datalyes/problem2full

Problem to Full Patent Retrieval

Dataset Description

This dataset is part of PatenTEB, a comprehensive benchmark for evaluating text embedding models on patent-specific tasks. PatenTEB comprises 15 tasks across retrieval, classification, paraphrase detection, and clustering, with 2.06 million examples designed to reflect real-world patent analysis workflows.

Paper: PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding

Task Details

• Task Name: problem2full
• Task Type: Retrieval
• Test Samples: 18,729

Asymmetric retrieval task retrieving full patent documents using problem descriptions as queries. Tests whether models recognize that different patents addressing the same technical problem may use varied terminology and solutions. Fragment removal prevents trivial matching.

Dataset Structure

This is a retrieval task where models find relevant patents given a query.

Splits:

• test: Query-document pairs for retrieval evaluation

Columns:

• first_ipcr3
• q
• problem
• full_text
• first_ipcr3_count

Data Sample

Below is a 5-row preview of the test set:

first_ipcr3,q,problem,full_text,first_ipcr3_count
A23,044-552-420-254-43X,"obtain the subject composition capable of retaining freshness of meat, vegetables and fruits in refrigerators and food-preservation boxes for a lon...","gel composition for retaining freshness [SEP] . in this gel composition for retaining freshness, (a) a freshness- retaining component [a wasabi(jap...",1
A23,199-693-114-580-448,provide a soybean curd which allows a diner to enjoy the meal through an element (visual sense) other than the taste (sense of taste),"soybean curd, utensil for molding soybean curd, and method for molding soybean curd [SEP] . a soybean curd 1 has notch parts 2a to 2d formed in the...",1
A23,084-201-115-246-149,"prepare a meat-like food having juicy feeling, fibrous characteristics and close resemblance to natural meat, by mixing an o/w-type emulsion with a...","preparation of meat-like food [SEP] . an o/w-type emulsion having a compression oil-separation ratio of >=15% and composed mainly of a protein (a),...",1
A23,039-078-892-788-003,provide an application of symbiotic organisms in a therapeutic composition,"method, system and composition to reduce cholesterol using bacillus coagulans spore [SEP] . the therapeutic composition includes a combination of l...",1
A23,096-205-776-779-309,prevent fracture in a spread without modifying protein by low temp. pasteurization by mixing a water phase containing a nonmodified whey protein an...,water-in-oil type emulsion spread containing natural whey protein and preparation thereof [SEP] . a nonwhey protein by 5 to 0wt.% is mixed with 20 ...,1

Evaluation Metrics

This task uses NDCG@10 (Normalized Discounted Cumulative Gain at rank 10) as the primary metric. NDCG measures ranking quality by discounting relevance scores by logarithmic position, normalized by the ideal ranking.

Usage

Load Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("datalyes/{task_name}")

# Access test split
test_data = dataset['test']

Use with Sentence Transformers

from sentence_transformers import SentenceTransformer

# Load a patent-specialized model
model = SentenceTransformer("datalyes/patembed-base")

# Encode patent texts
embeddings = model.encode(test_data['text'])

Integrate with MTEB

This dataset is designed to be integrated with the MTEB (Massive Text Embedding Benchmark) framework. Integration with MTEB is in progress and will be available once the corresponding pull requests are accepted.

Benchmark Context

This dataset is part of a larger benchmark suite:

Benchmark Component	Description
PatenTEB	15 tasks covering retrieval, classification, paraphrase, clustering
Test Data (Released)	319,320 examples across all 15 tasks
Training/Validation Data	1.74 million examples (planned for future release)
Total Dataset Size	2.06 million annotated instances

Note: Currently, only the test split is publicly available. Training and validation data release is planned for a future date.

All 15 Tasks (NEW to MTEB):

• 3 classification tasks: Bloom timing, NLI directionality, IPC3 classification
• 2 clustering tasks: IPC-based, Inventor-based
• 8 retrieval tasks: 3 symmetric (IN/MIXED/OUT domain) + 5 asymmetric (fragment-to-full)
• 2 paraphrase tasks: Problem and solution paraphrase detection

MTEB Integration: Upcoming (PR in progress)

Citation

If you use this dataset, please cite our paper:

@misc{ayaou2025patentebcomprehensivebenchmarkmodel,
      title={PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding}, 
      author={Iliass Ayaou and Denis Cavallucci},
      year={2025},
      eprint={2510.22264},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.22264}
}

License

This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

• You are free to share and adapt the material
• You must give appropriate credit
• You may not use the material for commercial purposes
• If you remix, transform, or build upon the material, you must distribute your contributions under the same license

For full license details, see: https://creativecommons.org/licenses/by-nc-sa/4.0/

Contact

• Authors: Iliass Ayaou, Denis Cavallucci
• Institution: ICUBE Laboratory, INSA Strasbourg
• GitHub: github.com/iliass-y/patenteb
• HuggingFace: huggingface.co/datalyes