Update link to swift-coreml-transformers

cc @lysandrejik

README.md

@@ -105,7 +105,7 @@ python -m pytest -sv ./examples/
 
 You should check out our [`swift-coreml-transformers`](https://github.com/huggingface/swift-coreml-transformers) repo.
 
-It contains an example of a conversion script from a Pytorch trained Transformer model (here, `GPT-2`) to a CoreML model that runs on iOS devices.
+It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
 
 At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models to productizing them in CoreML, or prototype a model or an app in CoreML then research its hyperparameters or architecture from TensorFlow 2.0 and/or PyTorch. Super exciting!
 

docs/source/installation.rst → docs/source/installation.md

-Installation
-================================================
+# Installation
 
 Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
 
-With pip
-^^^^^^^^
+## With pip
 
 PyTorch Transformers can be installed using pip as follows:
 
-.. code-block:: bash
+``` bash
+pip install transformers
+```
 
-   pip install transformers
-
-From source
-^^^^^^^^^^^
+## From source
 
 To install from source, clone the repository and install with:
 
-.. code-block:: bash
-
-    git clone https://github.com/huggingface/transformers.git
-    cd transformers
-    pip install [--editable] .
-
+``` bash
+git clone https://github.com/huggingface/transformers.git
+cd transformers
+pip install [--editable] .
+```
 
-Tests
-^^^^^
+## Tests
 
-An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the `tests folder <https://github.com/huggingface/transformers/tree/master/transformers/tests>`_ and examples tests in the `examples folder <https://github.com/huggingface/transformers/tree/master/examples>`_.
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
 
 Tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
 
 Run all the tests from the root of the cloned repository with the commands:
 
-.. code-block:: bash
-
-    python -m pytest -sv ./transformers/tests/
-    python -m pytest -sv ./examples/
-
+``` bash
+python -m pytest -sv ./transformers/tests/
+python -m pytest -sv ./examples/
+```
 
-OpenAI GPT original tokenization workflow
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+## OpenAI GPT original tokenization workflow
 
-If you want to reproduce the original tokenization process of the ``OpenAI GPT`` paper, you will need to install ``ftfy`` (use version 4.4.3 if you are using Python 2) and ``SpaCy`` :
+If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` (use version 4.4.3 if you are using Python 2) and `SpaCy`:
 
-.. code-block:: bash
+``` bash
+pip install spacy ftfy==4.4.3
+python -m spacy download en
+```
 
-   pip install spacy ftfy==4.4.3
-   python -m spacy download en
+If you don't install `ftfy` and `SpaCy`, the `OpenAI GPT` tokenizer will default to tokenize using BERT's `BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
 
-If you don't install ``ftfy`` and ``SpaCy``\ , the ``OpenAI GPT`` tokenizer will default to tokenize using BERT's ``BasicTokenizer`` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
-
-
-Note on model downloads (Continuous Integration or large-scale deployments)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+## Note on model downloads (Continuous Integration or large-scale deployments)
 
 If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way faster, and cheaper. Feel free to contact us privately if you need any help.
 
+## Do you want to run a Transformer model on a mobile device?
 
-Do you want to run a Transformer model on a mobile device?
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-You should check out our `swift-coreml-transformers <https://github.com/huggingface/swift-coreml-transformers>`_ repo.
-
-It contains an example of a conversion script from a Pytorch trained Transformer model (here, ``GPT-2``) to a CoreML model that runs on iOS devices.
+You should check out our [swift-coreml-transformers](https://github.com/huggingface/swift-coreml-transformers) repo.
 
-It also contains an implementation of BERT for Question answering.
+It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
 
 At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch to productizing them in CoreML,
-or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!
\ No newline at end of file
+or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!