# No fine-tuning required

A zero-shot classification model based on the Transformers architecture that can classify text without fine-tuning

ResNet50x16 visual model based on the CLIP framework, supporting zero-shot image classification tasks

A CLIP model based on ResNet101 architecture, supporting zero-shot image classification tasks.

Vision Transformer model trained with self-supervised DINOv2, specifically designed for encoding chest X-ray images

A versatile large language model capable of handling various natural language processing tasks

DeBERTa-v3 model specifically designed for zero-shot classification, trained on 27 tasks and 310 categories, supporting multi-domain text classification

This model is trained using self-supervised tuning methods and is suitable for zero-shot text classification tasks. It can perform sentiment analysis, topic classification, and other tasks without additional fine-tuning.

A vision-language pretrained model implemented based on OpenCLIP, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

Whisper is a pre-trained automatic speech recognition (ASR) and speech translation model, trained on 680,000 hours of labeled data with strong generalization capabilities

This model uses the BART-large architecture, trained on the MultiNLI dataset, and can be used for zero-shot text classification tasks.