# Binary Classification Model

A Vision Transformer-based binary classification model specifically designed for detecting deepfake images, with an accuracy rate of 99.2%

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on a binary stroke detection dataset for image classification tasks, achieving an evaluation accuracy of 92.22%.

A binary classification model using fine-tuned Vision Transformer (ViT) to detect deepfake images

A phishing URL detection model fine-tuned based on the BERT architecture, used to identify potentially malicious URLs

This model is fine-tuned based on the BERT architecture for detecting gibberish text (e.g., random character combinations) in Turkish.

This is a binary classification model trained via AutoTrain for detecting pneumonia-related medical images.

This is a binary vision model trained via AutoTrain, specifically designed to differentiate between histological images of enchondroma and low-grade chondrosarcoma.

This is a vision binary classification model trained with AutoTrain for ad detection tasks.

A DistilBERT-based SMS spam detection model, fine-tuned on the sms_spam dataset, achieving an accuracy of 99.21%

A model fine-tuned on CodeXGLUE defect detection dataset based on CodeBERT, used to identify insecure code

A text classification model trained on Chinese dialogue emotion datasets, supporting binary classification: positive and negative.

This is an image classification model generated by HuggingPics, specifically designed to identify hot dogs in pictures