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import json |
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import datasets |
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_AMAZON_REVIEW_2023_DESCRIPTION = """\ |
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Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset. |
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This dataset mainly includes reviews (ratings, text) and item metadata (desc- |
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riptions, category information, price, brand, and images). Compared to the pre- |
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vious versions, the 2023 version features larger size, newer reviews (up to Sep |
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2023), richer and cleaner meta data, and finer-grained timestamps (from day to |
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milli-second). |
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""" |
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class RawMetaAmazonReview2023Config(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(RawMetaAmazonReview2023Config, self).__init__(**kwargs) |
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self.suffix = 'jsonl' |
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self.domain = self.name[len(f'raw_meta_'):] |
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self.description = f'This is a subset for items in domain: {self.domain}.' |
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self.data_dir = f'raw/meta_categories/meta_{self.domain}.jsonl' |
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class AmazonReview2023(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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RawMetaAmazonReview2023Config( |
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name='raw_meta_All_Beauty' |
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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=_AMAZON_REVIEW_2023_DESCRIPTION + self.config.description |
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) |
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def _split_generators(self, dl_manager): |
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dl_dir = dl_manager.download_and_extract(self.config.data_dir) |
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return [ |
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datasets.SplitGenerator( |
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name='full', |
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gen_kwargs={"filepath": dl_dir} |
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) |
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] |
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def _generate_examples(self, filepath): |
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with open(filepath, 'r', encoding='utf-8') as file: |
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for idx, line in enumerate(file): |
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if self.config.suffix == 'jsonl': |
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try: |
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dp = json.loads(line) |
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""" |
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For item metadata, 'details' is free-form structured data |
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Here we dump it to string to make huggingface datasets easy |
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to store. |
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""" |
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if isinstance(self.config, RawMetaAmazonReview2023Config) and \ |
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'details' in dp: |
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dp['details'] = json.dumps(dp['details']) |
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except: |
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continue |
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else: |
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raise ValueError(f'Unknown suffix {self.config.suffix}.') |
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yield idx, dp |
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