slvnwhrl/tenkgnad-clustering-s2s

This dataset can be used as a benchmark for clustering word embeddings for German.

The datasets contains news article titles and is based on the dataset of the One Million Posts Corpus and 10kGNAD. It contains 10'267 unique samples, 10 splits with 1'436 to 9'962 samples and 9 unique classes. Splits are built similarly to MTEB's TwentyNewsgroupsClustering.

Have a look at German Text Embedding Clustering Benchmark (Github, Paper) for more infos, datasets and evaluation results.

If you use this dataset in your work, please cite the following paper:

@inproceedings{wehrli-etal-2023-german,
    title = "{G}erman Text Embedding Clustering Benchmark",
    author = "Wehrli, Silvan  and
      Arnrich, Bert  and
      Irrgang, Christopher",
    editor = "Georges, Munir  and
      Herygers, Aaricia  and
      Friedrich, Annemarie  and
      Roth, Benjamin",
    booktitle = "Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)",
    month = sep,
    year = "2023",
    address = "Ingolstadt, Germany",
    publisher = "Association for Computational Lingustics",
    url = "https://aclanthology.org/2023.konvens-main.20",
    pages = "187--201",
}