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* 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

I found there are two grammar errors or typo issues in the explanation of the encoding properties.

The original sentences:
If your was made of multiple \"parts\" such as (question, context), then this would be a vector with for each token the segment it belongs to
If your has been truncated into multiple subparts because of a length limit (for BERT for example the sequence length is limited to 512), this will contain all the remaining overflowing parts.

I think "input" should be inserted after the phrase "If your".

For the tutorial of "How to generate text", the URL link was wrong (it was linked to the tutorial of "How to train a language model").

I fixed the URL.

Remove hardcoded mask_token and use the value provided by the tokenizer.

Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>