# Multi-metric Optimization

A sentiment classification model fine-tuned based on BERTweet-base for analyzing sentiment tendencies in Bitcoin-related tweets on Twitter

Bank transaction classifier fine-tuned based on Flaubert small French model

A speech processing model fine-tuned based on facebook/wav2vec2-base, demonstrating excellent performance on the evaluation set

An audio emotion classification model fine-tuned based on wav2vec2-base, used to identify the speaker's emotional state.

A weather image classification model based on Vision Transformer architecture, fine-tuned on the Kaggle weather dataset with an accuracy of 93.4%

An image caption generation model based on the VisionEncoderDecoder architecture, using Swin Transformer as the visual encoder and GPT-2 as the decoder, fine-tuned on the COCO2014 dataset

A ViT-based multi-class image classification model for lung and colon cancer, achieving 99.94% accuracy on the evaluation set

ViT-based fine-tuned model for Simpsons character classification with 95.3% accuracy

A Vision Transformer (ViT) based model fine-tuned on the CIFAR-10 dataset for image classification tasks

A fine-tuned brain tumor image classification model based on Swin Transformer architecture, achieving 96.47% accuracy on the evaluation set

An image classification model fine-tuned based on microsoft/dit-base, performing well on the image folder dataset

A meme classification model fine-tuned based on Microsoft's ResNet-50 architecture, achieving 64.5% accuracy on image classification tasks

An image classification model fine-tuned based on microsoft/resnet-152, achieving 76.74% accuracy on a custom image dataset

A keyword extraction model fine-tuned based on bert-base-cased, excelling at identifying key information from text

A Chinese sentiment analysis model fine-tuned based on bert-base-chinese, performing well on Chinese sentiment corpora.

Fine-tuned model based on the GPT-2 architecture, optimized for specific tasks

A financial event summarization model fine-tuned based on BART-large architecture, with excellent performance on Rouge metrics

This model is a fine-tuned text classification model based on BioBERT, excelling in medical text classification tasks.

This model is a fine-tuned English question generation model based on BART-large, specifically designed to generate relevant questions from given text and answers.

A German abstract generation model fine-tuned based on the T5-small architecture, trained for 7 epochs on the mlsum German dataset with a Rouge1 score of 42.38