# Few-shot learning

Vision-language model fine-tuned based on CLIP-ViT-B/32, suitable for image-text matching tasks

A zero-shot classification model fine-tuned from MoritzLaurer/deberta-v3-base-zeroshot-v2.0-c using the SmartShot method with an NLI framework

This is an efficient few-shot learning model based on SetFit, used for text classification tasks. It uses sentence-transformers/paraphrase-MiniLM-L6-v2 as the sentence embedding model and LogisticRegression for classification.

This model is a fine-tuned version based on microsoft/deberta-v3-base, achieving an outstanding accuracy of 99.87% on the evaluation set.

An image segmentation model fine-tuned based on nvidia/mit-b0, trained on the NICOPOI-9/morphpad_hgo_512_4class dataset, excelling in high-precision image segmentation tasks.

This model was trained from scratch on an unknown dataset and achieved good performance on the evaluation set, providing effective support for related tasks.

An image classification model based on Google Vision Transformer (ViT) architecture, specifically fine-tuned for the beans dataset

A cat-dog image classification model fine-tuned based on microsoft/resnet-50, achieving 95% accuracy on the evaluation set

A fine-tuned tomato leaf disease classification model based on Google Vision Transformer (ViT) architecture, achieving 99.67% accuracy on the evaluation set

This model is a text classification model fine-tuned on an unknown dataset based on PhoBERT-base-v2. It achieved an accuracy of 99.67% and an F1 score of 99.32% on the evaluation set.

Sapiens is a Vision Transformer model pre-trained on 300 million 1024×1024 resolution human images, excelling in human-centric vision tasks.

The Sunfall model is a natural language processing model developed based on Llama-3.1-8B-Stheno-v3.4, suitable for specific functions and application scenarios.

ⓍTTS is a speech generation model that can clone voices with just 6 seconds of audio and apply them to different languages. No need for hours of extensive training data.

An image classification model fine-tuned based on Google's ViT-base-patch16-224 model, achieving an accuracy of 68.97% on the evaluation set

This model is a fine-tuned image classification model based on Google's ViT-base-patch16-224 on an image folder dataset, achieving 89.21% accuracy on the validation set.

A fine-tuned emotion image classification model based on Google's ViT model, achieving an accuracy of 59.38% on the validation set.

A few-shot text classification model trained with AutoTrain, suitable for news article classification tasks

CosmicBun-8B is a merged model based on the Llama3-8B architecture, specializing in text generation tasks for scientific fields such as mathematics, physics, chemistry, and biology.

A cross-token large language model developed for the Tatar language, converted from Mistral-7B-Instruct-v0.2

A Turkish language model based on ytu-ce-cosmos/turkish-gpt2-medium, fine-tuned with a 35,000-instruction dataset, suitable for various text generation tasks

orthogonal-2x7B-v2-base is a Mixture of Experts model based on Mistral-7B-Instruct-v0.2 and SanjiWatsuki/Kunoichi-DPO-v2-7B, specializing in text generation tasks.

An image classification model fine-tuned on the Pokemon classification dataset based on Google's ViT model

This is an aspect-level sentiment analysis model based on the SetFit framework, specifically designed for aspect term identification in restaurant reviews.

A vision model fine-tuned based on google/vit-base-patch16-224, achieving an F1 score of 0.9931 on the evaluation set

This is a SetFit model trained on the sst2 dataset for English text classification tasks, utilizing efficient few-shot learning techniques.

This model is a fine-tuned image classification model based on google/vit-base-patch16-224-in21k on the CIFAR10 dataset, achieving an accuracy of 96.78%.

Tiny Vicuna 1B is a fine-tuned version based on TinyLlama using the WizardVicuna dataset, compatible with the Vicuna-v1.5 series, suitable for early experimental iterations.

A 7B-parameter large language model based on the Mistral architecture, utilizing SLERP fusion technology to merge weights from multiple high-performance models

Sabiá-7B is a Portuguese large language model developed by Maritaca AI, based on the LLaMA-1-7B architecture and specifically optimized for Portuguese language.

An image classification model fine-tuned on the CIFAR-10 dataset based on the Vision Transformer (ViT) architecture, achieving 99.2% accuracy

A model fine-tuned based on microsoft/deberta-v3-large for evaluation tasks. The specific task type is unknown.

IDEFICS is an open-source multimodal model capable of processing both image and text inputs to generate text outputs, serving as an open-source reproduction of Deepmind's Flamingo model.

Lightweight image classification model based on DeiT-tiny architecture, achieving 98.56% validation accuracy after fine-tuning on a custom image dataset

A vision Transformer model fine-tuned based on google/vit-base-patch16-224, suitable for image classification tasks

Fine-tuned version based on Google's ViT model, achieving 99.4% validation accuracy on image classification tasks

A vision model fine-tuned based on google/vit-base-patch16-224, specific purpose not clearly stated

Vision Transformer model fine-tuned on flower image dataset based on Google's ViT model

An image classification model based on Swin Transformer architecture, fine-tuned on an image folder dataset with an accuracy of 96.67%

This model is an image classification model fine-tuned on the beans dataset based on Google's ViT architecture, achieving 97.74% accuracy on the validation set.

This is a binary classification vision model trained using AutoTrain, specifically designed for image classification tasks.