Add an option to reduce compile() console spam (#23938)

* Add an option to reduce compile() console spam

* Add annotations to the example scripts

* Add notes to the quicktour docs as well

* minor fix

docs/source/en/tasks/language_modeling.mdx

@@ -306,12 +306,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 This can be done by specifying where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:

docs/source/en/tasks/masked_language_modeling.mdx

@@ -301,12 +301,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 This can be done by specifying where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:

docs/source/en/tasks/multiple_choice.mdx

@@ -335,10 +335,10 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tasks/semantic_segmentation.mdx

@@ -377,7 +377,7 @@ Start by defining the hyperparameters, optimizer and learning rate schedule:
 ```
 
 Then, load SegFormer with [`TFAutoModelForSemanticSegmentation`] along with the label mappings, and compile it with the
-optimizer:
+optimizer. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> from transformers import TFAutoModelForSemanticSegmentation
@@ -387,7 +387,7 @@ optimizer:
 ...     id2label=id2label,
 ...     label2id=label2id,
 ... )
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 Convert your datasets to the `tf.data.Dataset` format using the [`~datasets.Dataset.to_tf_dataset`] and the [`DefaultDataCollator`]:

docs/source/en/tasks/sequence_classification.mdx

@@ -259,12 +259,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tasks/summarization.mdx

@@ -267,12 +267,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the ROUGE score from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tasks/token_classification.mdx

@@ -361,12 +361,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the seqeval scores from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tasks/translation.mdx

@@ -276,12 +276,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the SacreBLEU metric from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).