# Semantic Similarity

GPTQ quantized version of Qwen3-Embedding-0.6B, optimized for video memory usage with minimal performance loss

Ruri is a Japanese universal text embedding model based on ModernBERT-Ja, offering versions with different parameter scales suitable for various text processing tasks.

This is a sentence-transformers model fine-tuned from dunzhang/stella_en_1.5B_v5, designed to generate 1024-dimensional dense vector representations for sentences and paragraphs, suitable for tasks like semantic text similarity and semantic search.

NoInstruct Small Embedding Model v0 is an improved embedding model focused on enhancing retrieval task performance while maintaining independence from arbitrary instruction encoding.

A sentence embedding model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and text similarity calculation

GGUF format version of the General Text Embedding (GTE) model, suitable for tasks like information retrieval and semantic text similarity.

This is a sentence-transformers-based sentence embedding model that maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

GTE-Base is a general-purpose text embedding model capable of converting text into high-dimensional vector representations, suitable for text classification and similarity search tasks.

GTE-Large is a text embedding model ported to ONNX, suitable for text classification and similarity search tasks.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 256-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a sentence similarity model based on sentence-transformers, which maps text to a 384-dimensional vector space for semantic search and clustering tasks.

This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic similarity and feature extraction tasks.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is the quantized version of multilingual-e5-small, optimized for inference performance through layer-wise quantization while retaining most of the original model's quality.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a vocabulary-pruned version of the intfloat/multilingual-e5-large model, retaining only Russian and English tokens while maintaining the original model's performance.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering and semantic search.

This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a sentence embedding model based on sentence-transformers, capable of converting text into 768-dimensional vector representations, suitable for tasks such as semantic similarity calculation.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a sentence embedding model based on sentence-transformers, capable of converting text into 384-dimensional vector representations.

DRAGON+ is a dense retrieval model based on the BERT architecture, employing an asymmetric dual-encoder architecture, suitable for text retrieval tasks.

A Transformer-based text ranking model for sorting sentences or paragraphs by relevance.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for tasks such as semantic search and text similarity calculation.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 64-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering and semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for tasks such as semantic search and text similarity calculation.

A Chinese sentence vector representation model based on the unsupervised version of SimCSE, trained with supervised contrastive learning using Chinese NLI data