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Hyperparameters

learning_rate=2e-5
per_device_train_batch_size=14
per_device_eval_batch_size=14
weight_decay=0.01
save_total_limit=3
num_train_epochs=3
predict_with_generate=True
fp16=True

Training Output

global_step=4248,
training_loss=2.930363613782405,
metrics={'train_runtime': 11857.8062,
'train_samples_per_second': 5.014,
'train_steps_per_second': 0.358,
'total_flos': 1.3114345819786445e+17,
'train_loss': 2.930363613782405,
'epoch': 3.0}

Training Results

Epoch Training Loss Validation Loss Rouge1 Rouge2 Rougel Rougelsum Bleu Gen Len
1 3.095400 2.864138 0.425500 0.139000 0.246300 0.246300 0.541400 141.540900
2 2.876500 2.811244 0.425600 0.139100 0.246500 0.246400 0.541600 141.619000
3 2.748300 2.797923 0.425800 0.138700 0.246400 0.246300 0.541800 141.597000
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F32
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Dataset used to train usakha/Prophetnet_MedPaper_model