Model Selection

Semantic Understanding Enhancement

# Semantic Understanding Enhancement

Vit So400m Patch14 Siglip Gap 224.v2 Webli

A ViT image encoder based on SigLIP 2, employing global average pooling with the attention pooling head removed, suitable for image feature extraction tasks.

Image Classification

Vit Large Patch16 Siglip Gap 512.v2 Webli

A vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction, using Global Average Pooling (GAP) instead of attention pooling head

Image Classification

Vit Large Patch16 Siglip Gap 256.v2 Webli

A ViT image encoder based on SigLIP 2, employing global average pooling with the attention pooling head removed, specifically designed for image feature extraction.

Vit Base Patch32 Siglip Gap 256.v2 Webli

A vision Transformer model based on SigLIP 2, using Global Average Pooling (GAP) instead of attention pooling head for image encoding

Vit Base Patch16 Siglip Gap 256.v2 Webli

A ViT image encoder based on SigLIP 2, employing global average pooling with the attention pooling head removed, suitable for image feature extraction.

Multimodal Fusion

Vit Base Patch16 Siglip Gap 224.v2 Webli

Vision Transformer model based on SigLIP 2, utilizing global average pooling for image features

Image Classification

Siglip2 Large Patch16 384

SigLIP 2 is an improved multilingual vision-language encoder based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction capabilities.

Mbert Multiconer22 Hi

This model is specifically designed for the SemEval Multiconer task, serving as a named entity recognition (NER) model to identify complex entity categories in multilingual and cross-domain texts.

Sequence Labeling

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