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Fix t5 doc typos (#3978)

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* Fix tpo in into and add line under

* Add missing blank line under

* Correct types under
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Lorenzo Ampil
2020-04-28 00:27:15 +08:00
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parent 97a375484c
commit 12bb7fe770
1 changed files with 4 additions and 2 deletions
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docs/source/model_doc/t5.rst
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@@ -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: