```py
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
benchmark = PyTorchBenchmark(args)
===PT-TF-SPLIT===
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
args = TensorFlowBenchmarkArguments(
...     models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
... )
benchmark = TensorFlowBenchmark(args)
```
Here, three arguments are given to the benchmark argument data classes, namely models, batch_sizes, and
sequence_lengths. The argument models is required and expects a list of model identifiers from the
model hub The list arguments batch_sizes and sequence_lengths define
the size of the input_ids on which the model is benchmarked. There are many more parameters that can be configured
