Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
sentiment-classification
Languages:
Russian
Size:
10K - 100K
License:
File size: 1,692 Bytes
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import datasets
import pandas as pd
class KinopoiskReviewsConfig(datasets.BuilderConfig):
def __init__(self, features, **kwargs):
super(KinopoiskReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.features = features
class Kinopoisk(datasets.GeneratorBasedBuilder):
#BUILDER_CONFIGS = [
# KinopoiskReviewsConfig(
# name="simple",
# description="Simple config",
# features=["content", "title", "grade3", "movie_name", "part", "review_id", "author", "date", "grade10"],
# )
#]
def _info(self):
return datasets.DatasetInfo(
description='Kinopoisk movie reviews dataset.',
features=datasets.Features(
{
"content": datasets.Value("string"),
"title": datasets.Value("string"),
"grade3": datasets.Value("string"),
"movie_name": datasets.Value("string"),
"part": datasets.Value("string"),
"review_id": datasets.Value("string"),
"author": datasets.Value("string"),
"date":datasets.Value("string"),
"grade10": datasets.Value("string"),
}
),
supervised_keys=None,
homepage='',
citation='',
)
def _generate_examples(self, filepath):
df = pd.read_json(filepath, lines=True)
rows = df.to_dict(orient="records")
for n, row in enumerate(rows):
example = row
example["Idx"] = n
yield example["Idx"], example
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