# High-precision Semantic Matching

This is a cross-encoder model fine-tuned from cross-encoder/ms-marco-MiniLM-L12-v2, specifically designed for re-ranking and semantic search tasks related to climate texts.

A sentence transformer model supporting multiple languages, suitable for text ranking tasks

Snowflake Arctic Embed M v2.0 is a multilingual sentence embedding model that supports over 50 languages, focusing on sentence similarity and feature extraction tasks.

Ruri-Reranker is a Japanese text re-ranking model based on sentence-transformers, specifically designed to optimize the relevance ranking between queries and documents.

This model is a fine-tuned version based on jina-embeddings-v2-base-en, specifically designed for technical stack document processing, suitable for tasks such as text classification, sentiment analysis, and entity recognition.

A Dutch legal text embedding model fine-tuned based on jina-embeddings-v2-base-en, specifically optimized for legal document retrieval tasks

A text embedding model fine-tuned based on jina-embeddings-v2-base-en, specifically optimized for legal consultation search engines, supporting feature extraction for Chinese and English legal texts.

Snowflake Arctic Embed M is a sentence transformer model focused on sentence similarity tasks, capable of efficiently extracting text features and calculating similarity between sentences.

A sentence-transformers-based text embedding model focused on sentence similarity calculation and feature extraction tasks.

This is a sentence embedding model based on the MPNet architecture, specifically optimized for handling negation sentences, suitable for sentence similarity calculation tasks.

A sentence embedding model fine-tuned based on sentence-transformers/all-mpnet-base-v2, suitable for sentence similarity tasks

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.