IndoBERT-SDGs-Oplib-Elsevier-Pruned

This model is a fine-tuned version of indobenchmark/indobert-base-p2 on the None dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

learning_rate: 1.4287607820196737e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.11880223991861419
num_epochs: 8/20
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Micro	F1 Macro	Precision Micro	Precision Macro	Recall Micro	Recall Macro	Roc Auc	Hamming Loss
No log	1.0	179	0.3583	0.0955	0.4542	0.1318	0.6984	0.2599	0.3365	0.1234	0.6531	0.1393
No log	2.0	358	0.2449	0.2843	0.7145	0.4748	0.8380	0.6607	0.6227	0.3992	0.7988	0.0857
0.3435	3.0	537	0.1952	0.3787	0.7708	0.6324	0.8991	0.8543	0.6745	0.5373	0.8294	0.0691
0.3435	4.0	716	0.1742	0.4059	0.7996	0.7018	0.8838	0.8411	0.7301	0.6168	0.8550	0.0630
0.3435	5.0	895	0.1621	0.4149	0.8209	0.7444	0.8561	0.8088	0.7885	0.6950	0.8805	0.0593
0.1526	6.0	1074	0.1525	0.4432	0.8292	0.7615	0.8628	0.8685	0.7982	0.7135	0.8859	0.0566
0.1526	7.0	1253	0.1521	0.4363	0.8294	0.7629	0.8633	0.8591	0.7980	0.7098	0.8858	0.0566
0.1526	8.0	1432	0.1493	0.44	0.8275	0.7542	0.8676	0.8694	0.7910	0.7044	0.8829	0.0568