---
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](https://github.com/apple/ml-qrecc). 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:

```python
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}
}
```