readme: ---
  license: apache-2.0
  thumbnail: https://huggingface.co/front/thumbnails/facebook.png
  ---
  ## RAG
  
  This is a non-finetuned version of the RAG-Sequence model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf) 
  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
  ```
  