Datasets:
metadata
annotations_creators:
- human-annotated
language:
- deu
- eng
- jpn
license: cc-by-4.0
multilinguality: multilingual
task_categories:
- text-classification
task_ids: []
dataset_info:
- config_name: de
features:
- name: text
dtype: string
- name: label
dtype: int32
- name: label_text
dtype: string
splits:
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- name: validation
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num_examples: 463
download_size: 564711
dataset_size: 981044.6496819437
- config_name: en
features:
- name: text
dtype: string
- name: label
dtype: int32
- name: label_text
dtype: string
splits:
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- name: validation
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- name: test
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download_size: 354903
dataset_size: 641553
- config_name: en-ext
features:
- name: text
dtype: string
- name: label
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dtype: string
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- config_name: ja
features:
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dtype: string
- name: label
dtype: int32
- name: label_text
dtype: string
splits:
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- name: test
num_bytes: 1723.6244635193134
num_examples: 11
download_size: 32658
dataset_size: 25935.1074984672
configs:
- config_name: de
data_files:
- split: train
path: de/train-*
- split: validation
path: de/validation-*
- split: test
path: de/test-*
- config_name: en
data_files:
- split: train
path: en/train-*
- split: validation
path: en/validation-*
- split: test
path: en/test-*
- config_name: en-ext
data_files:
- split: train
path: en-ext/train-*
- split: validation
path: en-ext/validation-*
- split: test
path: en-ext/test-*
- config_name: ja
data_files:
- split: train
path: ja/train-*
- split: validation
path: ja/validation-*
- split: test
path: ja/test-*
tags:
- mteb
- text
A collection of Amazon customer reviews annotated for counterfactual detection pair classification.
| Task category | t2c |
| Domains | Reviews, Written |
| Reference | https://arxiv.org/abs/2104.06893 |
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(["AmazonCounterfactualClassification"])
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{oneill-etal-2021-wish,
abstract = {Counterfactual statements describe events that did not or cannot take place. We consider the problem of counterfactual detection (CFD) in product reviews. For this purpose, we annotate a multilingual CFD dataset from Amazon product reviews covering counterfactual statements written in English, German, and Japanese languages. The dataset is unique as it contains counterfactuals in multiple languages, covers a new application area of e-commerce reviews, and provides high quality professional annotations. We train CFD models using different text representation methods and classifiers. We find that these models are robust against the selectional biases introduced due to cue phrase-based sentence selection. Moreover, our CFD dataset is compatible with prior datasets and can be merged to learn accurate CFD models. Applying machine translation on English counterfactual examples to create multilingual data performs poorly, demonstrating the language-specificity of this problem, which has been ignored so far.},
address = {Online and Punta Cana, Dominican Republic},
author = {O{'}Neill, James and
Rozenshtein, Polina and
Kiryo, Ryuichi and
Kubota, Motoko and
Bollegala, Danushka},
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
doi = {10.18653/v1/2021.emnlp-main.568},
editor = {Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau},
month = nov,
pages = {7092--7108},
publisher = {Association for Computational Linguistics},
title = {{I} Wish {I} Would Have Loved This One, But {I} Didn{'}t {--} A Multilingual Dataset for Counterfactual Detection in Product Review},
url = {https://aclanthology.org/2021.emnlp-main.568},
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("AmazonCounterfactualClassification")
desc_stats = task.metadata.descriptive_stats
{
"validation": {
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"max_text_length": 525,
"unique_texts": 1903,
"min_labels_per_text": 1,
"average_label_per_text": 1.0,
"max_labels_per_text": 1,
"unique_labels": 2,
"labels": {
"0": {
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},
"1": {
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}
},
"hf_subset_descriptive_stats": {
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"min_labels_per_text": 1,
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"labels": {
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},
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}
}
},
"en": {
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"labels": {
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},
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}
}
},
"de": {
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},
"ja": {
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},
"test": {
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},
"hf_subset_descriptive_stats": {
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}
}
}
}
}
This dataset card was automatically generated using MTEB