BGE-M3 model

**Overview**

The bge_m3 models are embedding models developed by Beijing Academy of Artificial Intelligence and packaged using MLflow’s sentence_transformers flavor. The models provided in this listing are

- [bge_m3](https://huggingface.co/BAAI/bge-m3)

Bge_m3 models are licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). By installing this listing, you acknowledge and agree to the license.

For example notebooks of using the bge_m3 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/bge/bge-m3).

**Use cases**

The bge_m3 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.

**概述**

bge_m3系列模型是由北京人工智能研究院开发的嵌入模型（embedding model），采用MLflow的sentence_transformers格式进行封装。本列表中提供的模型为：
- [bge_m3](https://huggingface.co/BAAI/bge-m3)

bge_m3系列模型采用[MIT许可证（MIT License）](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE)进行授权。安装本列表中的模型即代表您已知晓并同意该许可证条款。
如需了解在Databricks平台上针对各类应用场景使用bge_m3模型的示例笔记，请参考[Databricks ML示例仓库](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/embedding/bge/bge-m3)。

**应用场景**

bge_m3系列模型属于嵌入模型，可将文本映射至低维向量空间，典型应用场景包括：
- 检索增强生成（RAG，Retrieval-Augmented Generation）落地实现
- 基于聚类的数据可视化
- 语义搜索类文档搜索引擎

**产品详情**

本列表中的嵌入模型可直接部署至Databricks模型服务（Model Serving）以供即时使用，也可加载后进行微调或批量推理。如需了解更多细节，请安装本列表并查看各模型附带的模型卡片。

搜集汇总

背景与挑战

背景概述

BGE-M3模型由北京人工智能研究院开发，采用MIT许可证，适用于文本嵌入任务如RAG实现、数据聚类和语义搜索。该模型支持在Databricks平台直接部署或进行批量推理和微调。