14 февр. 2023 г. · The BERT-base model requires more than 400MB of memory and takes a few hundred milliseconds for inference on CPU instances. This makes it ...

18 апр. 2021 г. · The 16vCPU machine output consistent processing times between 40.80s to 43.34 for each file of 1500 sample. Overall vCPU usage is around 60%.

13 сент. 2021 г. · Using the GPU will result in faster results most likely if you can use it. If you use a GPU, try to use a DataLoader and make the Dataset run ...

Оценка 9,8/10 (13) 30 июл. 2022 г. · Inference time ranges from around 50 ms per sample on average to 0.6 ms on our dataset, depending on the hardware setup. On CPU the ONNX format ...

11 сент. 2023 г. · Our objective here is to conduct a comparative analysis of the inference speed across different BERT variants and identify the optimal model ...

20 июл. 2021 г. · Today, NVIDIA is releasing version 8 of TensorRT, which brings the inference latency of BERT-Large down to 1.2 ms on NVIDIA A100 GPUs with new ...

15 сент. 2021 г. · The pipeline makes it simple to perform inference on batches. On one pass, you can get the inference done instead of looping on a sequence of single texts.

19 янв. 2024 г. · In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model ...

20 апр. 2021 г. · This blog post is the first part of a series which will cover most of the hardware and software optimizations to better leverage CPUs for BERT model inference.

Our model achieves promising results in twelve English and Chinese datasets. It is able to speed up by a wide range from 1 to 12 times than BERT if given ...