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Models doc (#7345)

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* Clean up model documentation

* Formatting

* Preparation work

* Long lines

* Main work on rst files

* Cleanup all config files

* Syntax fix

* Clean all tokenizers

* Work on first models

* Models beginning

* FaluBERT

* All PyTorch models

* All models

* Long lines again

* Fixes

* More fixes

* Update docs/source/model_doc/bert.rst

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update docs/source/model_doc/electra.rst

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Last fixes

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Sylvain Gugger
2020-09-23 13:20:45 -04:00
committed by GitHub
parent 58405a527b
commit 3323146e90
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docs/source/model_doc/mbart.rst
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@@ -1,11 +1,11 @@
MBart
----------------------------------------------------
-----------------------------------------------------------------------------------------------------------------------
**DISCLAIMER:** If you see something strange,
file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
@sshleifer
Overview
~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The MBart model was presented in `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov
Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer. According to the abstract,
@@ -15,7 +15,7 @@ The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/ma
Training
~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task.
As the model is multilingual it expects the sequences in a different format. A special language id token
is added in both the source and target text. The source text format is ``X [eos, src_lang_code]``
@@ -25,7 +25,7 @@ the sequences for seq-2-seq fine-tuning.
- Supervised training
::
.. code-block::
example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
@@ -41,7 +41,7 @@ the sequences for seq-2-seq fine-tuning.
While generating the target text set the `decoder_start_token_id` to the target language id.
The following example shows how to translate English to Romanian using the ```facebook/mbart-large-en-ro``` model.
::
.. code-block::
from transformers import MBartForConditionalGeneration, MBartTokenizer
model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
@@ -54,21 +54,21 @@ the sequences for seq-2-seq fine-tuning.
MBartConfig
~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MBartConfig
:members:
MBartTokenizer
~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MBartTokenizer
:members: build_inputs_with_special_tokens, prepare_seq2seq_batch
MBartForConditionalGeneration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MBartForConditionalGeneration
:members: generate, forward