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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    FileNotFoundError
Message:      https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_Apparel_v1_00.tsv.gz
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 417, in _info
                  await _file_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 837, in _file_info
                  r.raise_for_status()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1005, in raise_for_status
                  raise ClientResponseError(
              aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_Apparel_v1_00.tsv.gz')
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 263, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 204, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 241, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 207, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/amazon_us_reviews/17b2481be59723469538adeb8fd0a68b0ba363bbbdd71090e72c325ee6c7e563/amazon_us_reviews.py", line 176, in _generate_examples
                  with open(file_path, "r", encoding="utf-8") as tsvfile:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 74, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
                  file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 134, in open
                  return self.__enter__()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 102, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1199, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/filesystems/compression.py", line 82, in _open
                  return self.file.open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 134, in open
                  return self.__enter__()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 102, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1199, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 356, in _open
                  size = size or self.info(path, **kwargs)["size"]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 115, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 100, in sync
                  raise return_result
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 55, in _runner
                  result[0] = await coro
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 430, in _info
                  raise FileNotFoundError(url) from exc
              FileNotFoundError: https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_Apparel_v1_00.tsv.gz

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Dataset Card for "amazon_us_reviews"

Dataset Summary

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.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

Apparel_v1_00

  • Size of downloaded dataset files: 648.64 MB
  • Size of the generated dataset: 2254.36 MB
  • Total amount of disk used: 2903.00 MB

An example of 'train' looks as follows.

{
    "customer_id": "45223824",
    "helpful_votes": 0,
    "marketplace": "US",
    "product_category": "Apparel",
    "product_id": "B016PUU3VO",
    "product_parent": "893588059",
    "product_title": "Fruit of the Loom Boys' A-Shirt (Pack of 4)",
    "review_body": "I ordered the same size as I ordered last time, and these shirts were much larger than the previous order. They were also about 6 inches longer. It was like they sent men's shirts instead of boys' shirts. I'll be returning these...",
    "review_date": "2015-01-01",
    "review_headline": "Sizes not correct, too big overall and WAY too long",
    "review_id": "R1N3Z13931J3O9",
    "star_rating": 2,
    "total_votes": 0,
    "verified_purchase": 1,
    "vine": 0
}

Automotive_v1_00

  • Size of downloaded dataset files: 582.15 MB
  • Size of the generated dataset: 1518.88 MB
  • Total amount of disk used: 2101.03 MB

An example of 'train' looks as follows.

{
    "customer_id": "16825098",
    "helpful_votes": 0,
    "marketplace": "US",
    "product_category": "Automotive",
    "product_id": "B000E4PCGE",
    "product_parent": "694793259",
    "product_title": "00-03 NISSAN SENTRA MIRROR RH (PASSENGER SIDE), Power, Non-Heated (2000 00 2001 01 2002 02 2003 03) NS35ER 963015M000",
    "review_body": "Product was as described, new and a great look. Only bad thing is that one of the screws was stripped so I couldn't tighten all three.",
    "review_date": "2015-08-31",
    "review_headline": "new and a great look. Only bad thing is that one of ...",
    "review_id": "R2RUIDUMDKG7P",
    "star_rating": 3,
    "total_votes": 0,
    "verified_purchase": 1,
    "vine": 0
}

Baby_v1_00

  • Size of downloaded dataset files: 357.40 MB
  • Size of the generated dataset: 956.30 MB
  • Total amount of disk used: 1313.70 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "customer_id": "23299101",
    "helpful_votes": 2,
    "marketplace": "US",
    "product_category": "Baby",
    "product_id": "B00SN6F9NG",
    "product_parent": "3470998",
    "product_title": "Rhoost Nail Clipper for Baby - Ergonomically Designed and Easy to Use Baby Nail Clipper, Natural Wooden Bamboo - Baby Health and Personal Care Kits",
    "review_body": "\"This is an absolute MUST item to have!  I was scared to death to clip my baby's nails.  I tried other baby nail clippers and th...",
    "review_date": "2015-08-31",
    "review_headline": "If fits so comfortably in my hand and I feel like I have ...",
    "review_id": "R2DRL5NRODVQ3Z",
    "star_rating": 5,
    "total_votes": 2,
    "verified_purchase": 1,
    "vine": 0
}

Beauty_v1_00

  • Size of downloaded dataset files: 914.08 MB
  • Size of the generated dataset: 2397.39 MB
  • Total amount of disk used: 3311.47 MB

An example of 'train' looks as follows.

{
    "customer_id": "24655453",
    "helpful_votes": 1,
    "marketplace": "US",
    "product_category": "Beauty",
    "product_id": "B00SAQ9DZY",
    "product_parent": "292127037",
    "product_title": "12 New, High Quality, Amber 2 ml (5/8 Dram) Glass Bottles, with Orifice Reducer and Black Cap.",
    "review_body": "These are great for small mixtures for EO's, especially for traveling.  I only gave this 4 stars because of the orifice reducer.  The hole is so small it is hard to get the oil out.  Just needs to be slightly bigger.",
    "review_date": "2015-08-31",
    "review_headline": "Good Product",
    "review_id": "R2A30ALEGLMCGN",
    "star_rating": 4,
    "total_votes": 1,
    "verified_purchase": 1,
    "vine": 0
}

Books_v1_00

  • Size of downloaded dataset files: 2740.34 MB
  • Size of the generated dataset: 7193.86 MB
  • Total amount of disk used: 9934.20 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "customer_id": "49735028",
    "helpful_votes": 0,
    "marketplace": "US",
    "product_category": "Books",
    "product_id": "0664254969",
    "product_parent": "248307276",
    "product_title": "Presbyterian Creeds: A Guide to the Book of Confessions",
    "review_body": "\"The Presbyterian Book of Confessions contains multiple Creeds for use by the denomination. This guidebook helps he lay person t...",
    "review_date": "2015-08-31",
    "review_headline": "The Presbyterian Book of Confessions contains multiple Creeds for use ...",
    "review_id": "R2G519UREHRO8M",
    "star_rating": 3,
    "total_votes": 1,
    "verified_purchase": 1,
    "vine": 0
}

Data Fields

The data fields are the same among all splits.

