RAG

This is a non-finetuned version of 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.

Rag consits of a question encoder , retriever and a generator . The retriever should be a RagRetriever instance. The question encoder can be any model that can be loaded with AutoModel and the generator can be any model that can be loaded with AutoModelForSeq2SeqLM .

This model is a non-finetuned RAG-Sequence model and was created as follows:

from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration, AutoTokenizer

model = RagSequenceForGeneration.from_pretrained_question_encoder_generator("facebook/dpr-question_encoder-single-nq-base", "facebook/bart-large")

question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")

tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
model.config.use_dummy_dataset = True
model.config.index_name = "exact"
retriever = RagRetriever(model.config, question_encoder_tokenizer, generator_tokenizer)

model.save_pretrained("./")
tokenizer.save_pretrained("./")
retriever.save_pretrained("./")

Note that the model is uncased so that all capital input letters are converted to lower-case.

Usage:

Note : the model uses the dummy retriever as a default. Better results are obtained by using the full retriever, by setting config.index_name="legacy" and config.use_dummy_dataset=False . The model can be fine-tuned as follows:

from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration

tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-base")
model = RagTokenForGeneration.from_pretrained("facebook/rag-sequence-base", retriever=retriever)

input_dict = tokenizer.prepare_seq2seq_batch("who holds the record in 100m freestyle", "michael phelps", return_tensors="pt") 

outputs = model(input_dict["input_ids"], labels=input_dict["labels"])

loss = outputs.loss

# train on loss