Fixed Sphinx errors and warnings

docs/source/model_doc/overview.rst

@@ -14,6 +14,8 @@ Here is a detailed documentation of the classes in the package and how to use th
    * - `Serialization best-practices <#serialization-best-practices>`__
      - How to save and reload a fine-tuned model
    * - `Configurations <#configurations>`__
+     - API of the configuration classes for BERT, GPT, GPT-2 and Transformer-XL
+
 
 TODO Lysandre filled: Removed Models/Tokenizers/Optimizers as no single link can be made.
 

docs/source/notebooks.rst

@@ -11,6 +11,6 @@ We include `three Jupyter Notebooks <https://github.com/huggingface/pytorch-pret
   The second NoteBook (\ `Comparing-TF-and-PT-models-SQuAD.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb>`_\ ) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer of the ``BertForQuestionAnswering`` and computes the standard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
 
 *
-  The third NoteBook (\ `Comparing-TF-and-PT-models-MLM-NSP.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/tree/mnotebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`_\ ) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model.
+  The third NoteBook (\ `Comparing-TF-and-PT-models-MLM-NSP.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/tree/notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`_\ ) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model.
 
 Please follow the instructions given in the notebooks to run and modify them.