metadata

annotations_creators:
  - human-annotated
language:
  - deu
license: mit
multilinguality: monolingual
source_datasets:
  - aari1995/false_friends_de_en_mteb
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: test
      num_bytes: 86442
      num_examples: 1524
  download_size: 41143
  dataset_size: 86442
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
tags:
  - mteb
  - text

FalseFriendsGermanEnglish

An MTEB dataset

Massive Text Embedding Benchmark

A dataset to identify False Friends / false cognates between English and German. A generally challenging task for multilingual models.


Task category	t2t
Domains	Written
Reference	https://drive.google.com/file/d/1jgq0nBnV-UiYNxbKNrrr2gxDEHm-DMKH/view?usp=share_link

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(["FalseFriendsGermanEnglish"])
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.


@misc{Chibb_2022,
  abstract = {{This paper explores the robustness of multilingual language models against false friends. False friends are words that sound or are written the same in two different languages but have different meaning. Generally, it is argued that multilingual models, such as XLM-RoBERTA, can outperform monolingual models in most tasks on conventional datasets. However, false friends are not considered in these tests. In this paper, experiments with a false friends dataset show that multilingual models are not robust against false friends; they have problems creating monolingual representations and differentiating between meanings of similarly written words in different languages. An attempt of word-based finetuning multilingual models on false friends pairs is promising, however the results do not generally solve the presented problem and still, monolingual models are more robust against false friends.}},
  author = {Chibb, Aaron},
  month = {Sep},
  title = {{German-English False Friends in Multilingual Transformer Models: An Evaluation on Robustness and Word-to-Word Fine-Tuning}},
  year = {2022},
}


@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("FalseFriendsGermanEnglish")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 1524,
        "number_of_characters": 61254,
        "unique_pairs": 1502,
        "min_sentence1_length": 3,
        "avg_sentence1_length": 14.548556430446194,
        "max_sentence1_length": 63,
        "unique_sentence1": 489,
        "min_sentence2_length": 3,
        "avg_sentence2_length": 25.644356955380577,
        "max_sentence2_length": 72,
        "unique_sentence2": 986,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 762
            },
            "0": {
                "count": 762
            }
        }
    }
}

This dataset card was automatically generated using MTEB