datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ECommerce-Women-Clothing-ReviewsBitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.Ecommerce_textecommerce-behavior-data-from-multi-category-store_oct-nov_2019
eCommerce Behavior Data from Multi-Category Store
About the Dataset
This dataset contains behavioral data for 285 million user events from a large multi-category eCommerce store. The data spans 7 months (October 2019 - April 2020) and records various user interactions with products.
Dataset Overview
Time Frame: October 2019 - April 2020
Total Events: 285 million
Event Granularity: Each row represents an event associated with a product and a user.
Data Source:… See the full description on the dataset page: https://huggingface.co/datasets/kevykibbz/ecommerce-behavior-data-from-multi-category-store_oct-nov_2019.ecommerce-user-behavior-datae-commercee-commerce-orders
E-commerce Customer Order Behavior Dataset
A synthetic e-commerce dataset containing 10,000 orders with realistic customer behavior patterns, suitable for e-commerce analytics and machine learning tasks.
Dataset Card for E-commerce Orders
Dataset Summary
This dataset simulates customer order behavior in an e-commerce platform, containing detailed information about orders, customers, products, and delivery patterns. The data is synthetically generated with… See the full description on the dataset page: https://huggingface.co/datasets/millat/e-commerce-orders.eCommerce_Dataecommerce-analytics-sql-evaluation
Ecommerce Analytics SQL Evaluation (declared GMV, verified answer key)
An evalpack: an evaluation database generated from the answer key, not
annotated after the fact. A VLDB 2026 audit found 52.8% of BIRD Mini-Dev
answer keys wrong because benchmarks annotate answers onto existing
databases; this dataset inverts the order. The declared properties (curves,
shares, identities) are the specification, the database is generated to
satisfy them exactly, and every shipped question was… See the full description on the dataset page: https://huggingface.co/datasets/rasinmuhammed/ecommerce-analytics-sql-evaluation.Ecommerce_data.csvE-Commerce_FAQsecommerce-retail-product-matching-workflow-dataset
Ecommerce Retail Product Matching Workflow Dataset
This dataset is a public-facing sanitized workflow preview for managed ecommerce and retail product matching. It shows how candidate retrieval, UPC/model/brand/title/image evidence, customer-visible URL validation, confidence bands, review buckets, and rejection reasons can be structured for pricing intelligence, merchandising, data engineering, and AI-assisted product matching workflows.
Use this dataset to evaluate product… See the full description on the dataset page: https://huggingface.co/datasets/Octoparse/ecommerce-retail-product-matching-workflow-dataset.ecommerce-visual-matching-dataset
E-commerce Visual Matching Dataset
Candidate match workflow dataset for product identity resolution, visual similarity, and review decision fields.
A public-safe workflow preview of how Octoparse structures AI-assisted product matching pipelines for e-commerce and pricing teams. Every row represents a candidate pair evaluation — the same structure delivered to production clients.
Built by Octoparse Managed Data Service — managed web data pipelines for pricing intelligence and… See the full description on the dataset page: https://huggingface.co/datasets/Octoparse/ecommerce-visual-matching-dataset.E-Commerce-Product-Recommendationecommerce-salesecommerce-fraud-detection-synthetic-10k-sampl
🛡️ Synthetic E-Commerce Fraud & AML Detection Dataset (10k Evaluation Sample)
⚠️ NOTICE: This is a truncated 10,000-row evaluation sample strictly for schema verification and local testing.
💳 [OBTAIN THE 10-MILLION ROW COMMERCIAL LICENSE HERE] > https://buy.stripe.com/8x26oIad4eH9eJf6gJ5wI01
🚀 Quick Start (Load via Hugging Face)
Data scientists can instantly load this evaluation slice into their Pandas/Python environment using the datasets library:
from… See the full description on the dataset page: https://huggingface.co/datasets/chinna887/ecommerce-fraud-detection-synthetic-10k-sampl.ecommerce-predictor-data
E-commerce Customer Spending Dataset
A dataset containing customer behavior metrics from an e-commerce platform, used to predict yearly spending.
Dataset Description
This dataset contains information about customers of an e-commerce company that sells clothing online and also has in-store style sessions. Customers can come to the store for personal styling sessions, then order clothes through a mobile app or website.
Features
Column
Type… See the full description on the dataset page: https://huggingface.co/datasets/Srikanth-Karthi/ecommerce-predictor-data.E-commerce-Product-Review-Sentiment-Analysissouth-america-ecommerce-ux-monthly
South America E-Commerce UX Monthly Dataset — v2026-08
Longitudinal monthly dataset of e-commerce user experience (UX) signals collected from major
retail platforms in Brazil and Argentina. This is the August 2026 monthly release,
intended for academic reproducibility, cross-month comparison, and the publication of monthly UX
insight reports by the São Paulo (USP) & Buenos Aires (UBA) collaborative research team.
Quick Access
Version tag: v2026-08
Data file:… See the full description on the dataset page: https://huggingface.co/datasets/toolathon123/south-america-ecommerce-ux-monthly.brazil-argentina-ecommerce-cost-optimization
Brazil-Argentina E-Commerce Cost Optimization Dataset
Real-time operational dataset collected by a cross-border e-commerce retail company headquartered in São Paulo, Brazil, with operations in Buenos Aires, Argentina. This dataset is published to support the Data Science team's model training and cost-structure analysis during the quarterly cost-optimization program.
Overview
Number of records: 500
Time span: Rolling window covering the most recent 7 days… See the full description on the dataset page: https://huggingface.co/datasets/toolathon123/brazil-argentina-ecommerce-cost-optimization.E-Commerce-Title-and-Description-Dataecommerce_customer_behavior_analysise-commerce_ordersHere you can find the orders.csv file from the e-commerce database
ecommerce-fraud-detection-synthetic-10k-sampl
🛡️ Synthetic E-Commerce Fraud & AML Detection Dataset (10k Evaluation Sample)
⚠️ NOTICE: This is a truncated 10,000-row evaluation sample strictly for schema verification and local testing.
💳 [OBTAIN THE 10-MILLION ROW COMMERCIAL LICENSE HERE] > https://buy.stripe.com/8x26oIad4eH9eJf6gJ5wI01
🚀 Quick Start (Load via Hugging Face)
Data scientists can instantly load this evaluation slice into their Pandas/Python environment using the datasets library:
from datasets… See the full description on the dataset page: https://huggingface.co/datasets/apex0data/ecommerce-fraud-detection-synthetic-10k-sampl.random-ecommerce-transactions
Random E-commerce Transactions
A synthetic dataset of 5,000 randomly generated e-commerce orders (products, prices, countries, returns). For testing and demos only.
Note: This is randomly generated synthetic data with no real-world meaning. Generated for testing and demonstration purposes.
ECommerceProductReviewsE-commerce_return_abuse_detection_datasetIn the booming world of e-commerce, hassle-free return policies are a major driver of customer satisfaction. However, this convenience comes at a massive cost: Return Abuse and Fraud. Retailers lose billions annually to "wardrobing" (wearing an item once and returning it), policy abuse (excessive legitimate returns), and outright fraudulent returns (returning empty boxes, stolen goods, or different items).
Detecting these bad actors without adding friction to the experience of legitimate… See the full description on the dataset page: https://huggingface.co/datasets/sarveshchhetri/E-commerce_return_abuse_detection_dataset.retail-ecommerce-churnE-Commerce-Customer-Intent-Detection-Datasete-commerce-intent-classifier-dataset