# Fine-Tuned Model

A ModernBERT-base fine-tuned model for financial news sentiment analysis, achieving an F1 score of 0.9765 on the evaluation set

Fine-tuned based on Google's ViT model for melanoma cancer image classification, achieving an accuracy of 93.95%.

DETR object detection model fine-tuned on an image folder dataset, based on facebook/detr-resnet-50

An audio classification model based on the TRillsson3 architecture, fine-tuned on the superb dataset for keyword recognition tasks

Agricultural vision image classification model based on Swin Transformer Tiny architecture, fine-tuned on the imagefolder dataset with an accuracy of 92.03%

An image segmentation model fine-tuned on the inStryde/inStrydeSegmentationFoot dataset, based on nvidia/mit-b5

This model is a fine-tuned version of facebook/wav2vec2-base, primarily used for speech recognition tasks.

A vision-language model based on ViT-GPT2 architecture for image-to-text tasks

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset for Hindi speech recognition tasks.

A sequence-to-sequence language model based on the mbart50 architecture, supporting text generation tasks from Sinhala to English