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facebook/rag-token-base facebook/rag-token-base
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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, RagTokenForGeneration tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-base") model = RagTokenForGeneration.from_pretrained("facebook/rag-token-base")

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