# Multi-emotion classification

A speech emotion recognition model fine-tuned based on WavLM-Base, capable of classifying audio into 7 different emotions

A Russian emotion classification model fine-tuned based on lightweight RuBERT-tiny2, capable of recognizing five emotions

This model is fine-tuned based on the Wav2Vec2 architecture, specifically designed to recognize six emotional states (sadness, anger, disgust, fear, happiness, neutral) in English speech, with an accuracy of 92.42%.

A multi-category emotion classification model fine-tuned on ModernBERT-base, capable of recognizing 7 emotion labels

A Wav2Vec2-fine-tuned English speech emotion recognition model capable of identifying six emotional states

A Czech emotion classification model fine-tuned on RoBERTa architecture, supporting six emotion categories

A facial expression recognition model fine-tuned based on a pre-trained model, which can effectively recognize eight different facial expressions.

A facial emotion recognition model fine-tuned based on Google's ViT-base model, achieving 91% accuracy on the test set.

A Chinese speech emotion classification model fine-tuned on the CASIA dataset using Tencent Game Partner's pre-trained Chinese HuBERT model, supporting 6 emotion categories.

A text-based emotion classification model capable of identifying six emotions: anger, disgust, fear, happiness, neutrality, and sadness.

A Russian speech emotion classification model fine-tuned based on AST architecture, supporting recognition of 7 emotional states

A text classification model based on the RoBERTa architecture, specifically fine-tuned for the GoEmotions dataset to recognize 28 different emotions.