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/quicktour.mdx

@@ -532,12 +532,12 @@ All models are a standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs
    ... )  # doctest: +SKIP
    ```
 
-5. When you're ready, you can call `compile` and `fit` to start training:
+5. When you're ready, you can call `compile` and `fit` to start training. 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 tensorflow.keras.optimizers import Adam
 
-   >>> model.compile(optimizer=Adam(3e-5))
+   >>> model.compile(optimizer=Adam(3e-5))  # No loss argument!
    >>> model.fit(tf_dataset)  # doctest: +SKIP
    ```
 

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).

docs/source/en/training.mdx

@@ -191,7 +191,7 @@ tokenized_data = dict(tokenized_data)
 labels = np.array(dataset["label"])  # Label is already an array of 0 and 1
 ```
 
-Finally, load, [`compile`](https://keras.io/api/models/model_training_apis/#compile-method), and [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) the model:
+Finally, load, [`compile`](https://keras.io/api/models/model_training_apis/#compile-method), and [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) the model. 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 TFAutoModelForSequenceClassification
@@ -200,7 +200,7 @@ from tensorflow.keras.optimizers import Adam
 # Load and compile our model
 model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
 # Lower learning rates are often better for fine-tuning transformers
-model.compile(optimizer=Adam(3e-5))
+model.compile(optimizer=Adam(3e-5))  # No loss argument!
 
 model.fit(tokenized_data, labels)
 ```
@@ -261,7 +261,7 @@ list of samples into a batch and apply any preprocessing you want. See our
 Once you've created a `tf.data.Dataset`, you can compile and fit the model as before:
 
 ```py
-model.compile(optimizer=Adam(3e-5))
+model.compile(optimizer=Adam(3e-5))  # No loss argument!
 
 model.fit(tf_dataset)
 ```

examples/tensorflow/contrastive-image-text/run_clip.py

@@ -561,6 +561,8 @@ def main():
             weight_decay_rate=training_args.weight_decay,
             adam_global_clipnorm=training_args.max_grad_norm,
         )
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
         model.compile(optimizer=optimizer, jit_compile=training_args.xla)
 
         if not training_args.do_eval:

examples/tensorflow/image-classification/run_image_classification.py

@@ -497,6 +497,8 @@ def main():
                 collate_fn=collate_fn,
             ).with_options(dataset_options)
 
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
         model.compile(optimizer=optimizer, jit_compile=training_args.xla, metrics=["accuracy"])
 
         push_to_hub_model_id = training_args.push_to_hub_model_id

examples/tensorflow/language-modeling-tpu/run_mlm.py

@@ -235,8 +235,10 @@ def main(args):
             num_warmup_steps=total_train_steps // 20,
             init_lr=args.learning_rate,
             weight_decay_rate=args.weight_decay_rate,
-            # TODO Add the other Adam parameters?
         )
+
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
         model.compile(optimizer=optimizer, metrics=["accuracy"])
 
     def decode_fn(example):

examples/tensorflow/language-modeling/run_clm.py

@@ -537,7 +537,8 @@ def main():
             adam_global_clipnorm=training_args.max_grad_norm,
         )
 
-        # no user-specified loss = will use the model internal loss
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
         model.compile(optimizer=optimizer, jit_compile=training_args.xla)
         # endregion
 

examples/tensorflow/language-modeling/run_mlm.py

@@ -559,8 +559,9 @@ def main():
             adam_global_clipnorm=training_args.max_grad_norm,
         )
 
-        # no user-specified loss = will use the model internal loss
-        model.compile(optimizer=optimizer, jit_compile=training_args.xla, run_eagerly=True)
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
+        model.compile(optimizer=optimizer, jit_compile=training_args.xla)
         # endregion
 
         # region Preparing push_to_hub and model card

examples/tensorflow/multiple-choice/run_swag.py

@@ -455,6 +455,8 @@ def main():
             )
         else:
             optimizer = None
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
         model.compile(optimizer=optimizer, metrics=["accuracy"], jit_compile=training_args.xla)
         # endregion
 

examples/tensorflow/question-answering/run_qa.py

@@ -656,7 +656,8 @@ def main():
                 adam_global_clipnorm=training_args.max_grad_norm,
             )
 
-            # no user-specified loss = will use the model internal loss
+            # Transformers models compute the right loss for their task by default when labels are passed, and will
+            # use this for training unless you specify your own loss function in compile().
             model.compile(optimizer=optimizer, jit_compile=training_args.xla, metrics=["accuracy"])
 
         else:

examples/tensorflow/summarization/run_summarization.py

@@ -674,6 +674,8 @@ def main():
         # endregion
 
         # region Training
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
         model.compile(optimizer=optimizer, jit_compile=training_args.xla)
         eval_metrics = None
         if training_args.do_train:

examples/tensorflow/text-classification/run_glue.py

@@ -453,6 +453,8 @@ def main():
             metrics = []
         else:
             metrics = ["accuracy"]
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
         model.compile(optimizer=optimizer, metrics=metrics, jit_compile=training_args.xla)
         # endregion
 

examples/tensorflow/text-classification/run_text_classification.py

@@ -487,6 +487,8 @@ def main():
             metrics = []
         else:
             metrics = ["accuracy"]
+        # Transformers models compute the right loss for their task by default when labels are passed, and will
+        # use this for training unless you specify your own loss function in compile().
         model.compile(optimizer=optimizer, metrics=metrics)
         # endregion