Datasets:

mwirth-epo
/

cpc-classification-data

dataset_info:
  - config_name: main_group
    features:
      - name: publication_number
        dtype: string
      - name: labels
        sequence: string
    splits:
      - name: train
        num_bytes: 369885895
        num_examples: 7491648
      - name: test
        num_bytes: 43089767
        num_examples: 832405
    download_size: 163987062
    dataset_size: 412975662
  - config_name: subgroup
    features:
      - name: publication_number
        dtype: string
      - name: labels
        sequence: string
    splits:
      - name: train
        num_bytes: 693083457
        num_examples: 7492144
      - name: test
        num_bytes: 80785020
        num_examples: 832461
    download_size: 399887593
    dataset_size: 773868477
configs:
  - config_name: main_group
    data_files:
      - split: train
        path: main_group/train-*
      - split: test
        path: main_group/test-*
  - config_name: subgroup
    data_files:
      - split: train
        path: subgroup/train-*
      - split: test
        path: subgroup/test-*
    default: true
license: cc-by-sa-4.0
task_categories:
  - text-classification
tags:
  - legal
pretty_name: CPC classification datasets
size_categories:
  - 1M<n<10M

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