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[doc] normalize HF Transformers string (#15023)
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docs/source/benchmarks.mdx

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[[open-in-colab]]
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Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
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Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks.
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A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found [here](https://github.com/huggingface/notebooks/tree/master/examples/benchmark.ipynb).
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A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found [here](https://github.com/huggingface/notebooks/tree/master/examples/benchmark.ipynb).
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## How to benchmark 🤗 Transformer models
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## How to benchmark 🤗 Transformers models
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The classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] allow to flexibly benchmark 🤗 Transformer models. The benchmark classes allow us to measure the _peak memory usage_ and _required time_ for both _inference_ and _training_.
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The classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] allow to flexibly benchmark 🤗 Transformers models. The benchmark classes allow us to measure the _peak memory usage_ and _required time_ for both _inference_ and _training_.
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<Tip>
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docs/source/testing.mdx

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# Testing
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Let's take a look at how 🤗 Transformer models are tested and how you can write new tests and improve the existing ones.
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Let's take a look at how 🤗 Transformers models are tested and how you can write new tests and improve the existing ones.
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There are 2 test suites in the repository:
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