E5 v2 Models

**Overview** The e5_v2 models are embedding models developed by Microsoft and packaged using MLflow’s sentence_transformers flavor. The models provided in this listing are - [e5_base_v2](https://huggingface.co/intfloat/e5-base-v2) - [e5_large_v2](https://huggingface.co/intfloat/e5-large-v2) - [e5_small_v2](https://huggingface.co/intfloat/e5-small-v2) E5_v2 models are licensed under the [MIT License](https://github.com/microsoft/unilm/blob/master/LICENSE). By installing this listing, you acknowledge and agree to the license. For example notebooks of using the e5_v2 models in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/embedding/e5-v2). **Use cases** The e5_v2 models are embedding models that map text to low-dimensional vectors, and some example use cases are: - RAG implementation - Data visualization with clustering - Semantic search document search engine **Product details** The embedding models in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. For more details, install the listing and view the provided model cards for each model.