Update README.md

README.md

@@ -14,10 +14,10 @@ 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")
+model = RagSequenceForGeneration.from_pretrained_question_encoder_generator("repo_name")
 
-question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
-generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
+question_encoder_tokenizer = AutoTokenizer.from_pretrained("repo_name")
+generator_tokenizer = AutoTokenizer.from_pretrained("repo_name")
 
 tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
 model.config.use_dummy_dataset = True
@@ -40,9 +40,9 @@ 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)
+tokenizer = RagTokenizer.from_pretrained("repo_name")
+retriever = RagRetriever.from_pretrained("repo_name")
+model = RagTokenForGeneration.from_pretrained("repo_name", retriever=retriever)
 
 input_dict = tokenizer.prepare_seq2seq_batch("who holds the record in 100m freestyle", "michael phelps", return_tensors="pt")