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facebook/rag-sequence-nq facebook/rag-sequence-nq
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pytorch

tf

Contributed by

Facebook AI company
2 team members · 23 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, RagSequenceForGeneration tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq") model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq")

RAG

This is the RAG-Sequence 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, RagSequenceForGeneration 

tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq") 
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True) 
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever) 

input_dict = tokenizer.prepare_seq2seq_batch("how many countries are in europe", return_tensors="pt") 

generated = model.generate(input_ids=input_dict["input_ids"]) 
print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0]) 

# should give 54 => google says either 44 or 51