths).
Files
One Parquet file per query: entry_{id}.parquet (100 entries).
Schema
| Column | Description |
|---|---|
kind |
query_text, query_image, or candidate |
candidate_idx |
0–19 for candidates; null for queries |
score |
Text-retrieval score (candidates only) |
embedding |
ColQwen multi-vector embedding (N, 128) |
Each file contains 2 query rows + 20 candidate rows.
Usage
import pandas as pd
df = pd.read_parquet("entry_1.parquet")
text = df[df.kind == "query_text"].iloc[0].embedding
candidates = df[df.kind == "candidate"].sort_values("candidate_idx")
Generated from local retrieval cache via scripts/export_cache_parquet.py.
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