Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Added MTEB availability and partial reproduction steps
Browse files
README.md
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# **DAPFAM** dataset
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* Multi‑jurisdictional, English‑only text (families may originate in US, JP, EP, CN, …).
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* Parquet qrel file: `qrels_all.parquet`.
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```
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corpus.parquet # 45 336 rows, targets – every original column from the paper
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queries.parquet # 1 247 rows, queries – same columns + abstract_keywords
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qrels_all.parquet # (all | in | out) four‑column tables → query_id · relevant_id · relevance_score · domain_rel
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```
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```python
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from datasets import load_dataset
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If you find our paper or dataset helpful, please consider citing as follows:
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```
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@misc{ayaou2025dapfamdomainawarefamilyleveldataset,
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}
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```
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## Quick Stats
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* **Queries**: 1,247
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* **Corpus (targets)**: 45,336
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* **Qrels (all)**: 49,869
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* **Qrels (in)**: 19,736
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* **Qrels (out)**: 5,193
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# **DAPFAM** dataset
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> **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.
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### DAPFAM — A Domain‑Aware Family‑level Dataset to benchmark cross‑domain patent retrieval
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**License:** CC‑BY‑NC‑SA‑4.0
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**Tasks:** text‑retrieval (patent family prior‑art retrieval)
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**Languages:** English (eng‑Latn)
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**Evaluation date span:** 1964‑06‑26 → 2023‑06‑20
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**Cite:** Ayaou et al., 2025 — _DAPFAM: A Domain‑Aware Family‑level Dataset to benchmark cross‑domain patent retrieval_ (arXiv:2506.22141)
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---
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### Summary
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**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.
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**What makes DAPFAM different?**
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- **Explicit domain partitions** (IN vs OUT) → enables true cross‑domain evaluation.
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- **Family‑level aggregation** → reduces cross‑jurisdiction redundancy.
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- **Compute‑aware** → Small enough to support passage level experimentations on consumer-grade hardware.
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---
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### Benchmark DAPFAM on MTEB
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**18 retrieval tasks** have been added (ALL / IN / OUT × 3 query × 3 target field views). Six of them were directly evaluated in the paper.
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#### Task naming scheme
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- Query view: **TA** (Title+Abstract) or **TAC** (Title+Abstract+Claims)
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- Target view: **TA**, **TAC**, or **FullText** (adds description)
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- Subsets: **ALL**, **IN**, **OUT** (IPC overlap filtering)
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#### Task list (18 total)
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**ALL**
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- `DAPFAMAllTitlAbsToTitlAbsRetrieval`
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- `DAPFAMAllTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
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- `DAPFAMAllTitlAbsToFullTextRetrieval`
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- `DAPFAMAllTitlAbsClmToTitlAbsRetrieval`
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- `DAPFAMAllTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
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- `DAPFAMAllTitlAbsClmToFullTextRetrieval`
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**IN**
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- `DAPFAMInTitlAbsToTitlAbsRetrieval`
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- `DAPFAMInTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
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- `DAPFAMInTitlAbsToFullTextRetrieval`
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- `DAPFAMInTitlAbsClmToTitlAbsRetrieval`
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- `DAPFAMInTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
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- `DAPFAMInTitlAbsClmToFullTextRetrieval`
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**OUT**
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- `DAPFAMOutTitlAbsToTitlAbsRetrieval`
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- `DAPFAMOutTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
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- `DAPFAMOutTitlAbsToFullTextRetrieval`
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- `DAPFAMOutTitlAbsClmToTitlAbsRetrieval`
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- `DAPFAMOutTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
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- `DAPFAMOutTitlAbsClmToFullTextRetrieval`
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#### Quick start — run all tasks
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```python
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import mteb
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from sentence_transformers import SentenceTransformer
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model_name = "Snowflake/snowflake-arctic-embed-m-v2.0"
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model = SentenceTransformer(model_name, trust_remote_code=True,
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model_kwargs={"torch_dtype":"float16"}).cuda().eval()
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task_names = [
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# ALL
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"DAPFAMAllTitlAbsToTitlAbsRetrieval",
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"DAPFAMAllTitlAbsToTitlAbsClmRetrieval",
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"DAPFAMAllTitlAbsToFullTextRetrieval",
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"DAPFAMAllTitlAbsClmToTitlAbsRetrieval",
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"DAPFAMAllTitlAbsClmToTitlAbsClmRetrieval",
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"DAPFAMAllTitlAbsClmToFullTextRetrieval",
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# IN
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"DAPFAMInTitlAbsToTitlAbsRetrieval",
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"DAPFAMInTitlAbsToTitlAbsClmRetrieval",
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"DAPFAMInTitlAbsToFullTextRetrieval",
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"DAPFAMInTitlAbsClmToTitlAbsRetrieval",
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"DAPFAMInTitlAbsClmToTitlAbsClmRetrieval",
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"DAPFAMInTitlAbsClmToFullTextRetrieval",
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# OUT
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"DAPFAMOutTitlAbsToTitlAbsRetrieval",
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"DAPFAMOutTitlAbsToTitlAbsClmRetrieval",
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"DAPFAMOutTitlAbsToFullTextRetrieval",
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"DAPFAMOutTitlAbsClmToTitlAbsRetrieval",
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"DAPFAMOutTitlAbsClmToTitlAbsClmRetrieval",
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"DAPFAMOutTitlAbsClmToFullTextRetrieval",
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]
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tasks = mteb.get_tasks(tasks=task_names)
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results = mteb.MTEB(tasks=tasks).run(
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model,
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output_folder=f"mteb_res/{model_name}",
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encode_kwargs={"batch_size": 16, "prompt_name": None}
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)
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print(results)
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```
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> 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.
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---
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### How to Load the Dataset
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```python
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from datasets import load_dataset
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dc = load_dataset("datalyes/DAPFAM_patent", "corpus") # 45,336 targets
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dq = load_dataset("datalyes/DAPFAM_patent", "queries") # 1,247 queries
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dr = load_dataset("datalyes/DAPFAM_patent", "relations") # qrels: all/in/out
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```
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**Counts**
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- Queries: **1,247**
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- Targets: **45,336**
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- Qrels (all): **≈49,869** (positives + sampled negatives)
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- Positive qrels: **IN ~19,736**, **OUT ~5,193**
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---
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### Evaluation choices
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- Metrics: **NDCG@100** (primary), **Recall@100** (secondary).
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- Document-level views in MTEB; paper also explores **passage-level** retrieval and **RRF fusion** separately.
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- Encoder: `Snowflake/snowflake-arctic-embed-m-v2.0`; in-paper runs quantized to int8 for efficiency.
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---
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### Citation
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```
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@misc{ayaou2025dapfamdomainawarefamilyleveldataset,
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title={DAPFAM: A Domain-Aware Family-level Dataset to benchmark cross domain patent retrieval},
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author={Iliass Ayaou and Denis Cavallucci and Hicham Chibane},
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year={2025},
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eprint={2506.22141},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2506.22141},
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}
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```
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