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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""id-review-gen: An Indonesian Review Generation Dataset."""
import csv
import pandas as pd
import datasets
_DESCRIPTION = """\
This dataset is built as a playground for review text generation.
"""
_HOMEPAGE = "https://github.com/jakartaresearch"
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_TRAIN_URL = (
"https://github.com/jakartaresearch/id-review-gen/blob/main/data/id-review-generation.csv"
)
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ReviewGen(datasets.GeneratorBasedBuilder):
"""GooglePlayReview: An Indonesian Sentiment Analysis Dataset."""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{"text": datasets.Value("string"), "label": datasets.Value("string")}
)
return datasets.DatasetInfo(
description=_DESCRIPTION, features=features, homepage=_HOMEPAGE
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
"""Generate examples."""
df = pd.read_csv(filepath, encoding="utf-8")
for item in df.itertuples():
print(item)
yield item.Index, {"text": item.text, "label": item.label}
# with open(filepath, encoding="utf-8") as csv_file:
# csv_reader = csv.reader(csv_file, delimiter=",")
# next(csv_reader)
# for id_, row in enumerate(csv_reader):
# text, label = row
# yield id_, {"text": text, "label": label}
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