Update README.md

README.md

@@ -2,9 +2,6 @@
 language: multilingual
 datasets:
 - mc4
-tags:
-- summarization
-- translation
 
 license: apache-2.0
 ---
@@ -40,10 +37,10 @@ loss = model(input_ids, labels=labels).loss # forward pass
 For batched inference & training it is however recommended using a tokenizer class for padding:
 
 ```python
-from transformers import T5ForConditionalGeneration, ByT5Tokenizer
+from transformers import T5ForConditionalGeneration, AutoTokenizer
 
 model = T5ForConditionalGeneration.from_pretrained('google/byt5-large')
-tokenizer = ByT5Tokenizer.from_pretrained('google/byt5-large')
+tokenizer = AutoTokenizer.from_pretrained('google/byt5-large')
 
 model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
 labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids