[Doctest] Setup, quicktour and task_summary (huggingface#13078)

* Fix doctests for quicktour

* Adapt causal LM exemple

* Remove space

* Fix until summarization

* End of task summary

* Style

* With last changes in quicktour

docs/source/quicktour.rst

@@ -65,7 +65,7 @@ make them readable. For instance:
 .. code-block::
 
     >>> classifier('We are very happy to show you the 🤗 Transformers library.')
-    [{'label': 'POSITIVE', 'score': 0.9997795224189758}]
+    [{'label': 'POSITIVE', 'score': 0.99978}]
 
 That's encouraging! You can use it on a list of sentences, which will be preprocessed then fed to the model as a
 `batch`, returning a list of dictionaries like this one:
@@ -195,7 +195,8 @@ sequence:
 .. code-block::
 
     >>> print(inputs)
-    {'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+    {'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102],
+     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
 
 You can pass a list of sentences directly to your tokenizer. If your goal is to send them through your model as a
 batch, you probably want to pad them all to the same length, truncate them to the maximum length the model can accept
@@ -260,12 +261,12 @@ objects are described in greater detail :doc:`here <main_classes/output>`. For n
     >>> ## 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)
+            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
     >>> ## TENSORFLOW CODE
     >>> print(tf_outputs)
     TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
-    array([[-4.0832963 ,  4.3364143 ],
-           [ 0.081807  , -0.04178282]], dtype=float32)>, hidden_states=None, attentions=None)
+    array([[-4.0833 ,  4.3364  ],
+           [ 0.0818, -0.0418]], dtype=float32)>, hidden_states=None, attentions=None)
 
 Notice how the output object has a ``logits`` attribute. You can use this to access the model's final activations.
 
@@ -283,7 +284,7 @@ Let's apply the SoftMax activation to get predictions.
     >>> pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
     >>> ## TENSORFLOW CODE
     >>> import tensorflow as tf
-    >>> tf.nn.softmax(tf_outputs.logits, axis=-1)
+    >>> tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
 
 We can see we get the numbers from before:
 
@@ -292,8 +293,8 @@ We can see we get the numbers from before:
     >>> ## TENSORFLOW CODE
     >>> print(tf_predictions)
     tf.Tensor(
-    [[2.2042994e-04 9.9977952e-01]
-     [5.3086340e-01 4.6913657e-01]], shape=(2, 2), dtype=float32)
+    [[2.2043e-04 9.9978e-01]
+     [5.3086e-01 4.6914e-01]], shape=(2, 2), dtype=float32)
     >>> ## PYTORCH CODE
     >>> print(pt_predictions)
     tensor([[2.2043e-04, 9.9978e-01],
@@ -309,14 +310,14 @@ attribute:
     >>> pt_outputs = pt_model(**pt_batch, labels = torch.tensor([1, 0]))
     >>> print(pt_outputs)
     SequenceClassifierOutput(loss=tensor(0.3167, grad_fn=<NllLossBackward>), logits=tensor([[-4.0833,  4.3364],
-    [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
+            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
     >>> ## TENSORFLOW CODE
     >>> import tensorflow as tf
     >>> tf_outputs = tf_model(tf_batch, labels = tf.constant([1, 0]))
     >>> print(tf_outputs)
-    TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2051287e-04, 6.3326043e-01], dtype=float32)>, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
-    array([[-4.0832963 ,  4.3364143 ],
-           [ 0.081807  , -0.04178282]], dtype=float32)>, hidden_states=None, attentions=None)
+    TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2051e-04, 6.3326e-01], dtype=float32)>, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
+    array([[-4.0833 ,  4.3364  ],
+           [ 0.0818, -0.0418]], dtype=float32)>, hidden_states=None, attentions=None)
 
 Models are standard `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ or `tf.keras.Model
 <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ so you can use them in your usual training loop. 🤗