Retrieval Augmented Generation (RAG) and Pre-Trained Embedding Models
Retrieval Augmented Generation (RAG) involves providing additional knowledge to large language models (LLMs) from external data sources to enhance their performance. The architecture of RAG typically includes pre-trained embedding models that are trained on broad, general-purpose datasets but may struggle with domain-specific concepts and nuances. Fine-tuning these models on domain-specific data can significantly improve their accuracy for specialized tasks or domains, enhancing the retrieval of relevant context.
Using Amazon SageMaker for Fine-Tuning Sentence Transformer Embedding Models
Amazon SageMaker offers a seamless environment for simplifying the machine learning workflow, from data preparation to model deployment. By fine-tuning Sentence Transformer embedding models on SageMaker, developers can leverage the platform’s support for popular open-source frameworks and built-in algorithms for various use cases. The process involves preparing the data, training the model, and deploying it as a SageMaker endpoint, enabling more accurate and relevant responses tailored to specific domains or tasks.
Deploying and Evaluating Fine-Tuned Models in SageMaker
After fine-tuning the embedding model and creating an inference script, the model can be deployed as a SageMaker endpoint for inference. By comparing cosine similarity scores between sentences using the original pre-trained model and the fine-tuned model, the impact of fine-tuning on capturing semantic relationships can be evaluated. The fine-tuned model demonstrates improved performance in capturing domain-specific relationships, resulting in more accurate representations and better retrieval performance in RAG systems.
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