README.md

---
license: cc-by-nc-sa-4.0
pretty_name: "DAPFAM –\_Domain‑Aware Patent Retrieval at the Family level"
tags:
- patents
- retrieval
- information‑retrieval
- cross‑domain
- patent
- fulltext
size_categories:
- 10K<n<100K
language:
- en

configs:
- config_name: corpus
  data_files: "corpus.parquet"
- config_name: queries
  data_files: "queries.parquet"
- config_name: relations
  data_files: "qrels_all.parquet"


---

# **DAPFAM** dataset

For more details on the dataset construction and baseline experimentations, see the accompanying paper: **Ayaou et al., 2025 — “DAPFAM: A Domain‑Aware Patent Retrieval Dataset Aggregated at the Family Level”[(Here)](https://doi.org/10.48550/arXiv.2506.22141) .**


## Summary

DAPFAM provides **1 247 domain balanced full-text query patent families** and **45 336 full-text target families** with forward/backward‑citation relevance labels (≈ 50 K pairs). Each relevant link is explicitly marked **in‑domain** or **out‑of‑domain** according to IPC 3‑char overlap, enabling rigorous cross‑domain evaluation.

* Full text **(title · abstract · claims · description)** plus rich metadata for *every* family.
* Multi‑jurisdictional, English‑only text (families may originate in US, JP, EP, CN, …).
* Parquet qrel file: `qrels_all.parquet`.

## Dataset Structure

```
corpus.parquet   # 45 336 rows, targets – every original column from the paper
queries.parquet  # 1 247 rows,   queries – same columns + abstract_keywords
qrels_all.parquet  # (all | in | out) four‑column tables → query_id · relevant_id · relevance_score · domain_rel
```

## How to load

```python
from datasets import load_dataset

#According to your usage, you might not need to load all 3 subsets

dc = load_dataset("datalyes/DAPFAM_patent", "corpus")

dq = load_dataset("datalyes/DAPFAM_patent", "queries")

dr = load_dataset("datalyes/DAPFAM_patent", "relations")
```

## Citation

If you find our paper or dataset helpful, please consider citing as follows:

```
@misc{ayaou2025dapfam,
    title={DAPFAM: A Domain-Aware Patent Retrieval Dataset Aggregated at the Family Level},
    author={Iliass Ayaou and Denis Cavallucci and Hicham Chibane},
    year={2025},
    eprint={2506.22141},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

## Quick Stats

* **Queries**: 1,247
* **Corpus (targets)**: 45,336
* **Qrels (all)**: 49,869
* **Qrels (in)**: 19,736
* **Qrels (out)**: 5,193