# Semantic similarity calculation

This is a multilingual sentence embedding model that can map sentences and paragraphs to a 768-dimensional vector space, suitable for tasks such as clustering and semantic search.

A CrossEncoder model based on the Transformer architecture, fine-tuned on the Quora question pair dataset, used to calculate scores for text pairs, suitable for semantic similarity and semantic search tasks.

This is a model based on the Cross Encoder architecture, specifically designed for text pair classification tasks. It is fine-tuned on the Quora question pair dataset and is suitable for semantic similarity judgment and semantic search scenarios.

A sentence transformer model fine-tuned based on Redis Langcache Embed v1, used to generate 768-dimensional sentence embedding vectors

This is a sentence-transformers model fine-tuned from intfloat/multilingual-e5-small, trained with Korean query-passage pairs to enhance performance in Korean retrieval tasks.

This model is distilled from all-MiniLM-L12-v2, achieving nearly 2x faster inference speed while maintaining high accuracy on both CPU and GPU.

This is a SentenceTransformer model fine-tuned from Snowflake/snowflake-arctic-embed-l-v2.0, trained on a clustering dataset. It maps sentences and paragraphs into a 1024-dimensional dense vector space, suitable for semantic text similarity and semantic search.

Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, focusing on sentence similarity calculation and feature extraction tasks.

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

A sentence embedding model fine-tuned on the Korean triplet dataset based on the Alibaba-NLP/gte-multilingual-base model for semantic similarity calculation

This is a sentence transformer model fine - tuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2, which can map text to a 384 - dimensional vector space for tasks such as semantic similarity calculation.

stella_en_400M_v5_cpu is a model that performs excellently in multiple natural language processing tasks, especially in tasks such as classification, retrieval, clustering, and semantic text similarity.

A Korean sentence embedding model fine - tuned on Alibaba - NLP/gte - multilingual - base, supporting tasks such as semantic text similarity calculation and semantic search.

This is a sentence-transformers model fine-tuned on intfloat/multilingual-e5-small, designed to map sentences and paragraphs into a 384-dimensional dense vector space, supporting tasks such as semantic text similarity and semantic search.

Russian universal sentence encoder, based on the sentence-transformers framework, specifically designed to extract 1024-dimensional dense vectors for Russian text

BERT model for computing Russian sentence embeddings, developed based on cointegrated/LaBSE-en-ru with optimized Russian language processing performance

Jina Embeddings V2 Base is a sentence embedding model optimized for Chinese, which can convert text into high-dimensional vector representations for calculating sentence similarity and feature extraction.

LLM2Vec is a supervised learning model based on Meta-Llama-3, focusing on natural language processing tasks such as sentence similarity, and supporting various application scenarios such as text embedding, information retrieval, and text classification.

multilingual - e5 - small is a model that performs excellently in multilingual text processing tasks, supporting various tasks such as classification, retrieval, clustering, re - ranking, and semantic text similarity.

A Russian encoder model based on the RoPEBert architecture, trained using cloning methods, supports 512-token context, and surpasses the original ruBert-base model in quality

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

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

SimCSE(sup) is a model for Chinese sentence similarity tasks. It can encode sentences into embedding vectors and calculate the cosine similarity between sentences.

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

ONNX version of the Dutch sentence transformer based on the RobBERT model, mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks

This is a Hebrew sentence similarity calculation model based on sentence-transformers, which can map text to a 512-dimensional vector space for semantic search and clustering tasks

This is a Japanese sentence embedding model trained using the supervised SimCSE method, specifically designed for generating high-quality sentence representations.

BGE Base English v1.5 is a transformer-based sentence embedding model, specifically designed for extracting sentence features and calculating sentence similarity.

This is a vocabulary-pruned version of intfloat/multilingual-e5-base, retaining only English and Russian vocabulary.

This is a sentence embedding model based on sentence-transformers, capable of converting text into 1024-dimensional dense vector representations, suitable for tasks like semantic search and text clustering.

This is a multilingual sentence embedding model that maps text to a 512-dimensional vector space, suitable for semantic search and clustering tasks.

indoSBERT-large is an Indonesian sentence embedding model based on sentence-transformers, which maps sentences and paragraphs into a 256-dimensional vector space, suitable for tasks such as clustering and semantic search.

CompCSE and SimCSE are contrastive learning-based sentence embedding models for calculating sentence similarity.

A Chinese sentence embedding model based on the CoSENT (Cosine Sentence) model, mapping sentences to a 768-dimensional dense vector space, suitable for tasks such as sentence embedding, text matching, or semantic search.

A model based on sentence-transformers for mapping target words in sentences to a 1024-dimensional vector space, supporting word similarity calculation and semantic search tasks.

A model that maps abstract sentence descriptions to matching sentences, trained on Wikipedia using a dual-encoder architecture.

This is a sentence embedding model based on the ConGen framework, capable of mapping sentences to a 128-dimensional dense vector space, suitable for tasks such as semantic search.

Multilingual sentence embedding model that maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks

This is a Polish-language sentence embedding model capable of mapping sentences and paragraphs into a 1024-dimensional vector space, suitable for semantic search and clustering tasks.

A sentence transformer model based on ult5-pt-small that maps sentences and paragraphs into 512-dimensional vectors, suitable for tasks like text clustering, similarity calculation, and semantic search.