Update README.md

README.md

@@ -124,7 +124,8 @@ model = AutoModelForSeq2SeqLM.from_pretrained('stanfordnlp/mrt5-small', trust_re
 input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3  # add 3 for special tokens
 labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3  # add 3 for special tokens
 
-loss = model(input_ids, labels=labels).loss # forward pass
+# Forward pass with hard deletion
+loss = model(input_ids, labels=labels, hard_delete=True).loss
 ```
 
 For batched inference and training, you can use ByT5's tokenizer class:
@@ -138,7 +139,8 @@ tokenizer = AutoTokenizer.from_pretrained('google/byt5-small')
 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
 
-loss = model(**model_inputs, labels=labels).loss # forward pass
+# Forward pass with hard deletion
+loss = model(**model_inputs, labels=labels, hard_delete=True).loss
 ```
 
 ## Training Details