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+## RAG
+
+This is the RAG-Token Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf) 
+by Aleksandra Piktus et al.
+
+## Usage:
+
+```python
+
+from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
+
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
+retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
+model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
+
+input_dict = tokenizer.prepare_seq2seq_batch("How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="pt")
+outputs = model(input_ids=input_dict["input_ids"], labels=input_dict["labels"])
+
+# outputs.loss should give 76.1230
+
+generated = model.generate(input_ids=input_dict["input_ids"], num_beams=4)
+generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)
+
+# generated_string should give 270,000 -> not quite correct the answer, but it also only uses a dummy index
+```