diff --git a/docs/source/model_doc/t5.rst b/docs/source/model_doc/t5.rst
index 6ddfdbcc..38069801 100644
--- a/docs/source/model_doc/t5.rst
+++ b/docs/source/model_doc/t5.rst
@@ -20,13 +20,14 @@ Training
 ~~~~~~~~~~~~~~~~~~~~
 T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher forcing.
 This means that for training we always need an input sequence and a target sequence. 
-The input sequence is fed to the model using ``input_ids``. The target sequence is shifted to the right, *i.e.* perprended by a start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the ``lm_labels``. The PAD token is hereby used as the start-sequence token.
+The input sequence is fed to the model using ``input_ids``. The target sequence is shifted to the right, *i.e.* prepended by a start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the ``lm_labels``. The PAD token is hereby used as the start-sequence token.
 T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
 
 - Unsupervised denoising training
+
   In this setup spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) 
   and the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. 
-  Each sentinel tokens represents a unique mask token for this sentence and should start with ``<extra_id_1>``, ``<extrac_id_2>``, ... up to ``<extra_id_100>``. As a default 100 sentinel tokens are available in ``T5Tokenizer``.
+  Each sentinel token represents a unique mask token for this sentence and should start with ``<extra_id_1>``, ``<extra_id_2>``, ... up to ``<extra_id_100>``. As a default 100 sentinel tokens are available in ``T5Tokenizer``.
   *E.g.* the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be processed as follows: 
 
 ::
@@ -37,6 +38,7 @@ T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
   model(input_ids=input_ids, lm_labels=lm_labels)
 
 - Supervised training
+
   In this setup the input sequence and output sequence are standard sequence to sequence input output mapping.
   In translation, *e.g.* the input sequence "The house is wonderful." and output sequence "Das Haus ist wunderbar." should 
   be processed as follows: