{{modelData.modeling.metrics.evaluationMetric | mlMetricName : modelData.modeling.metrics }}
Model
Model ID
Model type
Target
Classes
{{ modelClass }}
Trained on
Columns (train set)
Rows (train set)
Train/validation split ratio Legacy - Test set used as validation set
Pretrained Model
Fine-tuned backbone layers {{modelData.actualParams.resolved.retrainedLayers}} / {{deephubNbLayersPerModel[modelData.modeling.pretrainedModel]}}
Optimizer {{ getParamDescription(modelData.modeling.type, "OPTIMIZERS", modelData.modeling.optimizer) }}
Learning rate scheduler {{ getParamDescription(modelData.modeling.type, "LR_SCHEDULERS", modelData.modeling.lrScheduler) }}
Initial learning rate
Final learning rate
Weight decay
Batch size
Epochs scheduled
Epochs trained
Epochs till Best Model {{modelData.actualParams.resolved.keptModelEpoch + 1}}
Early stopping {{ modelData.modeling.earlyStopping.enabled ? "Enabled" : "Disabled"}}
Early stopping min delta
Early stopping patience (in Epochs)
Code Env
Python version
Metadata
Optional. Informative labels for the model. The model:algorithm, model:date, model:name, trainDataset:dataset-name, testDataset:dataset-name labels, evaluation:date and evaluationDataset:dataset-name are automatically added.