chunked/content_aware_chunking/_benchmarks/chunk_4.txt

In the following example, it is shown how a BERT model of type bert-base-cased can be benchmarked.

from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
args = PyTorchBenchmarkArguments(models=["google-bert/bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
benchmark = PyTorchBenchmark(args)
</pt>
<tf>py
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
args = TensorFlowBenchmarkArguments(
     models=["google-bert/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.