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amazon_us_reviews

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns : marketplace - 2 letter country code of the marketplace where the review was written. customer_id - Random identifier that can be used to aggregate reviews written by a single author. review_id - The unique ID of the review. product_id - The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id. product_parent - Random identifier that can be used to aggregate reviews for the same product. product_title - Title of the product. product_category - Broad product category that can be used to group reviews (also used to group the dataset into coherent parts). star_rating - The 1-5 star rating of the review. helpful_votes - Number of helpful votes. total_votes - Number of total votes the review received. vine - Review was written as part of the Vine program. verified_purchase - The review is on a verified purchase. review_headline - The title of the review. review_body - The review text. review_date - The date the review was written.

amazon_us_reviews is configured with tfds.structured.amazon_us_reviews.AmazonUSReviewsConfig and has the following configurations predefined (defaults to the first one):

  • Wireless_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Wireless_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Watches_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Watches_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Video_Games_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Video_Games_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Video_DVD_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Video_DVD_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Video_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Video_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Toys_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Toys_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Tools_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Tools_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Sports_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Sports_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Software_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Software_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Shoes_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Shoes_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Pet_Products_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Pet_Products_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Personal_Care_Appliances_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Personal_Care_Appliances_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • PC_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon PC_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Outdoors_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Outdoors_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Office_Products_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Office_Products_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Musical_Instruments_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Musical_Instruments_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Music_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Music_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Mobile_Electronics_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Mobile_Electronics_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Mobile_Apps_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Mobile_Apps_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Major_Appliances_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Major_Appliances_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Luggage_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Luggage_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Lawn_and_Garden_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Lawn_and_Garden_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Kitchen_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Kitchen_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Jewelry_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Jewelry_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Home_Improvement_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Home_Improvement_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Home_Entertainment_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Home_Entertainment_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Home_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Home_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Health_Personal_Care_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Health_Personal_Care_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Grocery_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Grocery_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Gift_Card_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Gift_Card_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Furniture_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Furniture_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Electronics_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Electronics_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Digital_Video_Games_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Digital_Video_Games_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Digital_Video_Download_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Digital_Video_Download_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Digital_Software_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Digital_Software_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Digital_Music_Purchase_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Digital_Music_Purchase_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Digital_Ebook_Purchase_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Digital_Ebook_Purchase_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Camera_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Camera_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Books_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Books_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Beauty_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Beauty_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Baby_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Baby_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Automotive_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Automotive_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Apparel_v1_00 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Apparel_v1_00 products in US marketplace. Each product has its own version as specified with it.

  • Digital_Ebook_Purchase_v1_01 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Digital_Ebook_Purchase_v1_01 products in US marketplace. Each product has its own version as specified with it.

  • Books_v1_01 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Books_v1_01 products in US marketplace. Each product has its own version as specified with it.

  • Books_v1_02 (v0.1.0) (Size: ?? GiB): A dataset consisting of reviews of Amazon Books_v1_02 products in US marketplace. Each product has its own version as specified with it.

amazon_us_reviews/Wireless_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Watches_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Video_Games_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Video_DVD_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Video_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Toys_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Tools_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Sports_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Software_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Shoes_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Pet_Products_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Personal_Care_Appliances_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/PC_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Outdoors_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Office_Products_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Musical_Instruments_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Music_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Mobile_Electronics_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Mobile_Apps_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Major_Appliances_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Luggage_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Lawn_and_Garden_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Kitchen_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Jewelry_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Home_Improvement_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Home_Entertainment_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Home_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Health_Personal_Care_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Grocery_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Gift_Card_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Furniture_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Electronics_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Digital_Video_Games_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Digital_Video_Download_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Digital_Software_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Digital_Music_Purchase_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Digital_Ebook_Purchase_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Camera_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Books_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Beauty_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Baby_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Automotive_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Apparel_v1_00

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Digital_Ebook_Purchase_v1_01

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Books_v1_01

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

amazon_us_reviews/Books_v1_02

FeaturesDict({
    'data': FeaturesDict({
        'customer_id': Tensor(shape=(), dtype=tf.string),
        'helpful_votes': Tensor(shape=(), dtype=tf.int32),
        'marketplace': Tensor(shape=(), dtype=tf.string),
        'product_category': Tensor(shape=(), dtype=tf.string),
        'product_id': Tensor(shape=(), dtype=tf.string),
        'product_parent': Tensor(shape=(), dtype=tf.string),
        'product_title': Tensor(shape=(), dtype=tf.string),
        'review_body': Tensor(shape=(), dtype=tf.string),
        'review_date': Tensor(shape=(), dtype=tf.string),
        'review_headline': Tensor(shape=(), dtype=tf.string),
        'review_id': Tensor(shape=(), dtype=tf.string),
        'star_rating': Tensor(shape=(), dtype=tf.int32),
        'total_votes': Tensor(shape=(), dtype=tf.int32),
        'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    }),
})

Statistics

None computed

Urls

Supervised keys (for as_supervised=True)

None