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Inference Time of a CamemBERT Deep Learning Model for Sentiment Analysis of COVID Vaccines on Twitter.
Guerdoux, Guillaume; Tiffet, Théophile; Bousquet, Cedric.
Affiliation
  • Guerdoux G; Geegz, Paris, France.
  • Tiffet T; Unit of Public health, University hospital of Saint-Etienne, France.
  • Bousquet C; Unit of Public health, University hospital of Saint-Etienne, France.
Stud Health Technol Inform ; 295: 269-270, 2022 Jun 29.
Article in En | MEDLINE | ID: mdl-35773860
ABSTRACT
In previous work, we implemented a deep learning model with CamemBERT and PyTorch, and built a microservices architecture using the TorchServe serving library. Without TorchServe, inference time was three times faster when the model was loaded once in memory compared when the model was loaded each time. The preloaded model without TorchServe presented comparable inference time with the TorchServe instance. However, using a PyTorch preloaded model in a web application without TorchServe would necessitate to implement functionalities already present in TorchServe.
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Full text: 1 Database: MEDLINE Main subject: Vaccines / Social Media / Deep Learning / COVID-19 Limits: Humans Language: En Year: 2022 Type: Article

Full text: 1 Database: MEDLINE Main subject: Vaccines / Social Media / Deep Learning / COVID-19 Limits: Humans Language: En Year: 2022 Type: Article