correct RAG model cards (#7420)

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

@@ -11,17 +11,16 @@ by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
 
 The model is a *uncased* model, which means that capital letters are simply converted to lower-case letters.
 
-The model consits of a *question_encoder*, *retriever* and a *generator*. The retriever is extracts relevant passages from the *wiki_dpr* `train` datasets, which is linked above.
+The model consits of a *question_encoder*, *retriever* and a *generator*. The retriever extracts relevant passages from the *wiki_dpr* `train` datasets, which is linked above.
 The question_encoder and retriever are based on `facebook/dpr-question_encoder-single-nq-base` and `facebook/bart-large`, which were jointly finetuned on 
 on the *wiki_dpr* QA dataset in an end-to-end fashion.
 
 ## Usage:
 
-**Note**: In the usage example below only the *dummy* retriever of *wiki_dpr* is used because the real retriever requires to over 40 GB of RAM.
-The model can generate questions to any question as follows:
+**Note**: In the usage example below only the *dummy* retriever of *wiki_dpr* is used because the complete *lecagy* index requires over 75 GB of RAM.
+The model can generate answers to any factoid question as follows:
 
 ```python
-
 from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration 
  
 tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq") 

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

@@ -29,6 +29,8 @@ Note that the model is *uncased* so that all capital input letters are converted
 
 ## 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

model_cards/facebook/rag-token-nq/README.md

@@ -11,14 +11,14 @@ by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
 
 The model is a *uncased* model, which means that capital letters are simply converted to lower-case letters.
 
-The model consits of a *question_encoder*, *retriever* and a *generator*. The retriever is extracts relevant passages from the *wiki_dpr* `train` datasets, which is linked above.
+The model consits of a *question_encoder*, *retriever* and a *generator*. The retriever extracts relevant passages from the *wiki_dpr* `train` datasets, which is linked above.
 The question_encoder and retriever are based on `facebook/dpr-question_encoder-single-nq-base` and `facebook/bart-large`, which were jointly finetuned on 
 on the *wiki_dpr* QA dataset in an end-to-end fashion.
 
 ## Usage:
 
-**Note**: In the usage example below only the *dummy* retriever of *wiki_dpr* is used because the real retriever requires to over 40 GB of RAM.
-The model can generate questions to any question as follows:
+**Note**: In the usage example below only the *dummy* retriever of *wiki_dpr* is used because the complete *lecagy* index requires over 75 GB of RAM.
+The model can generate answers to any factoid question as follows:
 
 ```python
 from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration