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model_cards/facebook/rag-sequence-nq/README.md
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## RAG
This is the RAG-Sequence Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Aleksandra Piktus et al.
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
## Usage:
@ -18,7 +18,7 @@ outputs = model(input_ids=input_dict["input_ids"], labels=input_dict["labels"])
# outputs.loss should give 76.2978
generated = model.generate(input_ids=input_dict["input_ids"], num_beams=4)
generated = model.generate(input_ids=input_dict["input_ids"])
generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)
# generated_string should give 270,000,000 -> not quite correct the answer, but it also only uses a dummy index