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UTokyo-Yokoya-Lab/webis-touche2020-v3-CSR-L

Touche2020-v3-CodeSwitching An MTEB dataset Massive Text Embedding Benchmark Code-switching version of mteb/webis-touche2020-v3, with queries rewritten in Chinese-English and Japanese-English code-switching styles. Dataset Structure The dataset contains the following configurations: From original dataset (unchanged): corpus: Original corpus documents default: Original relevance judgments (qrels) Code-switching additions: queries_zh_en: Chinese-English code-switching… See the full description on the dataset page: https://huggingface.co/datasets/UTokyo-Yokoya-Lab/webis-touche2020-v3-CSR-L.

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Dataset Card

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">Touche2020-v3-CodeSwitching</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div>

Code-switching version of mteb/webis-touche2020-v3, with queries rewritten in Chinese-English and Japanese-English code-switching styles.

Dataset Structure

The dataset contains the following configurations:

From original dataset (unchanged):

  • —corpus: Original corpus documents
  • —default: Original relevance judgments (qrels)

Code-switching additions:

  • —queries_zh_en: Chinese-English code-switching queries
  • —queries_ja_en: Japanese-English code-switching queries

Usage

python
from datasets import load_dataset

# Load code-switching queries
queries_zh = load_dataset("UTokyo-Yokoya-Lab/webis-touche2020-v3-codeswitching", "queries_zh_en")
queries_ja = load_dataset("UTokyo-Yokoya-Lab/webis-touche2020-v3-codeswitching", "queries_ja_en")

# Load original configs
corpus = load_dataset("UTokyo-Yokoya-Lab/webis-touche2020-v3-codeswitching", "corpus")
qrels = load_dataset("UTokyo-Yokoya-Lab/webis-touche2020-v3-codeswitching", "default")

Attribution

Based on mteb/webis-touche2020-v3 (MIT License).

Citation

If you use this dataset, please also cite the original:

bibtex
@inproceedings{bondarenko2020overview,
  author = {Alexander Bondarenko and Maik Fr\"{o}be and Meriem Beloucif and Lukas Gienapp and Yamen Ajjour and Alexander Panchenko and Chris Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen},
  booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction. 11th International Conference of the CLEF Association (CLEF 2020)},
  doi = {10.1007/978-3-030-58219-7\_26},
  pages = {384--395},
  title = {Overview of Touch\'{e} 2020: Argument Retrieval},
  year = {2020},
}

@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and others},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi={10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo\"{\i}c and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022},
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}