# Fine-tuned Model

A text classification model fine-tuned on distilbert-base-uncased, trained on the AG News dataset with an accuracy of 89.06%

A text summarization model fine-tuned based on the T5-small architecture, specifically designed for processing stock news content

A text summarization model fine-tuned on the XSum dataset based on T5-small

A video analysis model fine-tuned based on MCG-NJU/videomae-base for deception detection tasks, achieving an accuracy of 70.37%

A fine-tuned DistilBERT-base-uncased food/non-food text classifier achieving 100% accuracy on the evaluation set

A sentiment analysis model fine-tuned based on distilbert-base-uncased for text sentiment classification tasks

This model is a fine-tuned version based on MCG-NJU/videomae-base-finetuned-kinetics, primarily used for video understanding tasks, achieving an accuracy of 91.38% on the evaluation set.

A video understanding model fine-tuned based on MCG-NJU/videomae-base-finetuned-kinetics, achieving 91.23% accuracy on the evaluation set

A video understanding model fine-tuned based on MCG-NJU/videomae-base-finetuned-kinetics, achieving an accuracy of 90.97% on the evaluation set

This is a Haitian Creole text-to-speech model, fine-tuned based on jsbeaudry/haitian_creole.

A financial domain sentiment analysis model fine-tuned based on econo-sentence-v2, achieving 96.2% accuracy on the Financial Phrase Bank dataset

A lightweight sentiment analysis model based on DistilBERT, suitable for text sentiment classification tasks

An audio classification model fine-tuned based on MIT/ast-finetuned-audioset-10-10-0.4593, specializing in heart sound classification tasks, achieving 96.95% accuracy on the validation set.

A fine-tuned speech processing model based on facebook/wav2vec2-base, achieving 99.48% accuracy on the evaluation set

A fine-tuned CAPTCHA OCR model based on microsoft/trocr-base-handwritten, trained on the nj_biergarten_captcha_v2 dataset

An object detection model fine-tuned from microsoft/conditional-detr-resnet-50, suitable for target detection tasks in specific scenarios

A fine-tuned audio classification model based on openai/whisper-tiny, excelling in voice command datasets

An object detection model fine-tuned for 20 epochs on a boat dataset, based on the facebook/detr-resnet-50 model

This model is an object detection model fine-tuned on a grasshopper detection dataset, based on Facebook's DETR-ResNet-50-DC5 architecture.

A sentiment analysis model based on DistilBERT, fine-tuned on an unknown dataset with an accuracy of 56.16%

This model is a fine-tuned object detection model based on microsoft/conditional-detr-resnet-50, specifically designed for trash detection tasks, with data augmentation techniques applied during training.

A fine-tuned version based on MCG-NJU/videomae-large model on the deepfake detection challenge dataset, used for video deepfake detection.

An image captioning model based on the ViT-GPT2 architecture, capable of generating natural language descriptions for input images.

This model is a fine-tuned version of google/flan-t5-base on the samsum dialogue summarization dataset, specifically designed for dialogue summarization tasks.

A text classification model fine-tuned based on distilbert-base-uncased, demonstrating outstanding performance on the evaluation set with both accuracy and F1 score reaching 1.0.

This model is a fine-tuned audio classification model based on asapp/sew-mid-100k on the alexandreacff/kaggle-fake-detection dataset, designed for fake audio detection.

A fine-tuned speech synthesis model based on microsoft/speecht5_tts, trained on an unknown dataset with a validation loss of 0.3771.

A fine-tuned printed license plate OCR model based on microsoft/trocr-base-printed, with a character error rate of 0.037 on the evaluation set

A speech processing model fine-tuned based on the facebook/wav2vec2-base model, achieving 99.97% accuracy on the evaluation set

A speech processing model fine-tuned based on the facebook/wav2vec2-base model, achieving 99.97% accuracy on the evaluation set

This model is a fine-tuned speech recognition model based on OpenAI's Whisper large on the Common Voice 14.0 Swahili (SW) dataset, achieving a word error rate (WER) of 25.13%.

An audio classification model fine-tuned on an audio folder dataset based on the wav2vec2-base model, achieving 99.82% accuracy on the validation set

This model is a dialect classifier based on the whisper-small architecture, designed to recognize and classify speech inputs of specific dialects.

Handwriting recognition model fine-tuned based on naver-clova-ix/donut-base

Fine-tuned Indian Sign Language image classification model based on Google Vision Transformer (ViT) architecture, achieving 99.05% accuracy

A Reranker model fine-tuned on Korean data based on BAAI/bge-reranker-large, designed to enhance Korean Retrieval-Augmented Generation (RAG) performance

An object detection model fine-tuned on a food image dataset based on facebook/detr-resnet-101

A language model fine-tuned on the wikitext2 dataset based on the GPT-2 architecture

A captcha recognition model fine-tuned based on Microsoft's trocr-base-printed model, specifically designed for OCR tasks involving printed text

This model is a fine-tuned version of facebook/detr-resnet-50 on a furniture dataset for object detection tasks.