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

> **What’s new (Sept 2025)** — **DAPFAM patent family retrieval tasks are now in MTEB.** 18 tasks (ALL / IN / OUT × 3 query views × 3 target views) are available, including the 6 main ones used in our paper. You can benchmark any model with a single script and reproduce the paper’s results by selecting the same encoder (Snowflake/snowflake-arctic-embed-m-v2.0). Our paper used int8 quantization for hardware reasons; results may vary very slightly (not significantly) if you run in float16/32.

### DAPFAM — A Domain‑Aware Family‑level Dataset to benchmark cross‑domain patent retrieval

**License:** CC‑BY‑NC‑SA‑4.0  
**Tasks:** text‑retrieval (patent family prior‑art retrieval)  
**Languages:** English (eng‑Latn)  
**Evaluation date span:** 1964‑06‑26 → 2023‑06‑20  
**Cite:** Ayaou et al., 2025 — _DAPFAM: A Domain‑Aware Family‑level Dataset to benchmark cross‑domain patent retrieval_ (arXiv:2506.22141)

---

### Summary

**DAPFAM** provides **1,247 query patent families** and **45,336 target families** with **citation‑based relevance** and explicit **domain labels** (IN/OUT). Each positive pair is IN‑domain if query and target share at least one IPC3 code, OUT‑domain otherwise. Text is at **family‑level full text** (title, abstract, claims, description). Supports both **document-** and **passage‑level** retrieval.

**What makes DAPFAM different?**
- **Explicit domain partitions** (IN vs OUT) → enables true cross‑domain evaluation.
- **Family‑level aggregation** → reduces cross‑jurisdiction redundancy.
- **Compute‑aware** → Small enough to support passage level experimentations on consumer-grade hardware.

---

### Benchmark DAPFAM on MTEB

**18 retrieval tasks** have been added (ALL / IN / OUT × 3 query × 3 target field views). Six of them were directly evaluated in the paper.

#### Task naming scheme
- Query view: **TA** (Title+Abstract) or **TAC** (Title+Abstract+Claims)  
- Target view: **TA**, **TAC**, or **FullText** (adds description)
- Subsets: **ALL**, **IN**, **OUT** (IPC overlap filtering)

#### Task list (18 total)

**ALL**
- `DAPFAMAllTitlAbsToTitlAbsRetrieval`
- `DAPFAMAllTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
- `DAPFAMAllTitlAbsToFullTextRetrieval`
- `DAPFAMAllTitlAbsClmToTitlAbsRetrieval`
- `DAPFAMAllTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
- `DAPFAMAllTitlAbsClmToFullTextRetrieval`

**IN**
- `DAPFAMInTitlAbsToTitlAbsRetrieval`
- `DAPFAMInTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
- `DAPFAMInTitlAbsToFullTextRetrieval`
- `DAPFAMInTitlAbsClmToTitlAbsRetrieval`
- `DAPFAMInTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
- `DAPFAMInTitlAbsClmToFullTextRetrieval`

**OUT**
- `DAPFAMOutTitlAbsToTitlAbsRetrieval`
- `DAPFAMOutTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
- `DAPFAMOutTitlAbsToFullTextRetrieval`
- `DAPFAMOutTitlAbsClmToTitlAbsRetrieval`
- `DAPFAMOutTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
- `DAPFAMOutTitlAbsClmToFullTextRetrieval`

#### Quick start — run all tasks
```python
import mteb
from sentence_transformers import SentenceTransformer

model_name = "Snowflake/snowflake-arctic-embed-m-v2.0"
model = SentenceTransformer(model_name, trust_remote_code=True,
                            model_kwargs={"torch_dtype":"float16"}).cuda().eval()

task_names = [
  # ALL
  "DAPFAMAllTitlAbsToTitlAbsRetrieval",
  "DAPFAMAllTitlAbsToTitlAbsClmRetrieval",
  "DAPFAMAllTitlAbsToFullTextRetrieval",
  "DAPFAMAllTitlAbsClmToTitlAbsRetrieval",
  "DAPFAMAllTitlAbsClmToTitlAbsClmRetrieval",
  "DAPFAMAllTitlAbsClmToFullTextRetrieval",
  # IN
  "DAPFAMInTitlAbsToTitlAbsRetrieval",
  "DAPFAMInTitlAbsToTitlAbsClmRetrieval",
  "DAPFAMInTitlAbsToFullTextRetrieval",
  "DAPFAMInTitlAbsClmToTitlAbsRetrieval",
  "DAPFAMInTitlAbsClmToTitlAbsClmRetrieval",
  "DAPFAMInTitlAbsClmToFullTextRetrieval",
  # OUT
  "DAPFAMOutTitlAbsToTitlAbsRetrieval",
  "DAPFAMOutTitlAbsToTitlAbsClmRetrieval",
  "DAPFAMOutTitlAbsToFullTextRetrieval",
  "DAPFAMOutTitlAbsClmToTitlAbsRetrieval",
  "DAPFAMOutTitlAbsClmToTitlAbsClmRetrieval",
  "DAPFAMOutTitlAbsClmToFullTextRetrieval",
]

tasks = mteb.get_tasks(tasks=task_names)
results = mteb.MTEB(tasks=tasks).run(
    model,
    output_folder=f"mteb_res/{model_name}",
    encode_kwargs={"batch_size": 16, "prompt_name": None}
)
print(results)
```

> To reproduce the **paper’s reported MTEB-compatible results**, restrict to the six **in-paper tasks** listed above. The encoder was run with int8 quantization in the paper; float16 runs on GPU may differ slightly.

---

### How to Load the Dataset

```python
from datasets import load_dataset

dc = load_dataset("datalyes/DAPFAM_patent", "corpus")      # 45,336 targets
dq = load_dataset("datalyes/DAPFAM_patent", "queries")     # 1,247 queries
dr = load_dataset("datalyes/DAPFAM_patent", "relations")   # qrels: all/in/out
```

**Counts**
- Queries: **1,247**
- Targets: **45,336**
- Qrels (all): **≈49,869** (positives + sampled negatives)
- Positive qrels: **IN ~19,736**, **OUT ~5,193**

---

### Evaluation choices
- Metrics: **NDCG@100** (primary), **Recall@100** (secondary).
- Document-level views in MTEB; paper also explores **passage-level** retrieval and **RRF fusion** separately.
- Encoder: `Snowflake/snowflake-arctic-embed-m-v2.0`; in-paper runs quantized to int8 for efficiency.

---


### Citation
```
@misc{ayaou2025dapfamdomainawarefamilyleveldataset,
  title={DAPFAM: A Domain-Aware Family-level Dataset to benchmark cross domain patent retrieval},
  author={Iliass Ayaou and Denis Cavallucci and Hicham Chibane},
  year={2025},
  eprint={2506.22141},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2506.22141},
}
```