# Semantic Similarity Calculation

This is a multilingual sentence embedding model capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as clustering and semantic search.

This is the quantized ONNX version of Alibaba-NLP/gte-multilingual-reranker-base, utilizing INT8 quantization, optimized for GPU, and suitable for text classification tasks.

A fine-tuned version of the bge-m3 model optimized for legal document retrieval tasks, specifically designed to handle the nuances of legal language, enabling accurate and efficient retrieval of relevant legal documents based on semantic similarity.

This is a model trained based on the sentence-transformers framework, specifically designed for semantic matching and similarity calculation of job titles.

This is a PyLate model fine-tuned from answerdotai/ModernBERT-base on the MS-MARCO dataset, designed for sentence similarity calculation and document retrieval.

Multilingual E5 Large is a multilingual sentence transformer model focused on sentence similarity and feature extraction tasks, supporting multiple languages.

A document embedding model for Vietnamese, supporting contexts up to 8096 tokens, trained based on gte-multilingual

NaSE is a news domain-specialized multilingual sentence encoder, based on LaBSE with domain-specific training, supporting sentence embedding and similarity calculation for 100+ languages.

InBedder is a text embedder specifically designed to follow instructions, capable of capturing text features specified by user instructions through question-answering.

A text embedding model fine-tuned based on BAAI/bge-small-en-v1.5, trained with the MEDI dataset and MTEB classification task datasets, optimized for query encoding in retrieval tasks.

BAAI/bge-base-en-v1.5 is a Transformer-based English text embedding model, primarily used for sentence similarity calculation and text classification tasks.

This is a lightweight sentence embedding model based on the BERT architecture, capable of converting sentences and paragraphs into 128-dimensional dense vectors, suitable for tasks such as semantic similarity calculation.

This is a multilingual sentence embedding model based on sentence-transformers, capable of mapping text to a 1024-dimensional vector space, suitable for semantic search and clustering tasks.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 1536-dimensional dense vector space.

GTE Tiny is a small general-purpose text embedding model suitable for various natural language processing tasks.

Quantized (INT8) ONNX variant of BGE-large-en-v1.5 with inference acceleration via DeepSparse

A Japanese sentence embedding model fine-tuned using supervised SimCSE method, suitable for sentence similarity calculation and feature extraction tasks.

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

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

GTE-small is a general text embedding model trained by Alibaba DAMO Academy, based on the BERT framework, suitable for tasks such as information retrieval and semantic text similarity.

E5-base-multilingual-4096 is a locally sparse global version based on intfloat/multilingual-e5-base, supporting multilingual text embedding models that can process up to 4096 tokens.

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 maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

A sentence transformer model optimized for phrases, mapping phrases into a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

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

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 768-dimensional dense vector space 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 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 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 1024-dimensional vector representations, suitable for tasks such as sentence similarity calculation and semantic search.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional 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 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

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

A sentence embedding model based on sentence-transformers that maps text to a 768-dimensional vector space

This is a sentence embedding model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space.

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

This is a sentence similarity calculation model based on sentence-transformers, capable of mapping text to a 384-dimensional vector space

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

This is a version of the Contriever MSMARCO model adapted for the Sentence Transformer framework, capable of mapping text to a 768-dimensional dense vector space, suitable for semantic search and clustering tasks.

A sentence embedding model compressed via homomorphic projection distillation, containing only 14 million parameters with a model size of 55MB, suitable for semantic retrieval tasks