datalyes/clusters_inventor

Patent Clustering by Inventor

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: clusters_inventor
• Task Type: Clustering
• Test Samples: 86,834

Clustering task grouping patent families by inventor identifiers, with clusters of size 100-1000 retained. This tests whether representations encode author identity signals correlating with research trajectories and collaboration networks.

Dataset Structure

This is a clustering task where models group similar patents together.

Splits:

• test: Test set for clustering evaluation

Columns:

• q
• text
• cluster_id

Data Sample

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

q,text,cluster_id
000-133-212-134-908,"reduced lens heating methods, apparatus, and systems [SEP] in one embodiment, a system is disclosed that includes an illuminator having a source th...",527
000-328-253-538-423,methods and apparatus for reuse optimization of a data storage process using an ordered structure [SEP] techniques for reducing a number of computa...,98
001-396-805-749-407,mram with sidewall protection and method of fabrication [SEP] beol memory cells are described that include one or more sidewall protection layers o...,396
001-943-995-960-886,techniques for harq retransmission skipping [SEP] the disclosure provides for selectively utilizing an inactive mode for saving power during wirele...,219
002-213-521-004-667,method and apparatus for transmitting pilot on multiple antennas [SEP] provide a method and apparatus for transmitting a pilot on multiple antennas...,551

Evaluation Metrics

This task uses V-measure as the primary metric, which is the harmonic mean of homogeneity and completeness. Clustering is performed using MiniBatchKMeans with the ground-truth cluster count.

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