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* Add link to new comunity notebook (optimization)

related to https://github.com/huggingface/transformers/issues/4842#event-3469184635

This notebook is about benchmarking model training with/without dynamic padding optimization. 
https://github.com/ELS-RD/transformers-notebook 

Using dynamic padding on MNLI provides a **4.7 times training time reduction**, with max pad length set to 512. The effect is strong because few examples are >> 400 tokens in this dataset. IRL, it will depend of the dataset, but it always bring improvement and, after more than 20 experiments listed in this [article](https://towardsdatascience.com/divide-hugging-face-transformers-training-time-by-2-or-more-21bf7129db9q-21bf7129db9e?source=friends_link&sk=10a45a0ace94b3255643d81b6475f409

), it seems to not hurt performance.

Following advice from @patrickvonplaten I do the PR myself :-)

* Update notebooks/README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>