# High-accuracy classification

GTE-small is an efficient sentence embedding model based on the thenlper/gte-small foundation model, focusing on sentence similarity tasks.

A BERT-based Spanish sentiment analysis classifier used to detect positive and negative sentiments in text.

A fine-tuned multilingual speech classification model based on Whisper-small, supporting speech recognition and classification for Uzbek, English, and Russian.

A bird sound classification model based on facebook/wav2vec2-base-960h, used to identify different bird species by sound.

A ResNet-50 fine-tuned image classification model for identifying 36 common fruits and vegetables

A Russian speech emotion recognition model fine-tuned based on facebook/wav2vec2-xls-r-300m, capable of identifying emotions such as neutral, positive, angry, and sad.

This model is a fine-tuned version based on ibert-roberta-base, specifically designed for detecting abusive or threatening speech.

An image classification model based on ViT architecture, fine-tuned on the FER2013 dataset for facial expression recognition tasks.

This is a multi-class classification model for detecting gibberish text with an accuracy rate as high as 97.35%.

A vision Transformer model based on BEiT architecture, fine-tuned on the FER2013 dataset for facial expression recognition tasks

This is a multi-category image classification model trained by AutoTrain, specifically designed for analyzing vehicle images.

A fine-tuned speech processing model based on facebook/wav2vec2-base, focusing on balanced positive-negative class classification tasks

A multilingual speech recognition model fine-tuned on the GOOGLE/XTREME_S - MINDS14.ALL dataset based on facebook/wav2vec2-xls-r-300m

A text classification model fine-tuned on the Amazon multilingual reviews dataset based on the roberta-base-bne model, primarily used for sentiment analysis of Spanish comments

A voice command recognition model trained with SpeechBrain, based on the Google Speech Commands dataset, capable of recognizing 12 keywords.

A fake news classification model fine-tuned based on ALBERT-base-v2, capable of accurately distinguishing between fake and real news