Apparel_v1_00

  • marketplace: a string feature.
  • customer_id: a string feature.
  • review_id: a string feature.
  • product_id: a string feature.
  • product_parent: a string feature.
  • product_title: a string feature.
  • product_category: a string feature.
  • star_rating: a int32 feature.
  • helpful_votes: a int32 feature.
  • total_votes: a int32 feature.
  • vine: a classification label, with possible values including Y (0), N (1).
  • verified_purchase: a classification label, with possible values including Y (0), N (1).
  • review_headline: a string feature.
  • review_body: a string feature.
  • review_date: a string feature.

Automotive_v1_00

  • marketplace: a string feature.
  • customer_id: a string feature.
  • review_id: a string feature.
  • product_id: a string feature.
  • product_parent: a string feature.
  • product_title: a string feature.
  • product_category: a string feature.
  • star_rating: a int32 feature.
  • helpful_votes: a int32 feature.
  • total_votes: a int32 feature.
  • vine: a classification label, with possible values including Y (0), N (1).
  • verified_purchase: a classification label, with possible values including Y (0), N (1).
  • review_headline: a string feature.
  • review_body: a string feature.
  • review_date: a string feature.

Baby_v1_00

  • marketplace: a string feature.
  • customer_id: a string feature.
  • review_id: a string feature.
  • product_id: a string feature.
  • product_parent: a string feature.
  • product_title: a string feature.
  • product_category: a string feature.
  • star_rating: a int32 feature.
  • helpful_votes: a int32 feature.
  • total_votes: a int32 feature.
  • vine: a classification label, with possible values including Y (0), N (1).
  • verified_purchase: a classification label, with possible values including Y (0), N (1).
  • review_headline: a string feature.
  • review_body: a string feature.
  • review_date: a string feature.

Beauty_v1_00

  • marketplace: a string feature.
  • customer_id: a string feature.
  • review_id: a string feature.
  • product_id: a string feature.
  • product_parent: a string feature.
  • product_title: a string feature.
  • product_category: a string feature.
  • star_rating: a int32 feature.
  • helpful_votes: a int32 feature.
  • total_votes: a int32 feature.
  • vine: a classification label, with possible values including Y (0), N (1).
  • verified_purchase: a classification label, with possible values including Y (0), N (1).
  • review_headline: a string feature.
  • review_body: a string feature.
  • review_date: a string feature.

Books_v1_00

  • marketplace: a string feature.
  • customer_id: a string feature.
  • review_id: a string feature.
  • product_id: a string feature.
  • product_parent: a string feature.
  • product_title: a string feature.
  • product_category: a string feature.
  • star_rating: a int32 feature.
  • helpful_votes: a int32 feature.
  • total_votes: a int32 feature.
  • vine: a classification label, with possible values including Y (0), N (1).
  • verified_purchase: a classification label, with possible values including Y (0), N (1).
  • review_headline: a string feature.
  • review_body: a string feature.
  • review_date: a string feature.

Data Splits

name train
Apparel_v1_00 5906333
Automotive_v1_00 3514942
Baby_v1_00 1752932
Beauty_v1_00 5115666
Books_v1_00 10319090
Books_v1_01 6106719
Books_v1_02 3105520
Camera_v1_00 1801974
Digital_Ebook_Purchase_v1_00 12520722
Digital_Ebook_Purchase_v1_01 5101693
Digital_Music_Purchase_v1_00 1688884
Digital_Software_v1_00 102084
Digital_Video_Download_v1_00 4057147
Digital_Video_Games_v1_00 145431
Electronics_v1_00 3093869
Furniture_v1_00 792113
Gift_Card_v1_00 149086
Grocery_v1_00 2402458
Health_Personal_Care_v1_00 5331449
Home_Entertainment_v1_00 705889
Home_Improvement_v1_00 2634781
Home_v1_00 6221559
Jewelry_v1_00 1767753
Kitchen_v1_00 4880466
Lawn_and_Garden_v1_00 2557288
Luggage_v1_00 348657
Major_Appliances_v1_00 96901
Mobile_Apps_v1_00 5033376
Mobile_Electronics_v1_00 104975
Music_v1_00 4751577
Musical_Instruments_v1_00 904765
Office_Products_v1_00 2642434
Outdoors_v1_00 2302401
PC_v1_00 6908554
Personal_Care_Appliances_v1_00 85981
Pet_Products_v1_00 2643619
Shoes_v1_00 4366916
Software_v1_00 341931
Sports_v1_00 4850360
Tools_v1_00 1741100
Toys_v1_00 4864249
Video_DVD_v1_00 5069140
Video_Games_v1_00 1785997
Video_v1_00 380604
Watches_v1_00 960872
Wireless_v1_00 9002021

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

https://s3.amazonaws.com/amazon-reviews-pds/LICENSE.txt

By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the Reviews Library is an Amazon Service subject to the Amazon.com Conditions of Use and you agree to be bound by them, with the following additional conditions:

In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Library for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Library or its contents, including use of the Reviews Library for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Library with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Library. If you violate any of the foregoing conditions, your license to access and use the Reviews Library will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.

Citation Information

No citation information.

Contributions

Thanks to @joeddav for adding this dataset.

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