|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""Yelp Polarity Reviews dataset.""" |
|
|
|
|
|
import os |
|
|
|
import datasets |
|
from datasets.tasks import TextClassification |
|
|
|
|
|
_DESCRIPTION = """\ |
|
Large Yelp Review Dataset. |
|
This is a dataset for binary sentiment classification. \ |
|
We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. \ |
|
|
|
ORIGIN |
|
The Yelp reviews dataset consists of reviews from Yelp. It is extracted |
|
from the Yelp Dataset Challenge 2015 data. For more information, please |
|
refer to http://www.yelp.com/dataset_challenge |
|
|
|
The Yelp reviews polarity dataset is constructed by |
|
Xiang Zhang (xiang.zhang@nyu.edu) 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). |
|
|
|
|
|
DESCRIPTION |
|
|
|
The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 |
|
negative, and 3 and 4 positive. For each polarity 280,000 training samples and |
|
19,000 testing samples are take randomly. In total there are 560,000 trainig |
|
samples and 38,000 testing samples. Negative polarity is class 1, |
|
and positive class 2. |
|
|
|
The files train.csv and test.csv contain all the training samples as |
|
comma-sparated values. There are 2 columns in them, corresponding to class |
|
index (1 and 2) and review text. The review texts are escaped using double |
|
quotes ("), and any internal double quote is escaped by 2 double quotes (""). |
|
New lines are escaped by a backslash followed with an "n" character, |
|
that is "\n". |
|
""" |
|
|
|
_CITATION = """\ |
|
@article{zhangCharacterlevelConvolutionalNetworks2015, |
|
archivePrefix = {arXiv}, |
|
eprinttype = {arxiv}, |
|
eprint = {1509.01626}, |
|
primaryClass = {cs}, |
|
title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}}, |
|
abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.}, |
|
journal = {arXiv:1509.01626 [cs]}, |
|
author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, |
|
month = sep, |
|
year = {2015}, |
|
} |
|
|
|
""" |
|
|
|
_DOWNLOAD_URL = "https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polarity_csv.tgz" |
|
|
|
|
|
class YelpPolarityReviewsConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for YelpPolarityReviews.""" |
|
|
|
def __init__(self, **kwargs): |
|
"""BuilderConfig for YelpPolarityReviews. |
|
|
|
Args: |
|
|
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
super(YelpPolarityReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
|
|
|
|
|
class YelpPolarity(datasets.GeneratorBasedBuilder): |
|
"""Yelp Polarity reviews dataset.""" |
|
|
|
BUILDER_CONFIGS = [ |
|
YelpPolarityReviewsConfig( |
|
name="plain_text", |
|
description="Plain text", |
|
) |
|
] |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
"text": datasets.Value("string"), |
|
"label": datasets.features.ClassLabel(names=["1", "2"]), |
|
} |
|
), |
|
supervised_keys=None, |
|
homepage="https://course.fast.ai/datasets", |
|
citation=_CITATION, |
|
task_templates=[TextClassification(text_column="text", label_column="label")], |
|
) |
|
|
|
def _vocab_text_gen(self, train_file): |
|
for _, ex in self._generate_examples(train_file): |
|
yield ex["text"] |
|
|
|
def _split_generators(self, dl_manager): |
|
arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL) |
|
train_file = os.path.join(arch_path, "yelp_review_polarity_csv", "train.csv") |
|
test_file = os.path.join(arch_path, "yelp_review_polarity_csv", "test.csv") |
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_file}), |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
"""Generate Yelp examples.""" |
|
with open(filepath, encoding="utf-8") as f: |
|
for line_id, line in enumerate(f): |
|
|
|
|
|
yield line_id, {"text": line[5:-2].strip(), "label": line[1]} |
|
|