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HZ-VUW/Pathology-Retrieval-Benchmark

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… See the full description on the dataset page: https://huggingface.co/datasets/HZ-VUW/Pathology-Retrieval-Benchmark.

sourceHugging Faceupdated 3mo agoView on Hugging Face

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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

python

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.