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* added multilabel classification using distilbert notebook to community notebooks

* added multilabel classification using distilbert notebook to community notebooks

* Update ONNX notebook to include section on quantization.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Addressing ONNX team comments

Co-authored-by: eliska <3648991+elisans@users.noreply.github.com>

Signed-off-by: lordtt13 <thakurtanmay72@yahoo.com>

* Créé avec Colaboratory

* delete old file

* tf_train

* adapt timing for tpu

* fix timing

* fix timing

* fix timing

* fix timing

* update notebook

* add tests

* Add Reformer MLM notebook

* Update notebooks/README.md

* first doc version

* add benchmark docs

* fix typos

* improve README

* Update docs/source/benchmarks.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* fix naming and docs
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* remove references to old API in docstring - update data processors

* style

* fix tests - better type checking error messages

* better type checking

* include awesome fix by @LysandreJik for #5310

* updated doc and examples

* add notebook

* Créé avec Colaboratory

* move notebook to correct folder

* correct link

* correct filename

* correct filename

* better name

* 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>

* Add missing arg when creating model

* Fix typos

* Remove from_tf flag when creating model

* Added links to more community notebooks

Added links to 3 more community notebooks from the git repo: https://github.com/abhimishra91/transformers-tutorials


Different Transformers models are fine tuned on Dataset using PyTorch

* Update README.md

* Update README.md

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

* fix longformer model names in examples

* a better name for the notebook

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* adding BART summarization how-to community notebook

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

* add T5 fine-tuning notebook [Community notebooks]

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

* Adding optimizations block from ONNXRuntime.

* Turn off external data format by default for PyTorch export.

* Correct the way use_external_format is passed through the cmdline args.

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Added generic ONNX conversion script for PyTorch model.

* WIP initial TF support.

* TensorFlow/Keras ONNX export working.

* Print framework version info

* Add possibility to check the model is correctly loading on ONNX runtime.

* Remove quantization option.

* Specify ONNX opset version when exporting.

* Formatting.

* Remove unused imports.

* Make functions more generally reusable from other part of the code.

* isort happy.

* flake happy

* Export only feature-extraction for now

* Correctly check inputs order / filter before export.

* Removed task variable

* Fix invalid args call in load_graph_from_args.

* Fix invalid args call in convert.

* Fix invalid args call in infer_shapes.

* Raise exception and catch in caller function instead of exit.

* Add 04-onnx-export.ipynb notebook

* More WIP on the notebook

* Remove unused imports

* Simplify & remove unused constants.

* Export with constant_folding in PyTorch

* Let's try to put function args in the right order this time ...

* Disable external_data_format temporary

* ONNX notebook draft ready.

* Updated notebooks charts + wording

* Correct error while exporting last chart in notebook.

* Adressing @LysandreJik comment.

* Set ONNX opset to 11 as default value.

* Set opset param mandatory

* Added ONNX export unittests

* Quality.

* flake8 happy

* Add keras2onnx dependency on extras["tf"]

* Pin keras2onnx on github master to v1.6.5

* Second attempt.

* Third attempt.

* Use the right repo URL this time ...

* Do the same for onnxconverter-common

* Added keras2onnx and onnxconveter-common to 1.7.0 to supports TF2.2

* Correct commit hash.

* Addressing PR review: Optimization are enabled by default.

* Addressing PR review: small changes in the notebook

* setup.py comment about keras2onnx versioning.

* add first text for generation

* add generation pipeline to usage

* Created using Colaboratory

* correct docstring

* finish

Changing from "fine-grained token-leven" to "fine-grained token-level"

* Update notebooks

* From local to global link

* from local links to *actual* global links