## RAG

This is the RAG-Token Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.

The model is a uncased model, which means that capital letters are simply converted to lower-case letters.

The model consits of a question_encoder, retriever and a generator. The retriever extracts relevant passages from the wiki_dpr train datasets, which is linked above. The question_encoder and retriever are based on facebook/dpr-question_encoder-single-nq-base and facebook/bart-large, which were jointly finetuned on on the wiki_dpr QA dataset in an end-to-end fashion.

## Usage:

Note: In the usage example below only the dummy retriever of wiki_dpr is used because the complete lecagy index requires over 75 GB of RAM. The model can generate answers to any factoid question as follows:

from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration