datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
amazon_polarity
Dataset Card for Amazon Review Polarity
Dataset Summary
The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review.
Supported Tasks and Leaderboards
text-classification, sentiment-classification: The dataset is mainly used for text classification: given the content and the title, predict the correct… See the full description on the dataset page: https://huggingface.co/datasets/fancyzhx/amazon_polarity.amazon-berkeley-objects
Amazon Berkeley Objects (ABO)
A Hugging Face packaging of the Amazon Berkeley Objects (ABO) dataset. The
data content is the official CC BY 4.0 release from
https://amazon-berkeley-objects.s3.amazonaws.com/index.html. This mirror
changes only the packaging: files are grouped into typed Parquet shards, and
every original media file is preserved byte-for-byte and never transcoded.
Images use the datasets Image() feature, 3D product models use the native
Mesh() feature (original… See the full description on the dataset page: https://huggingface.co/datasets/suvadityamuk/amazon-berkeley-objects.amazon_counterfactual
AmazonCounterfactualClassification
An MTEB dataset
Massive Text Embedding Benchmark
A collection of Amazon customer reviews annotated for counterfactual detection pair classification.
Task category
t2c
Domains
Reviews, Written
Reference
https://arxiv.org/abs/2104.06893
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(["AmazonCounterfactualClassification"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_counterfactual.SpIDER-Bench
SpIDER-Bench
Repository dependency graphs for software issue localization — the graph data behind
SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization
(arXiv:2512.16956).
Each benchmark instance gets one directed multigraph of its repository at the commit the
issue was filed against. Nodes are directories, files, classes and functions carrying
their source; edges are contains / imports / inherits / invokes relations between
them. SpIDER uses these… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/SpIDER-Bench.amazon_reviews_mcauley_1and5amazon_polarity
AmazonPolarityClassification
An MTEB dataset
Massive Text Embedding Benchmark
Amazon Polarity Classification Dataset.
Task category
t2c
Domains
Reviews, Written
Reference
https://huggingface.co/datasets/amazon_polarity
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(["AmazonPolarityClassification"])
evaluator = mteb.MTEB(task)
model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_polarity.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.RealKIE-FCC-Verified
RealKIE-FCC-Verified
It is a test set with single and multi-page invoices sourced from the Federal Communications Commission (FCC) to evaluate key information extraction (KIE) performance.
Task
Extract information from the document in JSON format given the corresponding JSON schema. It contains 75 documents, with:
a) image_files: Each document has multiple pages
b) json_schema: A common JSON schema requiring extraction of specified information including line… See the full description on the dataset page: https://huggingface.co/datasets/amazon-agi/RealKIE-FCC-Verified.model-mappingmusic-off-policy-evaluation-benchmark
Music Off-Policy Evaluation Dataset
Music Off-Policy Evaluation Dataset is a dataset designed for Off-Policy Evaluation (OPE) research. It contains logged interactions from the home page of Amazon Music.
Use cases:
Benchmarking OPE estimators
Evaluating counterfactual ranking policies offline
License
Music Off-Policy Evaluation Benchmark © 2026 by Amazon is licensed under Creative Commons Attribution-NonCommercial 4.0 International.… See the full description on the dataset page: https://huggingface.co/datasets/amazon/music-off-policy-evaluation-benchmark.Amazon_Sample_Metadata_2023
Dataset Card for Dataset Name
Original datasets can be found on: https://amazon-reviews-2023.github.io/
Dataset Details
This dataset was made as sample of several datasets from the link above.
Dataset Description
This dataset is a curated sample derived from seven filtered Amazon product category datasets(Amazon All Beauty, Amazon Fashion, Sports and Outdoors,
Health and Personal Care, Amazon Clothing Shoes and Jewlery,
Baby Products and Beauty and Personal… See the full description on the dataset page: https://huggingface.co/datasets/smartcat/Amazon_Sample_Metadata_2023.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.ConfBench
FCC Invoices Verified Augmented
Dataset Description
FCC Invoices Verified Augmented is a document understanding benchmark dataset consisting of 75 real-world Federal Communications Commission (FCC) invoice documents, each augmented with up to 18 distinct document degradation pipelines. The dataset is designed to support confidence calibration research, OCR robustness evaluation, and key information extraction (KIE) under realistic noise conditions.
Each document… See the full description on the dataset page: https://huggingface.co/datasets/amazon/ConfBench.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
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-Fashion-Training-Data-2023
Amazon Reviews 2023
Please also visit amazon-reviews-2023.github.io/ for more details, loading scripts, and preprocessed benchmark files.
[April 7, 2024] We add two useful files:
all_categories.txt: 34 lines (33 categories + "Unknown"), each line contains a category name.
asin2category.json: A mapping between parent_asin (item ID) to its corresponding category name.
This is a large-scale Amazon Reviews dataset, collected in 2023 by McAuley Lab, and it includes rich features… See the full description on the dataset page: https://huggingface.co/datasets/Pandeymp29/Amazon-Fashion-Training-Data-2023.AmazonQAC
AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset
Train Dataset Size: 395 million samplesTest Dataset Size: 20k samplesSource: Amazon Search LogsFile Format: ParquetCompression: Snappy
If you use this dataset, please cite our EMNLP 2024 paper:
@inproceedings{everaert-etal-2024-amazonqac,
title = "{A}mazon{QAC}: A Large-Scale, Naturalistic Query Autocomplete Dataset",
author = "Everaert, Dante and
Patki, Rohit and
Zheng, Tianqi and
Potts… See the full description on the dataset page: https://huggingface.co/datasets/amazon/AmazonQAC.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.AmazonMLChallengeStage1Amazon-Search-Benchmark
📃 Dataset Summary
This dataset is a benchmark for query-product retrieval, designed to evaluate the performance of algorithms in retrieving relevant products based on user search queries.
It is built from two Hugging Face datasets:
Amazon-C4
Amazon Reviews 2023
The dataset focuses on enhancing the retrieval of products by using structured product information (title, description, category) to generate search queries
and assess relevance.
The dataset contains two main parts:… See the full description on the dataset page: https://huggingface.co/datasets/apexlearningcurve/Amazon-Search-Benchmark.amazoncat-13kThis dataset has been created as an artefact of the paper AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets (Lesci and Vlachos, 2024).
More info about this dataset in the appendix of the paper.
The AmazonCat-13k dataset was released by McAuley and Leskovec (2013)
and is composed of product descriptions and reviews classified into 13k multi-label categories.
The dataset is split into 1.2M train and 300k evaluation instances.
It is commonly used as an extreme… See the full description on the dataset page: https://huggingface.co/datasets/pietrolesci/amazoncat-13k.asnq
Dataset Card for "asnq"
Dataset Summary
ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
candidate sentence is contained in the long_answer and if the… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/asnq.amazon_reviews_2013amazon_polarity_embeddings_random4
Dataset Card for "amazon_polarity_embeddings_random4"
More Information needed
amazon_metadata_datathon_2023
Dataset Card for "amazon_metadata_datathon_2023"
More Information needed
Amazon-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-all-beauty-filtered-limitedamazon-products-eval
Marqo Ecommerce Embedding Models
In this work, we introduce the AmazonProducts-3m dataset for evaluation. This dataset comes with the release of our state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L.
Released Content:
Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models
GoogleShopping-1m and AmazonProducts-3m for evaluation
Evaluation Code
The benchmarking results show that the… See the full description on the dataset page: https://huggingface.co/datasets/Marqo/amazon-products-eval.Amazon-Reviews-2023