Datasets:

mteb
/

TERRa

annotations_creators:
  - human-annotated
language:
  - rus
license: mit
multilinguality: monolingual
source_datasets:
  - ai-forever/terra-pairclassification
task_categories:
  - text-classification
task_ids:
  - semantic-similarity-classification
dataset_info:
  features:
    - name: sentence1
      dtype: string
    - name: sentence2
      dtype: string
    - name: labels
      dtype: int64
  splits:
    - name: train
      num_bytes: 1398771
      num_examples: 2616
    - name: dev
      num_bytes: 160249
      num_examples: 307
  download_size: 744340
  dataset_size: 1559020
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
tags:
  - mteb
  - text

TERRa

An MTEB dataset

Massive Text Embedding Benchmark

Textual Entailment Recognition for Russian. This task requires to recognize, given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text.


Task category	t2t
Domains	News, Web, Written
Reference	https://arxiv.org/pdf/2010.15925

How to evaluate on this task

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

import mteb

task = mteb.get_task("TERRa")
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 repository.

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.


@article{shavrina2020russiansuperglue,
  author = {Shavrina, Tatiana
and Fenogenova, Alena
and Emelyanov, Anton
and Shevelev, Denis
and Artemova, Ekaterina
and Malykh, Valentin
and Mikhailov, Vladislav
and Tikhonova, Maria
and Chertok, Andrey
and Evlampiev, Andrey},
  journal = {arXiv preprint arXiv:2010.15925},
  title = {RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark},
  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, Loïc 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("TERRa")

desc_stats = task.metadata.descriptive_stats

{}

This dataset card was automatically generated using MTEB