We created this space to create a collections of such datasets to boost the developement of RAG solutions and welcome any feedback about how your ideal RAG- ...

Retrieval-augmented generation (RAG) models combine the powers of pretrained dense retrieval (DPR) and sequence-to-sequence models. RagConfig · Rag specific outputs · RagRetriever

In this huggingface discussion you can share what you used the dataset for. Derives from https://www.kaggle.com/datasets/rtatman/questionanswer ...

This notebook demonstrates how you can evaluate your RAG (Retrieval Augmented Generation), by building a synthetic evaluation dataset and using LLM-as-a-judge.

Glaive-RAG-v1 is a dataset with ~50k samples built using the Glaive platform, for finetuning models for RAG use cases.

16 окт. 2023 г. · Retrieval Augmented Generation (RAG) is a pattern that works with pretrained Large Language Models (LLM) and your own data to generate responses.

# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately. # retriever = RagRetriever.

3 нояб. 2024 г. · In this article I will show you how to create your own RAG dataset consisting of contexts, questions, and answers from documents in any language. Read Files · Generating Question-Answer...

Retrieval-Augmented Generation (RAG) Dataset 12000 is an English dataset designed for RAG-optimized models, built by Neural Bridge AI, and released under ...

12 мар. 2024 г. · This is the RAG-Sequence Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara ...