# Semantic search optimization

A Romanian semantic understanding model fine-tuned based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2, compact in size yet outstanding in performance

A Romanian sentence transformer model fine-tuned from stsb-bert-tiny-safetensors, optimized for semantic similarity tasks

This model is converted from BAAI/bge-m3 to GGUF format via GGUF-my-repo, primarily used for sentence similarity calculation and feature extraction.

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

Giga Embeddings Instruct is a high-performance embedding model focused on text classification and retrieval tasks, excelling in multiple benchmark tests.

This is a SentenceTransformer model fine-tuned on jina-embeddings-v2-base-en for generating sentence embeddings and computing semantic similarity.

This is a Persian sentence embedding model based on TookaBERT-Large, which maps text to a 1024-dimensional vector space for tasks such as semantic similarity calculation.

This is a sentence-transformers model fine-tuned on a Korean triplet dataset based on Alibaba NLP/gte-multilingual-base, designed for semantic textual similarity tasks.

Stella 400M is a medium-scale English text processing model focused on classification and information retrieval tasks.

A Korean sentence embedding model fine-tuned from intfloat/multilingual-e5-small, supporting 384-dimensional vector representation, suitable for tasks like semantic similarity calculation

A Korean-optimized sentence embedding model based on BAAI/bge-m3, supporting 1024-dimensional vector representation, suitable for tasks like semantic similarity calculation

This is a Portuguese sentence encoder based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for tasks such as semantic search and text clustering.

A Portuguese sentence encoder based on sentence-transformers that can map text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

A sentence embedding extraction model specifically designed for Russian, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

A multilingual sentence embedding model based on sentence-transformers, supporting Catalan, capable of mapping text to a 768-dimensional vector space

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

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

This is a Portuguese-English sentence similarity model fine-tuned based on multilingual-e5-small, capable of mapping sentences to a 384-dimensional vector space.

This is a Spanish sentence similarity model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

This is a sentence transformer model based on IndoRoberta, capable of mapping Indonesian sentences and paragraphs into a 768-dimensional vector space, suitable for sentence similarity computation and semantic search tasks.

This is a sentence transformer version based on the intfloat/e5-large-v2 model, capable of mapping sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 128-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

Marathi sentence similarity model fine-tuned on STS dataset, supporting sentence embeddings and semantic similarity calculation

This is a multilingual sentence transformer model supporting English, Russian, Ukrainian, and Polish, primarily used for sentence similarity computation and feature extraction tasks.

This is an Indonesian sentence embedding model based on sentence-transformers that maps text to a 768-dimensional vector space

A sentence embedding model based on sentence-transformers that maps text to a 1024-dimensional vector space, suitable for semantic search and text clustering tasks.

Sentence embedding model based on MPNet architecture, mapping text to a 768-dimensional vector space, suitable for semantic search and sentence similarity calculation

This is a Korean sentence embedding model trained on KLUE/NLI and KLUE/STS datasets, utilizing the sentence-transformers framework and optimized for sentence similarity tasks through two-stage training.

A French sentence embedding model developed by La Javaness, fine-tuned based on the CamemBERT-large pre-trained model, focusing on French sentence semantic representation and similarity calculation.

A cross-encoder model based on sentence-transformers for sentence similarity calculation, particularly suitable for semantic search tasks.

This is a sentence-transformers model fine-tuned on Spanish semantic similarity datasets, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

This is a sentence embedding model based on sentence-transformers, capable of mapping text to a 512-dimensional vector space, suitable for semantic search and text similarity calculation.

SBERT-base-nli-v2 is a transformer-based sentence embedding model specifically designed for sentence similarity calculation and semantic search tasks.

A sentence embedding model optimized based on the GPT-2.7B architecture, specifically designed for semantic similarity calculation and natural language inference tasks