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"""Custom classification dataset."""
import csv
import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """\
"""
_CITATION = """\
"""
_TRAIN_DOWNLOAD_URL = "train.csv"
_TEST_DOWNLOAD_URL = "test.csv"
_DATASET_MAP = {"NEGATIVE": 0, "POSITIVE": 1}
class Custom(datasets.GeneratorBasedBuilder):
"""Custom classification dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(
names=["NEGATIVE", "POSITIVE"]
),
}
),
homepage="",
citation=_CITATION,
task_templates=[
TextClassification(text_column="text", label_column="label")
],
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": test_path}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
),
]
def _generate_examples(self, filepath):
"""Generate Custom examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file,
quotechar='"',
delimiter=",",
quoting=csv.QUOTE_ALL,
skipinitialspace=True,
)
for id_, row in enumerate(csv_reader):
text, label = row
label = _DATASET_MAP[label]
yield id_, {"text": text, "label": label}
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