diff --git a/docs/source/en/model_doc/t5.mdx b/docs/source/en/model_doc/t5.mdx
index 5a1928923476cb..92cd753b645767 100644
--- a/docs/source/en/model_doc/t5.mdx
+++ b/docs/source/en/model_doc/t5.mdx
@@ -187,12 +187,15 @@ ignored. The code example below illustrates all of this.
 
 >>> # encode the targets
 >>> target_encoding = tokenizer(
-...     [output_sequence_1, output_sequence_2], padding="longest", max_length=max_target_length, truncation=True
+...     [output_sequence_1, output_sequence_2],
+...     padding="longest",
+...     max_length=max_target_length,
+...     truncation=True,
+...     return_tensors="pt",
 ... )
 >>> labels = target_encoding.input_ids
 
 >>> # replace padding token id's of the labels by -100 so it's ignored by the loss
->>> labels = torch.tensor(labels)
 >>> labels[labels == tokenizer.pad_token_id] = -100
 
 >>> # forward pass
diff --git a/src/transformers/models/t5/modeling_flax_t5.py b/src/transformers/models/t5/modeling_flax_t5.py
index 918a605fc4813a..2732bf591690f7 100644
--- a/src/transformers/models/t5/modeling_flax_t5.py
+++ b/src/transformers/models/t5/modeling_flax_t5.py
@@ -1388,6 +1388,10 @@ class FlaxT5Model(FlaxT5PreTrainedModel):
     ... ).input_ids
     >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="np").input_ids
 
+    >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
+    >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
+    >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
     >>> # forward pass
     >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
     >>> last_hidden_states = outputs.last_hidden_state
diff --git a/src/transformers/models/t5/modeling_t5.py b/src/transformers/models/t5/modeling_t5.py
index e4c36109bd7709..0ec4c74d1d8da5 100644
--- a/src/transformers/models/t5/modeling_t5.py
+++ b/src/transformers/models/t5/modeling_t5.py
@@ -1376,6 +1376,10 @@ def forward(
         ... ).input_ids  # Batch size 1
         >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
 
+        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
+        >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
+        >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
         >>> # forward pass
         >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
         >>> last_hidden_states = outputs.last_hidden_state
diff --git a/src/transformers/models/t5/modeling_tf_t5.py b/src/transformers/models/t5/modeling_tf_t5.py
index 091cb9d63eb42d..dc909c8d8f3349 100644
--- a/src/transformers/models/t5/modeling_tf_t5.py
+++ b/src/transformers/models/t5/modeling_tf_t5.py
@@ -1180,6 +1180,10 @@ def call(
         ... ).input_ids  # Batch size 1
         >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids  # Batch size 1
 
+        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
+        >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
+        >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
         >>> # forward pass
         >>> outputs = model(input_ids, decoder_input_ids=decoder_input_ids)
         >>> last_hidden_states = outputs.last_hidden_state