mwirth-epo/cpc-classification-data

CPC classification datasets

These datasets have been used to train the CPC (Cooperative Patent Classification) classification models mentioned in the article Hähnke, V. D., Wéry, A., Wirth, M., & Klenner-Bajaja, A. (2025). Encoder models at the European Patent Office: Pre-training and use cases. World Patent Information, 81, 102360. https://doi.org/10.1016/j.wpi.2025.102360.

Columns:

• publication_number: the patent publication number, the content of the publication can be looked up using e.g. Espacenet or the EPO’s Open Patent Services
• labels: the CPC symbols used as prediction labels (CPC release 2024.01)

Datasets

Subgroup dataset

Used to train the subgroup model with 224 542 labels.

How to load the dataset:

from datasets import load_dataset
dataset = load_dataset("mwirth-epo/cpc-classification-data", name="subgroup")

Main group dataset

Used to train the main group model with 9 025 labels.

This dataset was created from the subgroup dataset with a filter excluding main groups with less than 20 documents.

How to load the dataset:

from datasets import load_dataset
dataset = load_dataset("mwirth-epo/cpc-classification-data", name="main_group")

Citation

BibTeX:

@article{HAHNKE2025102360,
title = {Encoder models at the European Patent Office: Pre-training and use cases},
journal = {World Patent Information},
volume = {81},
pages = {102360},
year = {2025},
issn = {0172-2190},
doi = {https://doi.org/10.1016/j.wpi.2025.102360},
url = {https://www.sciencedirect.com/science/article/pii/S0172219025000274},
author = {Volker D. Hähnke and Arnaud Wéry and Matthias Wirth and Alexander Klenner-Bajaja},
keywords = {Natural language processing, Language model, Encoder network, Classification, Cooperative Patent Classification}
}

APA:

Hähnke, V. D., Wéry, A., Wirth, M., & Klenner-Bajaja, A. (2025). Encoder models at the European Patent Office: Pre-training and use cases. World Patent Information, 81, 102360. https://doi.org/10.1016/j.wpi.2025.102360