Datasets:
File size: 3,396 Bytes
4e5209b 04f80d3 4e5209b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
"""Turkish review multi-classification dataset."""
import csv
import datasets
from datasets.tasks import TextClassification
_CITATION = """\
----ArabicNLPDataset----
"""
_DESCRIPTION = """\
The dataset, prepared in Arabic, includes 10.000 tests, 10.000 validations and 80000 train data.
The data is composed of customer comments and created from e-commerce sites.
"""
_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/BihterDass/ArabicTextClassificationDataset/main/train.csv"
_VALIDATION_DOWNLOAD_URL ="https://raw.githubusercontent.com/BihterDass/ArabicTextClassificationDataset/main/dev.csv"
_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/BihterDass/ArabicTextClassificationDataset/main/test.csv"
class ArabicNLPDatasetConfig(datasets.BuilderConfig):
"""BuilderConfig for ArabicNLPDataset Config"""
def __init__(self, **kwargs):
"""BuilderConfig for ArabicNLPDatasetConfig
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(ArabicNLPDatasetConfig, self).__init__(**kwargs)
class ArabicNLPDataset(datasets.GeneratorBasedBuilder):
"""ArabicNLPDataset Classification dataset."""
BUILDER_CONFIGS = [
ArabicNLPDatasetConfig(
name="arabicData",
version=datasets.Version("1.0.0"),
description="ArabicNLPDataset: It is a classification study that will contribute to natural language processing operations.",
),
]
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.ClassLabel(names=["neg", "nor","pos"]),
}
),
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://github.com/BihterDass/turkish-nlp-dataset",
citation=_CITATION,
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
validation_path = dl_manager.download_and_extract(_VALIDATION_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": validation_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file,
delimiter=",",
quoting=csv.QUOTE_ALL,
skipinitialspace=True,
)
for id_, row in enumerate(csv_reader):
(
text,
label,
) = row
yield id_, {
"text": text,
"label": int(label),
} |