Name
Title | ||
|---|---|---|
Inference Time of a CamemBERT Deep Learning Model for Sentiment Analysis of COVID Vaccines on Twitter. |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3233/SHTI220714 | International Conference on Informatics, Management and Technology in Healthcare (ICIMTH) |
Keywords | DocType | Volume |
Artificial Intelligence,COVID-19,MLOps,Social Media,Vaccines | Conference | 295 |
ISSN | Citations | PageRank |
1879-8365 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guillaume Guerdoux | 1 | 0 | 0.68 |
| Théophile Tiffet | 2 | 0 | 0.68 |
| Cedric Bousquet | 3 | 0 | 0.34 |