Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
natural-language-inference
Languages:
Catalan
Size:
1K - 10K
ArXiv:
License:
File size: 3,032 Bytes
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# Loading script for the TECA dataset.
import json
import datasets
logger = datasets.logging.get_logger(__name__)
# _CITATION = ""
_DESCRIPTION = """
XNLI-ca is the Catalan professional translation of the development and test partitions of the XNLI dataset, which contain 2490 and 5010 pairs of premises and hypotheses, respectively. This dataset was translated as part of the AINA project.
"""
_HOMEPAGE = """https://zenodo.org/record/4621378"""
_URL = "https://huggingface.co/datasets/projecte-aina/xnli-ca/resolve/main/"
_DEV_FILE = "xnli.dev.ca.json"
_TEST_FILE = "xnli.test.ca.json"
class xnliConfig(datasets.BuilderConfig):
""" Builder config for the XNLI-ca dataset """
def __init__(self, **kwargs):
"""BuilderConfig for XNLI-ca.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(xnliConfig, self).__init__(**kwargs)
class xnli(datasets.GeneratorBasedBuilder):
""" XNLI-ca Dataset """
BUILDER_CONFIGS = [
xnliConfig(
name="xnli-ca",
version=datasets.Version("1.0.1"),
description="XNLI-ca dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.features.ClassLabel
(names=
[
"entailment",
"neutral",
"contradiction"
]
),
}
),
homepage=_HOMEPAGE,
# citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data_dict = json.load(f)
for id_, article in enumerate(data_dict):
premise = article["premise"]
hypothesis = article["hypothesis"]
label = article["label"]
yield id_, {
"premise": premise,
"hypothesis": hypothesis,
"label": label,
}
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