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.

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