datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).amazon_reviews_multiWe provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.amazon_reviews_multi_enamazon_reviews_mcauley_1and5amazon_reviews_multiWe provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.Amazon-Reviews-DatasetThis dataset provides a free trial sample of best-selling products and their customer reviews from a leading e-commerce platform, designed to support product intelligence, sentiment analysis, and market trend evaluation. This sample is provided for evaluation purposes only. It includes a curated subset of the full dataset.
To access the complete dataset, request additional attributes, or explore alternative product segments, please contact the data provider directly.
Key Features
2… See the full description on the dataset page: https://huggingface.co/datasets/datahiveai/Amazon-Reviews-Dataset.Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).amazon-reviews-2023-with-asin
Amazon Reviews 2023 (with ASIN)
A trimmed version of the McAuley-Lab/Amazon-Reviews-2023 dataset, retaining only the fields most relevant for NLP tasks while adding explicit product identification via parent_asin.
What's Different from the Original
The original dataset includes 10+ fields per review and requires a legacy loading script that is no longer supported by HuggingFace. This version:
Keeps only 4 fields: rating, title, text, and parent_asin
Is stored in… See the full description on the dataset page: https://huggingface.co/datasets/BarbaDLuca/amazon-reviews-2023-with-asin.Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).amazon-reviews-2023-trimmed
Amazon Product Reviews 2023 (Trimmed, 34 Categories)
This dataset is a trimmed and restructured version of the Amazon Product Reviews 2023 dataset by Julian McAuley and the UCSD Computer Science department.
It includes 34 product categories, each stored as a folder containing multiple sharded Parquet files for scalable access.
Only three fields are retained:
rating — The numerical review score (originally overall)
title — The review title (from summary)
text — The full review body… See the full description on the dataset page: https://huggingface.co/datasets/bagadbilla/amazon-reviews-2023-trimmed.Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).AmazonReviewsClassification
AmazonReviewsClassification
An MTEB dataset
Massive Text Embedding Benchmark
A collection of Amazon reviews specifically designed to aid research in multilingual text classification.
Task category
t2c
Domains
Reviews, Written
Reference
https://arxiv.org/abs/2010.02573
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["AmazonReviewsClassification"])
evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/AmazonReviewsClassification.Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).amazon-reviews
Dataset Card for Amazon Reviews 2018
This dataset is a collection of title-review pairs collected from Amazon, as collected in Ni et al.. See Amazon Reviews 2018 for additional information.
This dataset can be used directly with Sentence Transformers to train embedding models.
Dataset Subsets
pair subset
Columns: "title", "review"
Column types: str, str
Examples:{
'title': "It doesn't fit my machine. I can't seem to ...",
'review': "It doesn't fit my… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/amazon-reviews.Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).amazon_us_reviewsAmazon 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_reviewsAmazon 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_reviews_multi_ja#amazon reviews multi japanese
This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the Japanese language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.
amazon-reviews-sentiment-analysis
Dataset Card for amazon reviews for sentiment analysis
Dataset Summary
One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/amazon-reviews-sentiment-analysis.Amazon-Reviews-2023-Books-Meta
Amazon Reviews 2023 (Books Only)
This is a subset of Amazon Review 2023 dataset. Please visit amazon-reviews-2023.github.io/ for more details, loading scripts, and preprocessed benchmark files.
[April 18, 2024] Update
This dataset was created and pushed for the first time.
This is a large-scale Amazon Reviews dataset, collected in 2023 by McAuley Lab, and it includes rich features such as:
User Reviews (ratings, text, helpfulness votes, etc.);
Item Metadata (descriptions… See the full description on the dataset page: https://huggingface.co/datasets/cogsci13/Amazon-Reviews-2023-Books-Meta.Amazon-Reviews-2023-Books-Review
Amazon Reviews 2023 (Books Only)
This is a subset of Amazon Review 2023 dataset. Please visit amazon-reviews-2023.github.io/ for more details, loading scripts, and preprocessed benchmark files.
[April 18, 2024] Update
This dataset was created and pushed for the first time.
This is a large-scale Amazon Reviews dataset, collected in 2023 by McAuley Lab, and it includes rich features such as:
User Reviews (ratings, text, helpfulness votes, etc.);
Item Metadata (descriptions… See the full description on the dataset page: https://huggingface.co/datasets/cogsci13/Amazon-Reviews-2023-Books-Review.amazon_reviews_multi
Amazon Reviews Multi (Data Files Version)
This dataset hosts the multilingual Amazon Reviews corpus as raw jsonl.gz data files for direct loading via datasets without using a dataset script.
Source
Original dataset name: amazon_reviews_multi
Original dataset card: https://huggingface.co/datasets/amazon_reviews_multi
Mirror used for raw file retrieval: https://huggingface.co/datasets/buruzaemon/amazon_reviews_multi
Features
Each record contains:
review_id… See the full description on the dataset page: https://huggingface.co/datasets/goosmanlei/amazon_reviews_multi.Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).amazon_reviews_2013Amazon-Reviews-2023-Extended-min500char
Amazon Reviews 2023 — Extended 5-core Dataset
Extended version of McAuley-Lab/Amazon-Reviews-2023 with review text and product metadata joined. Only products with combined text (title + features + description) ≥ 500 characters are included.
Columns
split — train / valid / test (last-out 5-core benchmark)
user_id — reviewer ID
parent_asin — product group ID
asin — exact variant purchased
product_images — list of hi-res image URLs
product_title — product name
features —… See the full description on the dataset page: https://huggingface.co/datasets/thulthula/Amazon-Reviews-2023-Extended-min500char.amazon_reviews_multi_es#amazon reviews multi spanish
This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the Spanish language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.
Amazon-Reviews-2023amazon_reviews_multi_de#amazon reviews multi german
This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the German language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.