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---
language:
- de
- en
- ja
dataset_info:
- config_name: de
features:
- name: text
dtype: string
- name: label
dtype: int32
- name: label_text
dtype: string
splits:
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num_examples: 5600
- name: validation
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num_examples: 466
- name: test
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num_examples: 934
download_size: 610356
dataset_size: 1054383
- 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: 382768
dataset_size: 685860
- config_name: en-ext
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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dataset_size: 1316317
- config_name: ja
features:
- name: text
dtype: string
- name: label
dtype: int32
- name: label_text
dtype: string
splits:
- name: train
num_bytes: 862548
num_examples: 5600
- name: validation
num_bytes: 73019
num_examples: 466
- name: test
num_bytes: 143450
num_examples: 934
download_size: 564439
dataset_size: 1079017
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-*
default: true
- 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-*
---
# Amazon Multilingual Counterfactual Dataset
The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form – If p was true, then q would be true (i.e. assertions whose antecedent (p) and consequent (q) are known or assumed to be false).
The key features of this dataset are:
* The dataset is multilingual and contains sentences in English, German, and Japanese.
* The labeling was done by professional linguists and high quality was ensured.
* The dataset is supplemented with the annotation guidelines and definitions, which were worked out by professional linguists. We also provide the clue word lists, which are typical for counterfactual sentences and were used for initial data filtering. The clue word lists were also compiled by professional linguists.
Please see the [paper](https://arxiv.org/abs/2104.06893) for the data statistics, detailed description of data collection and annotation.
GitHub repo URL: https://github.com/amazon-research/amazon-multilingual-counterfactual-dataset
## Usage
You can load each of the languages as follows:
```
from datasets import get_dataset_config_names
dataset_id = "SetFit/amazon_counterfactual"
# Returns ['de', 'en', 'en-ext', 'ja']
configs = get_dataset_config_names(dataset_id)
# Load English subset
dset = load_dataset(dataset_id, name="en")
```