Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Dataset Card for `Amazon Reviews 2023 All-Category k-Core`

These datasets are subsets of Amazon reviews dataset, collected in 2023 by McAuley Lab.
It contains all categories of the reviews from the original dataset that have more than $k \in [5, 20]$ interactions.
The original dataset contains reviews in the period of May. 1996 to Sep. 2023.
The reviews are grouped into 25 categories.
The dataset is in .parquet format.

k-core means that every user and every item has at least k interactions across ALL categories combined.

This condition may not hold within a single category.

Dataset Details

Dataset Description

The dataset contains reviews from Amazon, and it is a subset of the original dataset. The dataset is in .parquet format.

Please refer to the Dataset Creation and Processing section for more details about the dataset.

Dataset Structure

The repository is structured as follows:

amazon-2023-all-category-k-core/
  |- 5-core/
     |- 5-core.parquet                 # 5-core ratings of all categories, 3.16GB
  |- 20-core/
     |- category/
        |- Arts_Crafts_and_Sewing/
           |- ratings.parquet          # ratings of Arts, Crafts & Sewing
           |- meta.parquet             # meta data of items in Arts, Crafts & Sewing
           |- reviews.parquet          # reviews of items in Arts, Crafts & Sewing
        |- ...                         # other categories
     |- 20-core.parquet                # 20-core ratings of all categories, 1.1GB
     |- item_map.jsonl.gz              # item map, format: [{item_index:int, parent_asin:str}], 7.97MB
     |- user_map.jsonl.gz              # user map, format: [{user_index:int, user_id:str}],     29.4MB

Dataset Creation and Processing

Merge the ratings from all categories of Amazon reviews 2023 dataset
Filter out the ratings that have less than $k$ interactions, where $k \in [5, 20]$.
Filter out the meta data and reviews of items that are not in the filtered ratings.
Save the datasets in .parquet format.

Core Code Snippets

# Iteratively remove all users and items with fewer than k ratings

k = 20

while True:
    user_counts = df['user_id'].value_counts()
    item_counts = df['parent_asin'].value_counts()
    
    filtered_df = df[
        df['user_id'].isin(user_counts[user_counts >= k].index) &
        df['parent_asin'].isin(item_counts[item_counts >= k].index)
    ]
    
    if len(filtered_df) == len(df):
        break
    
    df = filtered_df

# `df` or `filtered_df` would be the resulted data.

Dataset Sources

The original dataset is available at Amazon reviews dataset.

Uses

This dataset can be used for recommendation systems, sentiment analysis, and other NLP tasks.

Glossary

The glossary of the dataset is available at Amazon Reviews#Data Fields.

Dataset Card Authors

Chenglong Ma

Dataset Card Contact

https://huggingface.co/ChenglongMa

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Total file size:

63.6 GB

Dataset Card for Amazon Reviews 2023 All-Category k-Core