InappropriatenessClassification

An MTEB dataset

Massive Text Embedding Benchmark

Inappropriateness identification in the form of binary classification

Task category	t2c
Domains	Web, Social, Written
Reference	https://aclanthology.org/2021.bsnlp-1.4

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(["InappropriatenessClassification"])
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{babakov-etal-2021-detecting,
  abstract = {Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.},
  address = {Kiyv, Ukraine},
  author = {Babakov, Nikolay  and
Logacheva, Varvara  and
Kozlova, Olga  and
Semenov, Nikita  and
Panchenko, Alexander},
  booktitle = {Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing},
  editor = {Babych, Bogdan  and
Kanishcheva, Olga  and
Nakov, Preslav  and
Piskorski, Jakub  and
Pivovarova, Lidia  and
Starko, Vasyl  and
Steinberger, Josef  and
Yangarber, Roman  and
Marci{\'n}czuk, Micha{\l}  and
Pollak, Senja  and
P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
Robnik-{\v{S}}ikonja, Marko},
  month = apr,
  pages = {26--36},
  publisher = {Association for Computational Linguistics},
  title = {Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation},
  url = {https://aclanthology.org/2021.bsnlp-1.4},
  year = {2021},
}


@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

Dataset Statistics

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

import mteb

task = mteb.get_task("InappropriatenessClassification")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 2048,
        "number_of_characters": 198775,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 8,
        "average_text_length": 97.05810546875,
        "max_text_length": 1168,
        "unique_text": 2048,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 1024
            },
            "0": {
                "count": 1024
            }
        }
    },
    "train": {
        "num_samples": 40000,
        "number_of_characters": 3878368,
        "number_texts_intersect_with_train": null,
        "min_text_length": 8,
        "average_text_length": 96.9592,
        "max_text_length": 1938,
        "unique_text": 40000,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 20000
            },
            "0": {
                "count": 20000
            }
        }
    }
}

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This dataset card was automatically generated using MTEB