rag sequence nq EU AI Act Compliance Profile

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

Your obligations as a rag sequence nq deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library