Datasets:
Tasks:
Text Classification
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
License:
"""TODO(x_stance): Add a description here.""" | |
import json | |
import os | |
import datasets | |
# TODO(x_stance): BibTeX citation | |
_CITATION = """\ | |
@inproceedings{vamvas2020xstance, | |
author = "Vamvas, Jannis and Sennrich, Rico", | |
title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection", | |
booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \\& 16th Conference on Natural Language Processing (KONVENS)", | |
address = "Zurich, Switzerland", | |
year = "2020", | |
month = "jun", | |
url = "http://ceur-ws.org/Vol-2624/paper9.pdf" | |
} | |
""" | |
# TODO(x_stance): | |
_DESCRIPTION = """\ | |
The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. | |
It can be used to train and evaluate stance detection systems. | |
""" | |
_URL = "https://github.com/ZurichNLP/xstance/raw/v1.0.0/data/xstance-data-v1.0.zip" | |
class XStance(datasets.GeneratorBasedBuilder): | |
"""TODO(x_stance): Short description of my dataset.""" | |
# TODO(x_stance): Set up version. | |
VERSION = datasets.Version("0.1.0") | |
def _info(self): | |
# TODO(x_stance): Specifies the datasets.DatasetInfo object | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# datasets.features.FeatureConnectors | |
features=datasets.Features( | |
{ | |
"question": datasets.Value("string"), | |
"id": datasets.Value("int32"), | |
"question_id": datasets.Value("int32"), | |
"language": datasets.Value("string"), | |
"comment": datasets.Value("string"), | |
"label": datasets.Value("string"), | |
"numerical_label": datasets.Value("int32"), | |
"author": datasets.Value("string"), | |
"topic": datasets.Value("string") | |
# These are the features of your dataset like images, labels ... | |
} | |
), | |
# If there's a common (input, target) tuple from the features, | |
# specify them here. They'll be used if as_supervised=True in | |
# builder.as_dataset. | |
supervised_keys=None, | |
# Homepage of the dataset for documentation | |
homepage="https://github.com/ZurichNLP/xstance", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
# TODO(x_stance): Downloads the data and defines the splits | |
# dl_manager is a datasets.download.DownloadManager that can be used to | |
# download and extract URLs | |
dl_dir = dl_manager.download_and_extract(_URL) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(dl_dir, "train.jsonl")}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(dl_dir, "test.jsonl")}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(dl_dir, "valid.jsonl")}, | |
), | |
] | |
def _generate_examples(self, filepath): | |
"""Yields examples.""" | |
# TODO(x_stance): Yields (key, example) tuples from the dataset | |
with open(filepath, encoding="utf-8") as f: | |
for id_, row in enumerate(f): | |
data = json.loads(row) | |
yield id_, { | |
"id": data["id"], | |
"question_id": data["question_id"], | |
"question": data["question"], | |
"comment": data["comment"], | |
"label": data["label"], | |
"author": data["author"], | |
"numerical_label": data["numerical_label"], | |
"topic": data["topic"], | |
"language": data["language"], | |
} | |