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"""Yelp Polarity Reviews dataset.""" |
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import datasets |
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from datasets.tasks import TextClassification |
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_DESCRIPTION = """\ |
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Large Yelp Review Dataset. |
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This is a dataset for binary sentiment classification. \ |
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We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. \ |
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ORIGIN |
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The Yelp reviews dataset consists of reviews from Yelp. It is extracted |
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from the Yelp Dataset Challenge 2015 data. For more information, please |
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refer to http://www.yelp.com/dataset_challenge |
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The Yelp reviews polarity dataset is constructed by |
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Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset. |
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It is first used as a text classification benchmark in the following paper: |
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Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks |
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for Text Classification. Advances in Neural Information Processing Systems 28 |
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(NIPS 2015). |
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DESCRIPTION |
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The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 |
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negative, and 3 and 4 positive. For each polarity 280,000 training samples and |
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19,000 testing samples are take randomly. In total there are 560,000 trainig |
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samples and 38,000 testing samples. Negative polarity is class 1, |
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and positive class 2. |
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The files train.csv and test.csv contain all the training samples as |
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comma-sparated values. There are 2 columns in them, corresponding to class |
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index (1 and 2) and review text. The review texts are escaped using double |
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quotes ("), and any internal double quote is escaped by 2 double quotes (""). |
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New lines are escaped by a backslash followed with an "n" character, |
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that is "\n". |
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""" |
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_CITATION = """\ |
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@article{zhangCharacterlevelConvolutionalNetworks2015, |
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archivePrefix = {arXiv}, |
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eprinttype = {arxiv}, |
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eprint = {1509.01626}, |
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primaryClass = {cs}, |
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title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}}, |
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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.}, |
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journal = {arXiv:1509.01626 [cs]}, |
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author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, |
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month = sep, |
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year = {2015}, |
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} |
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""" |
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_DOWNLOAD_URL = "https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polarity_csv.tgz" |
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class YelpPolarityReviewsConfig(datasets.BuilderConfig): |
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"""BuilderConfig for YelpPolarityReviews.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for YelpPolarityReviews. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(YelpPolarityReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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class YelpPolarity(datasets.GeneratorBasedBuilder): |
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"""Yelp Polarity reviews dataset.""" |
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BUILDER_CONFIGS = [ |
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YelpPolarityReviewsConfig( |
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name="plain_text", |
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description="Plain text", |
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) |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"label": datasets.features.ClassLabel(names=["1", "2"]), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://course.fast.ai/datasets", |
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citation=_CITATION, |
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task_templates=[TextClassification(text_column="text", label_column="label")], |
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) |
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def _vocab_text_gen(self, train_file): |
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for _, ex in self._generate_examples(train_file): |
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yield ex["text"] |
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def _split_generators(self, dl_manager): |
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arch_path = dl_manager.download(_DOWNLOAD_URL) |
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train_file = "yelp_review_polarity_csv/train.csv" |
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test_file = "yelp_review_polarity_csv/test.csv" |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": train_file, |
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"files": dl_manager.iter_archive(arch_path), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": test_file, |
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"files": dl_manager.iter_archive(arch_path), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, files): |
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"""Generate Yelp examples.""" |
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for path, f in files: |
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if path == filepath: |
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for line_id, line in enumerate(f): |
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line = line.decode("utf-8") |
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yield line_id, {"text": line[5:-2].strip(), "label": line[1]} |
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break |
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