Update README.md

README.md

@@ -44,4 +44,63 @@ configs:
   - 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](https://www.cooperativepatentclassification.org/home)) 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](https://worldwide.espacenet.com/patent/search?q=EP4030126A1) or the [EPO’s Open Patent Services](https://www.epo.org/en/searching-for-patents/data/web-services/ops)
+- `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:
+```python
+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:
+```python
+from datasets import load_dataset
+dataset = load_dataset("mwirth-epo/cpc-classification-data", name="main_group")
+```
+
+
+## Citation
+
+**BibTeX:**
+```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