README.md

metadata

language:
  - de
  - en
  - ja
dataset_info:
  - config_name: de
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int32
      - name: label_text
        dtype: string
    splits:
      - name: train
        num_bytes: 839355
        num_examples: 5600
      - name: validation
        num_bytes: 72051
        num_examples: 466
      - name: test
        num_bytes: 142977
        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:
      - name: train
        num_bytes: 548743
        num_examples: 4018
      - name: validation
        num_bytes: 46405
        num_examples: 335
      - name: test
        num_bytes: 90712
        num_examples: 670
    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:
      - name: train
        num_bytes: 1053699
        num_examples: 8000
      - name: validation
        num_bytes: 87748
        num_examples: 666
      - name: test
        num_bytes: 174870
        num_examples: 1334
    download_size: 731478
    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 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")