This project builds a lightweight Retrieval-Augmented Generation (RAG) system for question answering. Using a sampled dataset of Natural Questions dataset , making it computationally efficient for testing and development. and the facebook/rag-token-base model, it efficiently retrieves and generates answers.
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This project builds a lightweight Retrieval-Augmented Generation (RAG) system for question answering. Using a sampled dataset of Natural Questions dataset , making it computationally efficient for testing and development. and the facebook/rag-token-base model, it efficiently retrieves and generates answers.
Rockfrs/Efficient-Retrieval-Augmented-Generation-RAG-on-for-Question-Answering
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This project builds a lightweight Retrieval-Augmented Generation (RAG) system for question answering. Using a sampled dataset of Natural Questions dataset , making it computationally efficient for testing and development. and the facebook/rag-token-base model, it efficiently retrieves and generates answers.
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