cohere
The Cohere Embed-English-V3.0 model, a recent addition to Cohere's Embed V3 family, is setting new standards in the realm of text embeddings. Designed for the English language, this model transforms text into high-dimensional vector embeddings, making it ideal for a variety of applications, including semantic search, text
azure-ai
Azure AI has integrated the cutting-edge Cohere Embed v3 - English model, a state-of-the-art text embedding model that transforms text into 1024-dimensional numerical vectors. This model is designed to excel in various tasks including semantic search, retrieval-augmented generation (RAG), classification, and clustering. Model Overview The Cohere Embed v3 - English
databricks
The Databricks GTE Large (English) model is a cutting-edge text embedding model designed to enhance your data processing capabilities. Leveraging the prowess of Databricks Foundation Model APIs, this model offers a robust solution for various text-related tasks. Here are the key features that make Databricks GTE Large a standout: * Model
databricks
The databricks/databricks-bge-large-en model is a part of the BAAI General Embedding (BGE) series, specifically designed for generating high-quality text embeddings. This model boasts an embedding dimension of 1024 and a sequence length of 512, delivering exceptional performance across tasks such as retrieval, clustering, and pair classification. Integration with Databricks
cohere
The Cohere Embed-English-Light-v3.0 model is a recent addition to Cohere's suite of embedding models, engineered for efficient text embeddings. It's a leaner, faster option tailored for English text, making it an ideal choice for applications where speed and lower-dimensional embeddings are paramount. Dimensions and Performance