Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
100K - 1M
ArXiv:
License:
File size: 4,412 Bytes
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# 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.
"""The Yelp Review Full dataset for text classification."""
import csv
import datasets
from datasets.tasks import TextClassification
_CITATION = """\
@inproceedings{zhang2015character,
title={Character-level convolutional networks for text classification},
author={Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
booktitle={Advances in neural information processing systems},
pages={649--657},
year={2015}
}
"""
_DESCRIPTION = """\
The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data.
The Yelp reviews full star dataset is constructed by Xiang Zhang ([email protected]) from the above dataset.
It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun.
Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
"""
_HOMEPAGE = "https://www.yelp.com/dataset"
_LICENSE = "https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf"
_URLs = {
"yelp_review_full": "https://s3.amazonaws.com/fast-ai-nlp/yelp_review_full_csv.tgz",
}
class YelpReviewFullConfig(datasets.BuilderConfig):
"""BuilderConfig for YelpReviewFull."""
def __init__(self, **kwargs):
"""BuilderConfig for YelpReviewFull.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(YelpReviewFullConfig, self).__init__(**kwargs)
class YelpReviewFull(datasets.GeneratorBasedBuilder):
"""Yelp Review Full Star Dataset 2015."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
YelpReviewFullConfig(
name="yelp_review_full", version=VERSION, description="Yelp Review Full Star Dataset 2015"
),
]
def _info(self):
features = datasets.Features(
{
"label": datasets.features.ClassLabel(
names=[
"1 star",
"2 star",
"3 stars",
"4 stars",
"5 stars",
]
),
"text": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.name]
archive = dl_manager.download(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": "yelp_review_full_csv/train.csv", "files": dl_manager.iter_archive(archive)},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": "yelp_review_full_csv/test.csv", "files": dl_manager.iter_archive(archive)},
),
]
def _generate_examples(self, filepath, files):
"""Yields examples."""
for path, f in files:
if path == filepath:
csvfile = (line.decode("utf-8") for line in f)
data = csv.reader(csvfile, delimiter=",", quoting=csv.QUOTE_NONNUMERIC)
for id_, row in enumerate(data):
yield id_, {
"text": row[1],
"label": int(row[0]) - 1,
}
break
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