Switch from return_tuple to return_dict (#6138)

* Switch from return_tuple to return_dict

* Fix test

* [WIP] Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleC… (#5614)

* Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleChoice} models and tests

* AutoModels


Tiny tweaks

* Style

* Final changes before merge

* Re-order for simpler review

* Final fixes

* Addressing @sgugger's comments

* Test MultipleChoice

* Rework TF trainer (#6038)

* Fully rework training/prediction loops

* fix method name

* Fix variable name

* Fix property name

* Fix scope

* Fix method name

* Fix tuple index

* Fix tuple index

* Fix indentation

* Fix variable name

* fix eval before log

* Add drop remainder for test dataset

* Fix step number + fix logging datetime

* fix eval loss value

* use global step instead of step + fix logging at step 0

* Fix logging datetime

* Fix global_step usage

* Fix breaking loop + logging datetime

* Fix step in prediction loop

* Fix step breaking

* Fix train/test loops

* Force TF at least 2.2 for the trainer

* Use assert_cardinality to facilitate the dataset size computation

* Log steps per epoch

* Make tfds compliant with TPU

* Make tfds compliant with TPU

* Use TF dataset enumerate instead of the Python one

* revert previous commit

* Fix data_dir

* Apply style

* rebase on master

* Address Sylvain's comments

* Address Sylvain's and Lysandre comments

* Trigger CI

* Remove unused import

* Switch from return_tuple to return_dict

* Fix test

* Add recent model

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Julien Plu <plu.julien@gmail.com>

docs/source/quicktour.rst

@@ -230,19 +230,16 @@ final activations of the model.
 
     >>> ## PYTORCH CODE
     >>> print(pt_outputs)
-    SequenceClassifierOutput(loss=None, logits=tensor([[-4.0833,  4.3364],
-            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
+    (tensor([[-4.0833,  4.3364],
+            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>),)
     >>> ## TENSORFLOW CODE
     >>> print(tf_outputs)
     (<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
     array([[-4.0832963 ,  4.336414  ],
            [ 0.08181786, -0.04179301]], dtype=float32)>,)
 
-The model can return more than just the final activations, which is why the PyTorch output is a special class and the
-TensorFlow output is a tuple. Here we only asked for the final activations, so we get a tuple with one element on the
-TensorFlow side and a :class:`~transformers.modeling_outputs.SequenceClassifierOutput` with just the ``logits`` field
-filled on the PyTorch side.
-
+The model can return more than just the final activations, which is why the output is a tuple. Here we only asked for
+the final activations, so we get a tuple with one element.
 .. note::
 
     All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final
@@ -254,7 +251,7 @@ Let's apply the SoftMax activation to get predictions.
 
     >>> ## PYTORCH CODE
     >>> import torch.nn.functional as F
-    >>> pt_predictions = F.softmax(pt_outputs.logits, dim=-1)
+    >>> pt_predictions = F.softmax(pt_outputs[0], dim=-1)
     >>> ## TENSORFLOW CODE
     >>> import tensorflow as tf
     >>> tf_predictions = tf.nn.softmax(tf_outputs[0], axis=-1)
@@ -341,8 +338,8 @@ code is easy to access and tweak if you need to.
 
 In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's
 using the :doc:`DistilBERT </model_doc/distilbert>` architecture. As
-:class:`~transformers.AutoModelForSequenceClassification` (or  :class:`~transformers.TFAutoModelForSequenceClassification`
-if you are using TensorFlow)` was used, the model automatically created is then a
+:class:`~transformers.AutoModelForSequenceClassification` (or :class:`~transformers.TFAutoModelForSequenceClassification`
+if you are using TensorFlow) was used, the model automatically created is then a
 :class:`~transformers.DistilBertForSequenceClassification`. You can look at its documentation for all details relevant
 to that specific model, or browse the source code. This is how you would directly instantiate model and tokenizer
 without the auto magic:

docs/source/training.rst

@@ -49,7 +49,7 @@ put it in train mode.
 .. code-block:: python
 
     from transformers import BertForSequenceClassification
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
+    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', return_dict=True)
     model.train()
 
 This is useful because it allows us to make use of the pre-trained BERT