metadata

language:
  - cmn
multilinguality: monolingual
task_categories:
  - text-classification
task_ids:
  - semantic-similarity-classification
dataset_info:
  features:
    - name: sentence1
      sequence: string
    - name: sentence2
      sequence: string
    - name: labels
      sequence: int64
  splits:
    - name: validation
      num_bytes: 1349125
      num_examples: 1
  download_size: 662625
  dataset_size: 1349125
configs:
  - config_name: default
    data_files:
      - split: validation
        path: data/validation-*
tags:
  - mteb
  - text

Cmnli

An MTEB dataset

Massive Text Embedding Benchmark

Chinese Multi-Genre NLI


Task category	t2t
Domains	None
Reference	https://huggingface.co/datasets/clue/viewer/cmnli

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["Cmnli"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repitory.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.


@inproceedings{xu-etal-2020-clue,
  address = {Barcelona, Spain (Online)},
  author = {Xu, Liang  and
Hu, Hai  and
Zhang, Xuanwei  and
Li, Lu  and
Cao, Chenjie  and
Li, Yudong  and
Xu, Yechen  and
Sun, Kai  and
Yu, Dian  and
Yu, Cong  and
Tian, Yin  and
Dong, Qianqian  and
Liu, Weitang  and
Shi, Bo  and
Cui, Yiming  and
Li, Junyi  and
Zeng, Jun  and
Wang, Rongzhao  and
Xie, Weijian  and
Li, Yanting  and
Patterson, Yina  and
Tian, Zuoyu  and
Zhang, Yiwen  and
Zhou, He  and
Liu, Shaoweihua  and
Zhao, Zhe  and
Zhao, Qipeng  and
Yue, Cong  and
Zhang, Xinrui  and
Yang, Zhengliang  and
Richardson, Kyle  and
Lan, Zhenzhong},
  booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
  doi = {10.18653/v1/2020.coling-main.419},
  month = dec,
  pages = {4762--4772},
  publisher = {International Committee on Computational Linguistics},
  title = {{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark},
  url = {https://aclanthology.org/2020.coling-main.419},
  year = {2020},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  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},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("Cmnli")

desc_stats = task.metadata.descriptive_stats

{
    "validation": {
        "num_samples": 8315,
        "number_of_characters": 426122,
        "unique_pairs": 8312,
        "min_sentence1_length": 2,
        "avg_sentence1_length": 34.50847865303668,
        "max_sentence1_length": 135,
        "unique_sentence1": 4132,
        "min_sentence2_length": 2,
        "avg_sentence2_length": 16.738905592303066,
        "max_sentence2_length": 89,
        "unique_sentence2": 8305,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 4277
            },
            "0": {
                "count": 4038
            }
        }
    }
}

This dataset card was automatically generated using MTEB