# Semantic Search Optimization

A Transformer-based multilingual text ranking model supporting text relevance ranking tasks in 15 languages

This is a sentence-transformers model fine-tuned from nomic-ai/nomic-embed-text-v2-moe, which maps text to a 768-dimensional dense vector space for tasks such as semantic text similarity calculation.

cde-small-v1 is a small sentence embedding model based on transformer architecture, excelling in multiple text classification, clustering, and retrieval tasks.

A Korean-English bilingual sentence embedding model optimized based on BAAI/bge-m3, supporting semantic text similarity, information retrieval, and other tasks

GGUF quantized version of the bge-m3 embedding model, suitable for efficient text embedding tasks

This is an ONNX port of sentence-transformers/all-MiniLM-L6-v2, adjusted to return attention weights, specifically designed for BM42 search scenarios.

mxbai-embed-large-v1 is a sentence embedding model based on the BERT-large architecture, trained using AnglE loss function, supporting English text embedding, and offering multiple quantized versions to meet various needs.

BAAI/bge-large-zh-v1.5 embedding models in GGUF format, both quantized and non-quantized, optimized for llama.cpp, delivering significant speedup on CPU with minimal precision loss.

Jina Embeddings V2 Base is an efficient English text embedding model capable of converting text into high-dimensional vector representations, suitable for various natural language processing tasks.

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 text similarity calculation.

A sentence-transformers model developed by Hum, supporting 4096 tokens long text embedding, suitable for semantic search and clustering tasks

This is a sentence transformer model that maps text to a dense vector space, suitable for semantic search and clustering tasks.

A semantic search model based on BERT architecture, optimized for information retrieval systems, capable of mapping text to a 768-dimensional vector space

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

GLuCoSE is a Japanese text embedding model based on LUKE, suitable for sentence similarity and semantic search tasks.

This is a Hindi sentence similarity model fine-tuned on the STS dataset, supporting semantic similarity calculation between Hindi sentences.

An ONNX model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

This is an ONNX 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 sentence transformer model based on DistilBERT, specifically designed for feature extraction and sentence similarity calculation.

A sentence embedding model based on DistilBert TAS-B, optimized for semantic search tasks, capable of mapping text to a 768-dimensional vector space

A DistilBERT model with word2vec-initialized vocabulary, optimized for sentence similarity tasks and trained on the MS MARCO dataset

A sentence embedding model based on the DistilBERT architecture, initialized with a 256k vocabulary and word2vec, trained on the MS MARCO dataset, suitable for sentence similarity computation and semantic search tasks.

A sentence embedding model trained on the MS MARCO dataset, using a word2vec-initialized 256k vocabulary and DistilBERT architecture, suitable for semantic search and sentence similarity tasks

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

SBERT-large-nli-v2 is a large-scale sentence transformer model based on BERT, specifically designed for sentence similarity calculation and feature extraction.

SGPT is a sentence embedding model based on the GPT architecture, specifically designed for semantic search tasks, generating sentence representations through weighted mean pooling.