# Binary classification

This model is a BERT-Base-Uncased fine-tuned phishing site classification model that can predict whether a website belongs to the 'safe' or 'unsafe' category based on text input.

A Malay binary sentiment analysis model fine-tuned based on Deberta-V3-xsmall, supporting prediction of positive or negative sentiment labels

This model is a zero-shot classification model based on the DeBERTa-v3-large architecture, primarily trained on five NLI datasets, suitable for tasks that strictly follow the original NLI task format.

This model is a text classification model trained on five NLI datasets, primarily used for zero-shot classification tasks and serves as a comparative benchmark.

A binary classification image classification model trained with AutoTrain, performing excellently on the validation set

This is a binary classification model trained with AutoTrain, specifically designed to distinguish between images of cats and dogs.

This is a binary classification model trained using AutoTrain, specifically designed to distinguish between pictures of cats and dogs.

A cat and dog image classification model fine-tuned based on Google Vision Transformer (ViT) architecture, achieving 99% accuracy on the test set

This is a binary classification model trained via AutoNLP for detecting hate speech content in text.