metadata
language:
- en
pretty_name: QReCC Passage Collection
QReCC Passages (54M Web Crawl)
This repository hosts the QReCC passage collection—a raw web-crawl dataset of 54 million passages. It includes only "id" and "contents" per record, stored in compressed Parquet format for efficient loading and streaming.
Source & Context
This dataset complements the QReCC retrieval setup outlined in the Apple ML-QReCC GitHub repository. Use this passage collection as the retrieval corpus for query rewriting and conversational information-seeking tasks.
Files & Structure
Each Parquet file contains roughly 1 million passages with the following schema:
| Field | Type | Description |
|---|---|---|
id |
string | Unique passage identifier |
contents |
string | Raw passage text (web crawl) |
Files are compressed using zstd for optimal storage and performance.
Loading the Dataset
Use the Hugging Face datasets library for easy access:
from datasets import load_dataset
# Streaming mode across all shards:
ds = load_dataset("slupart/qrecc-passages", split="train", streaming=True)
# Or load them as a static dataset:
ds = load_dataset(
"slupart/qrecc-passages",
data_files={"train": "data/train-*.parquet"},
split="train"
)
# Inspect
print(ds)
print(ds[0])
print(ds[1234]["contents"][:200])
Contact & Citation
If you use this dataset in academic or applied work, you can cite the original QReCC dataset and our work:
- The original QReCC benchmark.
- Our work DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search
@inproceedings{lupart2025disco,
title={DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search},
author={Lupart, Simon and Aliannejadi, Mohammad and Kanoulas, Evangelos},
booktitle={Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={9--19},
year={2025}
}