Update README.md

model_cards/facebook/rag-sequence-base/README.md

 ## RAG
 
-This is a "base" 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) 
+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, RagSequenceForGeneration
+from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
 
 tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base")
-retriever = RagRetriever.from_pretrained("facebook/rag-sequence-base", index_name="exact", use_dummy_dataset=True)
-model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-base", retriever=retriever)
+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") 
 
-input_ids = tokenizer("What is the largest country in the world?", return_tensors="pt").input_ids
+outputs = model(input_dict["input_ids"], labels=input_dict["labels"])
 
-generated = model.generate(input_ids=input_ids)
-generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)
+loss = outputs.loss
 
-# => should give ["Asia ended in 2010 when China overtook Japan to become the world's second largest economy."]
-# Interesting answer. Definitely on topic, but might factual probably not fully correct.
+# train on loss
 ```