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facebook/rag-sequence-base

sourceHugging Faceapache-2.0updated 6y agoView on Hugging Face

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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:

python

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:

python

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