datalyes/para_solution

Patent Solution Paraphrase Detection

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: para_solution
• Task Type: Sts
• Test Samples: 18,656

Paraphrase detection for patent solution descriptions. Similar construction to problem paraphrase but focuses on technical solutions. Tests whether models can identify semantically equivalent solutions despite varying technical vocabulary.

Dataset Structure

This is a semantic textual similarity (STS) task for paraphrase detection.

Splits:

• test: Text pairs with similarity scores

Columns:

• first_ipcr3
• label
• q
• t
• text1
• text2
• first_ipcr3_count

Data Sample

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

first_ipcr3,label,q,t,text1,text2,first_ipcr3_count
A43,0,039-548-829-916-758,102-270-575-173-972,the fitting system for fitting crampons 1 to a mountaineering boot 2 has a sole the fitting system has at least one fitting sheet 6 formed at the s...,"the mobile telephone comprises a first memory 1, a second memory 2, a first cpu 3, a second cpu 4, an interface 5, an antenna 6, a battery 7, and a...",1
A43,0,054-575-120-834-568,095-877-460-725-49X,"a shoe includes an upper, a midsole 14, and an outsole 60, the structured midsole can accommodate both spiked outsoles 60 and spikeless outsoles th...","a speed correction quantity, such that it cancels the rotary speed change of a rotating hook driving shaft due to its change of torque, is prelimin...",1
A43,0,125-621-127-819-350,127-022-801-729-612,"the sports boot 1 comprising an outer shell 2 and the inner boot 3 into which it is inserted contains at least one opening 9, 10, 11, 25 in the out...",an ultrasonic wave generator consisting of two piezoelectric ceramic elements 4 in contact and continuity on the same axis and of a counter mass 5 ...,1
A43,0,024-073-786-048-581,040-493-692-192-153,"an adhesive coating device of a shoe material includes: a rotary frame unit 2 having a revolving shaft 21, a first rotary seat 31 rotating together...",a word recognition level and a learning change degree of a user are first determined (s20). a comprehension aid which is associated with the word c...,1
A43,0,138-464-905-692-84X,142-102-749-129-022,"a shoe equipped with a sole and an impact absorbing member comprises an elastic member(140), a first fixture(135), a second fixture(137), and a sup...","one or two or more kinds of substances selected from ethylene- -olefin coorigomers, -olefin oils, refined mineral oils and polybutenes are used as ...",1

Evaluation Metrics

This task uses Pearson correlation between predicted cosine similarities and binary labels as the primary metric. This measures how well the model's similarity scores align with paraphrase judgments.

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