Add push_to_hub to no_trainer examples (#13659)

* Add push_to_hub to no_trainer examples

* Quality

* Document integration

* Roll out to other examples

examples/pytorch/README.md

@@ -74,6 +74,17 @@ line, 🤗 Trainer supports resuming from a checkpoint via `trainer.train(resume
 2. If `resume_from_checkpoint` is a path to a specific checkpoint it will use that saved checkpoint folder to resume the training from.
 
 
+### Upload the trained/fine-tuned model to the Hub
+
+All the example scripts support automatic upload of your final model to the [Model Hub](https://huggingface.co/models) by adding a `--push_to_hub` argument. It will then create a repository with your username slash the name of the folder you are using as `output_dir`. For instance, `"sgugger/test-mrpc"` if your username is `sgugger` and you are working in the folder `~/tmp/test-mrpc`.
+
+To specify a given repository name, use the `--hub_model_id` argument. You will need to specify the whole repository name (including your username), for instance `--hub_model_id sgugger/finetuned-bert-mrpc`. To upload to an organization you are a member of, just use the name of that organization instead of your username: `--hub_model_id huggingface/finetuned-bert-mrpc`.
+
+A few notes on this integration:
+
+- you will need to be logged in to the Hugging Face website locally for it to work, the easiest way to achieve this is to run `huggingface-cli login` and then type your username and password when prompted. You can also pass along your authentication token with the `--hub_token` argument.
+- the `output_dir` you pick will either need to be a new folder or a local clone of the distant repository you are using.
+
 ## Distributed training and mixed precision
 
 All the PyTorch scripts mentioned above work out of the box with distributed training and mixed precision, thanks to