# Multi-class Classification

A facial image classification model using Vision Transformer (ViT) architecture for detecting dry, normal, and oily skin types

A celebrity classification model based on Google Vision Transformer (ViT) architecture, designed to recognize 1000 top celebrities

A Distilbert-based text classification model for detecting stereotypical content in sentences

This is a multi-class image classification model trained using AutoTrain, designed to recognize different ethnic features.

This is a visual image classification model trained on the ImageNet-1k dataset, capable of recognizing various common objects and scenes.

This is a multi-class image classification model trained via AutoTrain, specifically designed for dementia-related image classification tasks.

Food image classification model based on Google Vision Transformer (ViT) architecture, fine-tuned on the Food101 dataset with an accuracy of 90.9%

This is a food classification model fine-tuned on the food101 dataset based on Google's ViT model, achieving an accuracy of 89.5%.

A hotel image classification model based on PyTorch and HuggingPics, capable of recognizing different scenes and facilities in hotel environments.

An image classification model fine-tuned on the Food101 dataset based on Google's ViT model, achieving an accuracy of 85.39%

This is an AutoNLP-trained Bengali multi-class classification model that performs excellently on the IndicGLUE dataset.

This is a multi-class classification model trained using AutoNLP, specifically designed for song lyrics text classification tasks.

This is a multi-class classification model trained using AutoNLP, specifically designed for song lyrics text classification tasks.

This model is used to predict the nature category of French political statements, divided into problem, solution, and other.

This is a CIFAR-10 image classification model trained using supervised contrastive learning techniques, achieving a test accuracy of 81.06%

This is a multi-class classification model trained with AutoNLP, specifically designed for bank transaction classification tasks.

A Transformer model based on sparse attention, fine-tuned specifically for mobile app description classification tasks

This is a Turkish text classification model fine-tuned based on the BERT architecture, capable of classifying Turkish texts into 7 predefined categories.

This model predicts the historical era background of a story based on approximately 200-character Chinese plot summaries, trained on film and TV drama datasets.

A COVID-related French tweet classification model fine-tuned based on Yanzhu/bertweetfr-base, designed to identify COVID-related topics in tweets.

This is a French tweet sentiment classification model fine-tuned based on Yanzhu/bertweetfr-base, specifically designed for analyzing sentiment tendencies in COVID-19-related tweets.