Fix all sphynx warnings (#5068)

docs/source/model_doc/auto.rst

@@ -30,35 +30,35 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
 
 
 ``AutoModelForPreTraining``
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForPreTraining
     :members:
 
 
 ``AutoModelWithLMHead``
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelWithLMHead
     :members:
 
 
 ``AutoModelForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForSequenceClassification
     :members:
 
 
 ``AutoModelForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForQuestionAnswering
     :members:
 
 
 ``AutoModelForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForTokenClassification
     :members:

docs/source/model_doc/encoderdecoder.rst

@@ -1,5 +1,5 @@
 Encoder Decoder Models
------------
+------------------------
 
 This class can wrap an encoder model, such as ``BertModel`` and a decoder modeling with a language modeling head, such as ``BertForMaskedLM`` into a encoder-decoder model.
 
@@ -10,7 +10,7 @@ An application of this architecture could be *summarization* using two pretraine
 
 
 ``EncoderDecoderConfig``
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.EncoderDecoderConfig
     :members:

docs/source/model_doc/reformer.rst

@@ -4,7 +4,7 @@ Reformer
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_
 
 Overview
-~~~~~
+~~~~~~~~~~
 The Reformer model was presented in `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451.pdf>`_ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
 Here the abstract: 
 
@@ -13,7 +13,7 @@ Here the abstract:
 The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`_ .
 
 Axial Positional Encodings
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Axial Positional Encodings were first implemented in Google's `trax library <https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`_ and developed by the authors of this model's paper. In models that are treating very long input sequences, the conventional position id encodings store an embedings vector of size :math:`d` being the ``config.hidden_size`` for every position :math:`i, \ldots, n_s`, with :math:`n_s` being ``config.max_embedding_size``. *E.g.*, having a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000` would result in a position encoding matrix:
 
 .. math::