Comments (3)
Regarding this as a follow up I am presenting the code I use:
training_execution_params = {
"table_training":{
"n_epochs":2,
"n_bootstrap_rounds":100,
"batch_size":8,
"n_gradient_accumulation_steps":2
}
}
table_training_params = training_execution_params['table_training']
parent_model = REaLTabFormer(model_type="tabular",
batch_size=table_training_params['batch_size'],
epochs=table_training_params['n_epochs'],
gradient_accumulation_steps=table_training_params['n_gradient_accumulation_steps'],
logging_steps=1000,
save_strategy="epoch",
checkpoints_dir = MODEL_RUN_DIRECTORY_PATH / f'{table_name}_checkpoints')
trainer = parent_model.fit(df=entity_tables_dataframes[table_name],
num_bootstrap=table_training_params['n_bootstrap_rounds'],
device=get_device())
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Hello @efstathios-chatzikyriakidis , I assume this is related to #60. And I think you've got a solution to this already with the hold-out validation dataset. :)
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Yes, thank you!
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