added save_directories for _psave_pretrained_pt and _tf, changed model to...

     added save_directories for _psave_pretrained_pt and _tf, changed model to tf_model and pt_model, enable the notebook to run cleanly from top to bottom without error (#14529)

* added save_directories for _psave_pretrained_pt and _tf, changed model to tf_model and pt_model, enable the notebook to run cleanly from top to bottom without error

* Update quicktour.rst

* added >>>

* dependencies

* added space

docs/source/quicktour.rst

@@ -51,6 +51,15 @@ The easiest way to use a pretrained model on a given task is to use :func:`~tran
 Let's see how this work for sentiment analysis (the other tasks are all covered in the :doc:`task summary
 </task_summary>`):
 
+Install the following dependencies (if not already installed):
+
+.. code-block::
+
+    >>> pip install torch
+    >>> pip install tensorflow
+    >>> pip install transformers
+    >>> pip install datasets
+
 .. code-block::
 
     >>> from transformers import pipeline
@@ -337,8 +346,15 @@ Once your model is fine-tuned, you can save it with its tokenizer in the followi
 
 .. code-block::
 
-    tokenizer.save_pretrained(save_directory)
-    model.save_pretrained(save_directory)
+    >>> pt_save_directory = './pt_save_pretrained'
+    >>> tokenizer.save_pretrained(pt_save_directory)
+    >>> pt_model.save_pretrained(pt_save_directory)
+
+.. code-block::
+
+    >>> tf_save_directory = './tf_save_pretrained'
+    >>> tokenizer.save_pretrained(tf_save_directory)
+    >>> tf_model.save_pretrained(tf_save_directory)
 
 You can then load this model back using the :func:`~transformers.AutoModel.from_pretrained` method by passing the
 directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between
@@ -347,17 +363,17 @@ loading a saved PyTorch model in a TensorFlow model, use :func:`~transformers.TF
 
 .. code-block::
 
-    from transformers import TFAutoModel
-    tokenizer = AutoTokenizer.from_pretrained(save_directory)
-    model = TFAutoModel.from_pretrained(save_directory, from_pt=True)
+    >>> from transformers import TFAutoModel
+    >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
+    >>> tf_model = TFAutoModel.from_pretrained(pt_save_directory, from_pt=True)
 
 and if you are loading a saved TensorFlow model in a PyTorch model, you should use the following code:
 
 .. code-block::
 
-    from transformers import AutoModel
-    tokenizer = AutoTokenizer.from_pretrained(save_directory)
-    model = AutoModel.from_pretrained(save_directory, from_tf=True)
+    >>> from transformers import AutoModel
+    >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
+    >>> pt_model = AutoModel.from_pretrained(tf_save_directory, from_tf=True)
 
 Lastly, you can also ask the model to return all hidden states and all attention weights if you need them: