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 = {
"train": "https://huggingface.co/datasets/hezarai/sentiment-dksf/raw/main/sentiment_dksf_train.csv",
"test": "https://huggingface.co/datasets/hezarai/sentiment-dksf/raw/main/sentiment_dksf_test.csv"
}
class SentimentDKSF(datasets.GeneratorBasedBuilder):
"""Sentiment analysis on Digikala/SnappFood comments"""
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-dksf",
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate examples."""
label_mapping = {"negative": 0, "positive": 1, "neutral": 2}
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file, quotechar='"', skipinitialspace=True
)
for id_, row in enumerate(csv_reader):
text, label = row
label = label_mapping[label]
yield id_, {"text": text, "label": label}
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