# High Accuracy Classification

cde-small-v1 is a small sentence embedding model based on transformer architecture, excelling in multiple text classification, clustering, and retrieval tasks.

Speed Embedding 7B Instruct is a large-scale language model based on the Transformer architecture, focusing on text embedding and classification tasks, and has demonstrated outstanding performance in multiple benchmarks.

A 7B-parameter instruction-tuned model based on Qwen2 architecture, specializing in sentence similarity computation and text embedding tasks

A baby cry recognition model fine-tuned based on the distilhubert model, achieving an accuracy of 86.96% on the evaluation set

Gender classifier based on the HuBERT large model with 98.61% accuracy

A DistilBERT-based SMS spam classification model, fine-tuned on the sms_spam dataset, used to identify spam messages.

An image classification model fine-tuned based on Google's ViT architecture for skincare detection tasks, achieving a validation accuracy of 86.48%

Food image classification model trained on the Food101 dataset with an accuracy of 88.31%

A fine-tuned audio emotion classification model based on facebook/wav2vec2-base, achieving 73.98% accuracy on the evaluation set

A Persian text sentiment analysis model fine-tuned based on ParsBERT, achieving 87% accuracy on the evaluation set

This is an image classification model built based on PyTorch and HuggingPics, specifically designed to distinguish between Jeep and Toyota vehicles.

An image classification model generated by HuggingPics, specifically designed to identify different breeds of rare dogs.

This is an image classification model based on the PyTorch framework, specifically designed for recognizing sign language alphabets with 100% accuracy.

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

An image classification model based on PyTorch and HuggingPics for identifying different types of residences.

An image classification model based on PyTorch framework and HuggingPics tool, specifically designed for identifying rare dog breeds

Image classification model fine-tuned on CIFAR10 dataset based on ViT architecture

This is an image classification model generated based on PyTorch and HuggingPics, with an accuracy rate of 90.18%.

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

A pre-trained language model for the Korean financial domain, enhancing financial text processing performance through incremental pre-training and sentiment analysis fine-tuning

This is a Persian sentiment analysis model based on the mT5 architecture, capable of analyzing the emotional tendency of text.

A PyTorch-based image classification model for distinguishing between beer and wine pictures.

A sentiment analysis model fine-tuned based on the CAMeLBERT mixed model, supporting Arabic text sentiment classification