added readme file for bart

ivy_models/bart/README.rst

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+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo.png?raw=true#gh-light-mode-only
+   :width: 100%
+   :class: only-light
+
+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo_dark.png?raw=true#gh-dark-mode-only
+   :width: 100%
+   :class: only-dark
+
+
+.. raw:: html
+
+    <br/>
+    <a href="https://pypi.org/project/ivy-models">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://badge.fury.io/py/ivy-models.svg">
+    </a>
+    <a href="https://github.com/unifyai/models/actions?query=workflow%3Adocs">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://github.com/unifyai/models/actions/workflows/docs.yml/badge.svg">
+    </a>
+    <a href="https://github.com/unifyai/models/actions?query=workflow%3Anightly-tests">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://github.com/unifyai/models/actions/workflows/nightly-tests.yml/badge.svg">
+    </a>
+    <a href="https://discord.gg/G4aR9Q7DTN">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://img.shields.io/discord/799879767196958751?color=blue&label=%20&logo=discord&logoColor=white">
+    </a>
+    <br clear="all" />
+
+BART
+===========
+
+`BART <https://arxiv.org/abs/1910.13461>`_  also known as Bidirectional Autoencoder Representations from Transformers is a denoising autoencoder for pretraining 
+sequence-to-sequence models. It is trained by corrupting text with an arbitrary noising function, and learning a model to reconstruct the original text. 
+BART uses a standard Transformer-based neural machine translation architecture, which consists of a bidirectional encoder and a left-to-right decoder.
+
+The encoder takes the corrupted text as input and produces a sequence of hidden states. The decoder then takes these hidden states as input and predicts the original text, 
+one token at a time. The model is trained to minimize the negative log likelihood of the original text.
+BART can be used for a variety of natural language processing tasks, including text generation, translation, and comprehension. 
+It has been shown to achieve state-of-the-art results on a number of these tasks
+
+
+Getting started
+-----------------
+
+.. code-block:: python
+
+    import ivy
+    ivy.set_backend("torch")
+    from ivy_models.bart import BartModel
+    from ivy_models.bart.config_bart import BartConfig
+
+    # Instantiate bart model
+    ivy_bart = BartModel(BartConfig)
+
+The pretrained bart model is now ready to be used, and is compatible with any other PyTorch code
+
+Citation
+--------
+
+::
+
+    @article{
+      title={BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension},
+      author={Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer},
+      journal={arXiv preprint arXiv:1910.13461},
+      year={2019}
+    }
+
+
+    @article{lenton2021ivy,
+      title={Ivy: Templated deep learning for inter-framework portability},
+      author={Lenton, Daniel and Pardo, Fabio and Falck, Fabian and James, Stephen and Clark, Ronald},
+      journal={arXiv preprint arXiv:2102.02886},
+      year={2021}
+    }