datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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_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_multiPlease refer to https://huggingface.co/datasets/defunct-datasets/amazon_reviews_multi.task1577_amazon_reviews_multi_japanese_language_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1577_amazon_reviews_multi_japanese_language_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1577_amazon_reviews_multi_japanese_language_classification.amazon-reviews-statement-v0
Factual Explanations for Amazon Reviews (v0)
This repository provides five augmented datasets derived from the Amazon Reviews 2014 collection: Toys, Clothes, Beauty, Sports, and Cellphones.
Each user–item interaction (rating + review) is paired with:
atomic statement–topic–sentiment (STS) triplets extracted from the review,
a ground-truth explanation constructed by aggregating all extracted statements in a rule-based manner (no LLM generation at this step), and
domain topics… See the full description on the dataset page: https://huggingface.co/datasets/benkabongo25/amazon-reviews-statement-v0.task1342_amazon_us_reviews_title
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1342_amazon_us_reviews_title
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1342_amazon_us_reviews_title.Amazon-Reviews-Price_Prediction_Corpus
💰 Amazon Product Price Prediction Dataset
Dataset Summary
This dataset is a carefully curated subset of the McAuley-Lab/Amazon-Reviews-2023 dataset, specifically engineered for training Large Language Models (LLMs) to predict product prices from product descriptions. The dataset focuses on 8 major retail categories commonly found in home improvement and electronics stores.
🎯 Primary Use Case: Fine-tuning LLMs to estimate product prices based on product titles and… See the full description on the dataset page: https://huggingface.co/datasets/ksharma9719/Amazon-Reviews-Price_Prediction_Corpus.amazon_reviews_multi_fr_prompt_title_generation_from_a_review
amazon_reviews_multi_fr_prompt_title_generation_from_a_review
Summary
amazon_reviews_multi_fr_prompt_title_generation_from_a_review is a subset of the Dataset of French Prompts (DFP).It contains 3,989,924 rows that can be used for a text generation task.The original data (without prompts) comes from the dataset amazon_reviews_multi by Keung et al. where only the French split has been kept.A list of prompts (see below) was then applied in order to build the input and… See the full description on the dataset page: https://huggingface.co/datasets/CATIE-AQ/amazon_reviews_multi_fr_prompt_title_generation_from_a_review.edit_amazon_reviews_multi_es
Dataset Summary
The data file is intended for a tutorial: Summarization
Language
Spanish
Dataset Structure
id: record id
stars: An int between 1-5 indicating the number of stars.
review_body: The text body of the review.
review_title: The text title of the review.
language: The string identifier of the review language.
product_category: String representation of the product's category.
lenght_review_body: text length of review_body
lenght_review_title: text… See the full description on the dataset page: https://huggingface.co/datasets/KRadim/edit_amazon_reviews_multi_es.amazon_reviews_multi_fr_prompt_binary_text_generation_from_title_of_a_review
amazon_reviews_multi_fr_prompt_binary_text_generation_from_title_of_a_review
Summary
amazon_reviews_multi_fr_prompt_binary_text_generation_from_title_of_a_review is a subset of the Dataset of French Prompts (DFP).It contains 7,560,000 rows that can be used for a text generation task.The original data (without prompts) comes from the dataset amazon_reviews_multi by Keung et al. where only the French split has been kept.A list of prompts (see below) was then applied in… See the full description on the dataset page: https://huggingface.co/datasets/CATIE-AQ/amazon_reviews_multi_fr_prompt_binary_text_generation_from_title_of_a_review.amazon-reviews-2023-all-beauty-sample
Amazon Reviews 2023 – All_Beauty (Sampled)
This dataset is a sampled subset of the McAuley-Lab/Amazon-Reviews-2023
All_Beauty category, prepared for the YZM2022 Data Mining homework
(Assoc. Prof. Dr. Arzu Kakisim).
Sampling strategy
Source: full All_Beauty reviews (701K) and metadata (112K items).
3-core filtering (each user and item has at least 3 interactions, iterated to convergence).
Cap to the most recent 60 000 interactions, re-applied 3-core.
Metadata restricted… See the full description on the dataset page: https://huggingface.co/datasets/debolut/amazon-reviews-2023-all-beauty-sample.amazon_reviews_multi_fr_prompt_text_generation_from_title_of_a_review
amazon_reviews_multi_fr_prompt_text_generation_from_title_of_a_review
Summary
amazon_reviews_multi_fr_prompt_text_generation_from_title_of_a_review is a subset of the Dataset of French Prompts (DFP).It contains 7,560,000 rows that can be used for a text generation task.The original data (without prompts) comes from the dataset amazon_reviews_multi by Keung et al. where only the French split has been kept.A list of prompts (see below) was then applied in order to build the… See the full description on the dataset page: https://huggingface.co/datasets/CATIE-AQ/amazon_reviews_multi_fr_prompt_text_generation_from_title_of_a_review.amazon_reviews_multi_VN
Dataset Card for The Multilingual Amazon Reviews Corpus
Dataset Summary
Defunct: Dataset "amazon_reviews_multi" is defunct and no longer accessible due to the decision of data providers.
We 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… See the full description on the dataset page: https://huggingface.co/datasets/kuriyamasgr/amazon_reviews_multi_VN.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.edit_amazon_reviews_multi_en
Dataset Summary
The data file is intended for a tutorial: Summarization
Language
English
Dataset Structure
id: record id
stars: An int between 1-5 indicating the number of stars.
review_body: The text body of the review.
review_title: The text title of the review.
language: The string identifier of the review language.
product_category: String representation of the product's category.
lenght_review_body: text length of review_body
lenght_review_title: text… See the full description on the dataset page: https://huggingface.co/datasets/KRadim/edit_amazon_reviews_multi_en.task1575_amazon_reviews_multi_sentiment_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1575_amazon_reviews_multi_sentiment_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1575_amazon_reviews_multi_sentiment_classification.task1576_amazon_reviews_multi_english_language_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1576_amazon_reviews_multi_english_language_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1576_amazon_reviews_multi_english_language_classification.task1574_amazon_reviews_multi_language_identification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1574_amazon_reviews_multi_language_identification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1574_amazon_reviews_multi_language_identification.Amazon_Reviewsamazon_reviews_multi_VN
Dataset Card for The Multilingual Amazon Reviews Corpus
Dataset Summary
Defunct: Dataset "amazon_reviews_multi" is defunct and no longer accessible due to the decision of data providers.
We 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… See the full description on the dataset page: https://huggingface.co/datasets/MinQan/amazon_reviews_multi_VN.Amazon_Reviews