# Low Training Loss

This model is a fine-tuned version of Audio Spectrogram Transformer (AST) on the GTZAN music classification dataset for audio classification tasks, achieving an accuracy of 88%.

An audio classification model fine-tuned on the GTZAN dataset based on distilhubert, achieving 83% accuracy

This model is a speech intent classification model fine-tuned from facebook/wav2vec2-base, achieving an F1 score of 0.875 on the evaluation set.

This model is based on albert-base-v2's masked language model, specifically fine-tuned for electronic product attribute correction tasks

A keyword extraction model fine-tuned based on bert-base-uncased, suitable for English text keyword extraction tasks

This is an automatic speech recognition (ASR) model trained on the LibriSpeech dataset, capable of converting English speech into text.

This is an extractive QA model trained with AutoNLP, capable of extracting answers from given texts.

A multilingual text classification model fine-tuned based on the bert-base-multilingual-uncased model