metadata

license: cc-by-4.0

Touché23-ValueEval

link

Usage:

from datasets import load_dataset
import ast

def convert_labels(example):
    example["Labels"] = [i for i in ast.literal_eval(example["Labels"])]
    return example

valueeval23 = load_dataset("webis/Touche23-ValueEval")

training_dataset = valueeval23["training"].map(convert_labels)

See available dataset parts:

valueeval23

The Labels for each example is an array of 1s (argument resorts to value) and 0s (argument does not resort to value). The order is the same as in the original files:

labels = ["Self-direction: thought", "Self-direction: action", "Stimulation", "Hedonism", "Achievement", "Power: dominance", "Power: resources", "Face", "Security: personal", "Security: societal", "Tradition", "Conformity: rules", "Conformity: interpersonal", "Humility", "Benevolence: caring", "Benevolence: dependability", "Universalism: concern", "Universalism: nature", "Universalism: tolerance", "Universalism: objectivity"]