Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
Thai
Size:
10K - 100K
License:
# coding=utf-8 | |
# 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: Add a description here.""" | |
import csv | |
import os | |
import datasets | |
# no BibTeX citation | |
_CITATION = "" | |
_DESCRIPTION = """\ | |
Wongnai's review dataset contains restaurant reviews and ratings, mainly in Thai language. | |
The reviews are in 5 classes ranging from 1 to 5 stars. | |
""" | |
_LICENSE = "LGPL-3.0" | |
_URLs = {"default": "https://archive.org/download/wongnai_reviews/wongnai_reviews_withtest.zip"} | |
class WongnaiReviews(datasets.GeneratorBasedBuilder): | |
VERSION = datasets.Version("1.0.1") | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"review_body": datasets.Value("string"), | |
"star_rating": datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"]), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage="https://github.com/wongnai/wongnai-corpus", | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
my_urls = _URLs[self.config.name] | |
data_dir = dl_manager.download_and_extract(my_urls) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"filepath": os.path.join(data_dir, "w_review_train.csv"), "split": "train"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={"filepath": os.path.join(data_dir, "w_review_test.csv"), "split": "test"}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
with open(filepath, encoding="utf-8") as f: | |
spamreader = csv.reader(f, delimiter=";", quotechar='"') | |
for id_, row in enumerate(spamreader): | |
yield id_, {"review_body": row[0], "star_rating": row[1]} | |