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The SI model was trained using the following configurations.
The final model was trained on a dataset of 150,000 images.
Because the model was trained to be generative, it was also able to output reviews with preset sentiments.
One more small wrinkle: Bots that the model was trained on weren't all forms of automation on Twitter's platform.
Facebook's model was trained on a massive corpus of videos to identify interesting subsets of a video frame.
The HPI™ model was trained on ~3000 ICU and OR patients.
The model was trained and cross-validated with data from a 3,000 patient clinical database.
Moreover, the validation data has not been measured before the model was trained.
A single model was trained based on activity data from ChEMBL exclusively.
Secondly, the Family QSAM [41] model was trained on target descriptors only.
An ANN model was trained with these features for the quality prediction.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com