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[Doc model summary] add MBart model summary (#6649)

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@ -552,6 +552,31 @@ input becomes “My <x> very <y> .” and the target input becomes “<x> dog is
The library provides a version of this model for conditional generation.
MBart
----------------------------------------------
.. raw:: html
<a href="https://huggingface.co/models?filter=mbart">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-mbart-blueviolet">
</a>
<a href="model_doc/mbart.html">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mbart-blueviolet">
</a>
`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.
The model architecture and pre-training objective is same as BART, but MBart is trained on 25 languages
and is intended for supervised and unsupervised machine translation. MBart is one of the first methods
for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages,
The library provides a version of this model for conditional generation.
The `mbart-large-en-ro checkpoint <https://huggingface.co/facebook/mbart-large-en-ro>`_ can be used for english -> romanian translation.
The `mbart-large-cc25 <https://huggingface.co/facebook/mbart-large-cc25>`_ checkpoint can be finetuned for other translation and summarization tasks, using code in ```examples/seq2seq/``` , but is not very useful without finetuning.
.. _multimodal-models:
Multimodal models