# Semantic search

A Korean sentence transformer fine-tuned based on intfloat/multilingual-e5-small for Korean retrieval 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.

French sentence embedding model based on CamemBERT-large, providing powerful semantic search capabilities

A long-context bi-encoder based on MosaicML's pre-trained BERT with 1024 sequence length, for sentence and paragraph embeddings

A long-context bi-encoder based on MosaicML's pre-trained BERT with 1024 sequence length, designed for generating 768-dimensional dense vector representations of sentences and paragraphs

This model maps sentences and paragraphs into a 1024-dimensional 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 to 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 to a 512-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 to a 768-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 to 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 to a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

Hindi BERT model trained on NLI dataset for sentence similarity calculation and feature extraction

This is a sentence embedding model based on sentence-transformers, which can convert text into a 768-dimensional vector representation.

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 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 or semantic search.

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

This is a sentence embedding model based on the MiniLM architecture that can map text to a 384-dimensional vector space and is suitable for semantic search and clustering tasks.

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

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.

A lightweight sentence embedding model based on the MiniLM architecture that can map text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

This is a sentence embedding model based on sentence-transformers, which can convert text into a 768-dimensional dense vector representation.

Latvian pre-trained language model based on BERT architecture, suitable for various natural language understanding tasks

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

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

This is a small sentence transformer model based on DistilBERT, supporting Hindi-English mixed language (Hinglish), capable of mapping text to a 768-dimensional vector space.

This is a sentence embedding model based on sentence-transformers that can map text to a 512-dimensional vector space, suitable for tasks such as semantic search and text similarity calculation.

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