Datasets:
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import csv
import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """\
Sentiment analysis dataset extracted and labeled from Digikala and Snapp Food comments
"""
_DOWNLOAD_URLS = [
"https://huggingface.co/datasets/hezar-ai/sentiment_digikala_snappfood/blob/main/sentiment_digikala_snappfood_train.csv",
"https://huggingface.co/datasets/hezar-ai/sentiment_digikala_snappfood/blob/main/sentiment_digikala_snappfood_test.csv"
]
class SentimentDigikalaSnappfoodConfig(datasets.BuilderConfig):
"""BuilderConfig for SentimentMixedV1"""
def __init__(self, **kwargs):
"""BuilderConfig for SentimentMixedV1.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SentimentDigikalaSnappfoodConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class SentimentDigikalaSnappfood(datasets.GeneratorBasedBuilder):
"""Sentiment analysis on Digikala/SnappFood comments"""
BUILDER_CONFIGS = [
SentimentDigikalaSnappfoodConfig(
name="plain_text",
description="Plain text",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["negative", "positive", "neutral"])}
),
supervised_keys=None,
homepage="https://huggingface.co/datasets/hezar-ai/sentiment_digikala_snappfood",
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(_DOWNLOAD_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
),
]
def _generate_examples(self, filepath):
"""Generate examples."""
# For labeled examples, extract the label from the path.
label_mapping = {"negative": 0, "positive": 1, "neutral": 2}
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 = label_mapping[label]
yield id_, {"text": text, "label": label} |