# Embedding Models

Embedding models convert text into dense vector representations, enabling semantic search, clustering, and similarity comparison. They are essential for building search engines, recommendation systems, and knowledge retrieval applications.

* [<mark style="color:blue;">**BGE Large 1.5**</mark>](https://docs.ionos.com/sections-test/guides/ai/ai-model-hub/models/embedding-models/bge-large-1-5): Maps English text to a 1024-dimensional dense vector space, supporting high-quality semantic search and retrieval tasks.
* [<mark style="color:blue;">**BGE m3**</mark>](https://docs.ionos.com/sections-test/guides/ai/ai-model-hub/models/embedding-models/bge-m3): Handles long, multilingual text, mapping it to a 1024-dimensional vector space for cross-lingual search and analysis.
* [<mark style="color:blue;">**Paraphrase Multilingual MPNet v2**</mark>](https://docs.ionos.com/sections-test/guides/ai/ai-model-hub/models/embedding-models/paraphrase-multilingual-mpnet-v2): A sentence transformer model that maps text to a 768-dimensional vector space, enabling efficient paraphrase detection and semantic similarity tasks.