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Models doc (#7345)

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* Clean up model documentation

* Formatting

* Preparation work

* Long lines

* Main work on rst files

* Cleanup all config files

* Syntax fix

* Clean all tokenizers

* Work on first models

* Models beginning

* FaluBERT

* All PyTorch models

* All models

* Long lines again

* Fixes

* More fixes

* Update docs/source/model_doc/bert.rst

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update docs/source/model_doc/electra.rst

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Last fixes

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Sylvain Gugger
2020-09-23 13:20:45 -04:00
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commit 3323146e90
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docs/source/benchmarks.rst
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Benchmarks
==========
=======================================================================================================================
Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found `here <https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb>`__.
How to benchmark 🤗 Transformer models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` allow to flexibly benchmark 🤗 Transformer models.
The benchmark classes allow us to measure the `peak memory usage` and `required time` for both
@@ -300,7 +300,7 @@ deciding for which configuration the model should be trained.
Benchmark best practices
~~~~~~~~~~~~~~~~~~~~~~~~
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This section lists a couple of best practices one should be aware of when benchmarking a model.
@@ -311,7 +311,7 @@ This section lists a couple of best practices one should be aware of when benchm
Sharing your benchmark
~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Previously all available core models (10 at the time) have been benchmarked for `inference time`, across many different settings: using PyTorch, with
and without TorchScript, using TensorFlow, with and without XLA. All of those tests were done across CPUs (except for