# Multi-round Optimization

Korean text summarization model based on T5 architecture, specifically fine-tuned for news reporting

Arabic sentiment analysis model fine-tuned on AraBERT, achieving 80.03% accuracy on the evaluation set

A French sentiment classification model fine-tuned on camembert-base, achieving 89.32% accuracy on the evaluation set

Indian English speech recognition model fine-tuned based on Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700

This is an Automatic Speech Recognition (ASR) model trained on an unknown dataset, achieving a Word Error Rate (WER) of 0.6309.

A model fine-tuned on the ComunaliRoma dataset based on the Italian BERT base model

This is an Indonesian sentiment analysis model based on the RoBERTa architecture, fine-tuned on the indonlu dataset, specifically designed for sentiment classification tasks in Indonesian SMS texts.

This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the MNLI dataset, primarily used for natural language inference tasks.