FlagEmbedding maps text to a low-dimensional dense vector, which can be used for tasks like retrieval, classification, clustering, or semantic search. It also can be used in vector databases for LLMs.
This model has been fine-tuned on domain specific data and is for demonstration purposes and not for production usage.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to the HuggingFace age model.
https://huggingface.co/BAAI/bge-large-en#license
For more details please refer to the Github: FlagEmbedding repo.
Runtime Engine(s): Holoscan SDK
Supported Hardware Microarchitecture Compatibility:
[Preferred/Supported] Operating System(s):
v 0.1
Link: [https://github.com/synthetichealth/synthea]
Data Collection Method by dataset: Synthetic
Labeling Method by dataset: Synthetic
Properties (Quantity, Dataset Descriptions, Sensor(s)): 2500 synthetic electronic healthcare record (EHR) document and question pairs
Dataset License(s): [https://github.com/synthetichealth/synthea/blob/master/LICENSE]
Link: internal dataset generated by LLM
Data Collection Method by dataset: Automated
Labeling Method by dataset: Automated
Properties (Quantity, Dataset Descriptions, Sensor(s)): 2500 Q/A pairs from synthea dataset
Dataset License: [https://github.com/synthetichealth/synthea/blob/master/LICENSE]
Link: synthetic dataset generated by LLM
Data Collection Method by dataset: Automated
Labeling Method by dataset: Automated
Properties (Quantity, Dataset Descriptions, Sensor(s)): 15 Q/A pairs
Dataset License(s): [https://github.com/synthetichealth/synthea/blob/master/LICENSE]
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