RAG
This is a non-finetuned version of the RAG-Token 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-Token model and was created as follows:
from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration, AutoTokenizer
model = RagTokenForGeneration.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-token-base")
retriever = RagRetriever.from_pretrained("facebook/rag-token-base")
model = RagTokenForGeneration.from_pretrained("facebook/rag-token-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
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