# High-Precision Semantic Matching

This is a cross-encoder model fine-tuned from BAAI/bge-reranker-base, designed for scoring text pairs, suitable for text reordering and semantic search tasks.

PyLate is a sentence similarity model based on the ColBERT architecture, using Alibaba-NLP/gte-modernbert-base as the base model and trained with distillation loss, suitable for information retrieval tasks.

BAAI/bge-large-zh-v1.5 is a Chinese sentence transformer model primarily used for feature extraction and sentence similarity calculation.

This is a cross-encoder model fine-tuned from ModernBERT-base for text re-ranking and semantic search tasks.

This is a preview version of a Japanese general-purpose reranking model, trained on the cl-nagoya/ruri-v3-pt-310m base model, specifically designed for Japanese text relevance ranking tasks.

An Arabic sentence transformer trained on Egyptian legal books and synthetic data, optimized for semantic text similarity and information retrieval tasks.

A finance-domain retrieval-augmented generation model optimized based on the stella_en_400M_v5 architecture, supporting semantic retrieval and paragraph matching for financial documents

Marsilia-Embeddings-FR-Base is a French embedding model specifically designed for financial domain tasks, demonstrating the importance of task-specific fine-tuning for embedding models in Retrieval-Augmented Generation (RAG) applications.

A text embedding model developed by Salesforce Research, trained on E5-mistral-7b-instruct and Mistral-7B-v0.1, primarily used for text retrieval tasks.

This dataset is used for training and evaluating the SimCSE (Simple Contrastive Learning of Sentence Embeddings) model, supporting sentence similarity tasks.

DRAGON+ is a dense retrieval model based on the BERT architecture, with initial weights derived from RetroMAE and trained on enhanced data from the MS MARCO corpus.