Omkar2118/datasets huggingface dataset:
AE-110k
Full name: AliExpress - 110k

The dataset contains product information from AliExpress Sports & Entertainment category. Each attribute value in "Item Specific" is matched against the product title using exact string match to generate positive triples <title, attribute, value>. Negative triples <title, attribute, NULL> are randomly generated. Each triple is stored in a line and separated by \u0001.

Introducing paper: Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title
WDC-PAVE
Full name: Web Data Commones - Product Attribute Value Extraction

The datasets contains 1,420 human annotated product offers, systematically selected from the Web Data Commons Product Matching Corpus, featuring 24,582 annotated attribute-value pairs, making it a valuable resource for both product attribute-value extraction and product matching tasks. 
The normalized gold standard contains the standardized attribute value pairs as described below.

Introducing paper: Using LLMs for the Extraction and Normalization of Product Attribute Values
OA-Mine - annotations

The dataset contains Amazon products from 10 product categories with full human annotations.
The dataset was collected in 2021. The products may have been taken down from Amazon since the collection of the dataset.

Introducing paper: OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
noisy-ADE20K-DS

A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [DS] employs downsampled semantic maps that are resized by nearest-neighbor interpolation, simulating human errors e.g., jagged edges and coarse/low resolution user inputs.

Introducing paper: Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis
noisy-ADE20K-Edge

A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [Edge] masks the edges of instances with an unlabeled class, imitating incomplete annotations around edges, especially between instances.

Introducing paper: Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis
noisy-ADE20K-Random

A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [Random] randomly adds an unlabeled class to the semantic maps, mimicing unintended user error and extreme random noise.

Introducing paper: Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis