Omkar2118/datasets huggingface dataset:
MMSD2.0
Full name: Towards a Reliable Multi-modal Sarcasm Detection System

Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-modal sarcasm detection system:(1) There are some spurious cues in MMSD, leading to the model bias learning; (2) The negative samples in MMSD are not always reasonable.To solve the aforementioned issues, we introduce MMSD2.0, a correction dataset that fixes the shortcomings of MMSD, by removing the spurious cues and re-annotating the unreasonable samples.Meanwhile, we present a novel framework called multi-view CLIP that is capable of leveraging multi-grained cues from multiple perspectives (i.e., text, image, and text-image interaction view) for multi-modal sarcasm detection.Extensive experiments show that MMSD2.0 is a valuable benchmark for building reliable multi-modal sarcasm detection systems and multi-view CLIP can significantly outperform the previous best baselines (with a 5.6% improvement).

Introducing paper: MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
DreamBooth

The **DreamBooth dataset** is a collection of images used for fine-tuning text-to-image diffusion models for subject-driven generation¹. Here are some key details about the dataset:

- The dataset includes **30 subjects** from **15 different classes**¹.
- Among these subjects, **9 are live subjects** (such as dogs and cats) and **21 are objects**¹.
- The dataset contains a variable number of images per subject, typically between **4 to 6 images**¹.
- Images of the subjects are usually captured in different conditions, environments, and under different angles¹.
- The dataset also includes a file `prompts_and_classes.txt` which contains all of the prompts used in the paper for live subjects and objects, as well as the class name used for the subjects¹.
- The images have either been captured by the paper authors or sourced from www.unsplash.com¹.
- The `references_and_licenses.txt` file contains a list of all the reference links to the images in www.unsplash.com, along with the attribution to the photographer and the license of the image¹.

This dataset is part of the official repository for the Google paper "DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation"¹. If you use this work, please cite the paper¹. Please note that this is not an officially supported Google product¹.

(1) GitHub - google/dreambooth. https://github.com/google/dreambooth.
(2) DreamBooth - Hugging Face. https://huggingface.co/docs/diffusers/training/dreambooth.
(3) google/dreambooth · Datasets at Hugging Face. https://huggingface.co/datasets/google/dreambooth.
(4) dreambooth: Mirror of https://huggingface.co/datasets/google .... https://gitee.com/hf-datasets/dreambooth.
(5) undefined. https://github.com/huggingface/diffusers.
(6) undefined. https://huggingface.co/datasets/google.

Introducing paper: DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation