Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
1M - 10M
ArXiv:
License:
File size: 3,994 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 amazon polarity dataset for text classification."""
import csv
import os
import datasets
_CITATION = """\
@inproceedings{mcauley2013hidden,
title={Hidden factors and hidden topics: understanding rating dimensions with review text},
author={McAuley, Julian and Leskovec, Jure},
booktitle={Proceedings of the 7th ACM conference on Recommender systems},
pages={165--172},
year={2013}
}
"""
_DESCRIPTION = """\
The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review.
"""
_HOMEPAGE = "https://registry.opendata.aws/"
_LICENSE = "Apache License 2.0"
_URLs = {
"amazon_polarity": "https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download",
}
class AmazonPolarityConfig(datasets.BuilderConfig):
"""BuilderConfig for AmazonPolarity."""
def __init__(self, **kwargs):
"""BuilderConfig for AmazonPolarity.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(AmazonPolarityConfig, self).__init__(**kwargs)
class AmazonPolarity(datasets.GeneratorBasedBuilder):
"""Amazon Polarity Classification Dataset."""
VERSION = datasets.Version("3.0.0")
BUILDER_CONFIGS = [
AmazonPolarityConfig(
name="amazon_polarity", version=VERSION, description="Amazon Polarity Classification Dataset."
),
]
def _info(self):
features = datasets.Features(
{
"label": datasets.features.ClassLabel(
names=[
"negative",
"positive",
]
),
"title": datasets.Value("string"),
"content": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
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, "amazon_review_polarity_csv", "train.csv"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "amazon_review_polarity_csv", "test.csv"),
"split": "test",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
data = csv.reader(f, delimiter=",", quoting=csv.QUOTE_ALL)
for id_, row in enumerate(data):
yield id_, {
"title": row[1],
"content": row[2],
"label": int(row[0]) - 1,
}
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