Document Trainer limitation on custom models (#10635)

docs/source/main_classes/trainer.rst

@@ -21,7 +21,7 @@ Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.T
 customization during training.
 
 The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
-<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
+<https://github.com/NVIDIA/apex>`__ and Native AMP for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
 
 Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop which supports
 the above features. To inject custom behavior you can subclass them and override the following methods:
@@ -39,6 +39,18 @@ the above features. To inject custom behavior you can subclass them and override
 - **evaluate** -- Runs an evaluation loop and returns metrics.
 - **predict** -- Returns predictions (with metrics if labels are available) on a test set.
 
+.. warning::
+
+    The :class:`~transformers.Trainer` class is optimized for 🤗 Transformers models and can have surprising behaviors
+    when you use it on other models. When using it on your own model, make sure:
+
+    - your model always return tuples or subclasses of :class:`~transformers.file_utils.ModelOutput`.
+    - your model can compute the loss if a :obj:`labels` argument is provided and that loss is returned as the first
+      element of the tuple (if your model returns tuples)
+    - your model can accept multiple label arguments (use the :obj:`label_names` in your
+      :class:`~transformers.TrainingArguments` to indicate their name to the :class:`~transformers.Trainer`) but none
+      of them should be named :obj:`"label"`.
+
 Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function for multi-label
 classification: