datalyes/title2full

Title 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: title2full
• Task Type: Retrieval
• Test Samples: 18,727

Asymmetric retrieval task evaluating whether models can bridge the semantic gap between concise titles and full technical disclosures. A common entry point for patent searches. Fragment removal prevents lexical 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
• title
• full_text
• first_ipcr3_count

Data Sample

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

first_ipcr3,q,title,full_text,first_ipcr3_count
A23,157-316-376-370-590,method for producing rugosa rose extract-containing beverage,"provide a food composition containing a rugosa rose extract, and a method for producing the same. provided is a method for producing a rugosa rose ...",1
A23,022-175-548-510-544,"boiled and seasoned peanuts with shell, and method for preparation thereof","obtain seasoned boiled peanuts inside the shells, using mature fresh peanuts having the shells as raw material, suitable as a snack, relish, etc., ...",1
A23,138-718-020-747-409,"method for producing powder for supplementary food, and supplementary food",provide a supplementary food for adsorbing and holding a large amount of hydrogen molecules using a shell or a pearl containing conchiolin (protein...,1
A23,062-522-141-635-526,method for preserving heat-cooked food and vacuum-sealed preserving vessel,provide a method for safely preserving food in which an accident due to food can be avoided even if there is a secondary pollution due to bacteria ...,1
A23,173-670-246-413-224,frozen confectionery product comprising inclusions and method of producing the same,"food-processing industry, in particular, ice-cream production. frozen confectionery product, such as water ice-cream or sorbet, comprises 2-16 indi...",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