Labira/LabiraPJOK_456_100_Full

Labira/LabiraPJOK_123_100_Full

This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0108
• Validation Loss: 0.0014
• Epoch: 99

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Epoch
4.8014	3.8239	0
3.5330	3.0989	1
3.0273	2.6526	2
2.6530	2.0593	3
2.2572	1.6401	4
1.7060	1.0829	5
1.2904	0.6494	6
0.9646	0.4921	7
0.6371	0.2708	8
0.4612	0.2947	9
0.4154	0.2030	10
0.4027	0.1670	11
0.2759	0.1051	12
0.2515	0.1313	13
0.1759	0.0651	14
0.1293	0.0732	15
0.1595	0.0472	16
0.0989	0.0647	17
0.0797	0.0566	18
0.1292	0.0351	19
0.1098	0.0743	20
0.1490	0.0591	21
0.0934	0.0558	22
0.0720	0.0330	23
0.0502	0.0265	24
0.0598	0.0235	25
0.0589	0.0272	26
0.0409	0.0243	27
0.0445	0.0199	28
0.0425	0.0395	29
0.0420	0.0252	30
0.0332	0.0194	31
0.0286	0.0178	32
0.0480	0.0184	33
0.0361	0.0279	34
0.0529	0.0195	35
0.0296	0.0194	36
0.0346	0.0143	37
0.0256	0.0177	38
0.0331	0.0098	39
0.0386	0.0086	40
0.0303	0.0053	41
0.0310	0.0154	42
0.0193	0.0024	43
0.1070	0.0090	44
0.0937	0.0123	45
0.0766	0.0112	46
0.0698	0.0057	47
0.0297	0.0043	48
0.0385	0.0117	49
0.0802	0.0181	50
0.1040	0.0072	51
0.0836	0.0163	52
0.0861	0.0060	53
0.0867	0.0079	54
0.1242	0.0041	55
0.1090	0.0070	56
0.0394	0.0042	57
0.0312	0.0041	58
0.0391	0.0020	59
0.0320	0.0023	60
0.0479	0.0135	61
0.0403	0.0017	62
0.0352	0.0019	63
0.0314	0.0030	64
0.0254	0.0020	65
0.0243	0.0013	66
0.0504	0.0022	67
0.0474	0.0023	68
0.0430	0.0036	69
0.0142	0.0021	70
0.0169	0.0014	71
0.0110	0.0013	72
0.0229	0.0011	73
0.0476	0.0008	74
0.0461	0.0012	75
0.0170	0.0013	76
0.0210	0.0020	77
0.0146	0.0021	78
0.0206	0.0019	79
0.0137	0.0021	80
0.0125	0.0015	81
0.0303	0.0026	82
0.0100	0.0019	83
0.0088	0.0015	84
0.0128	0.0016	85
0.0153	0.0018	86
0.0141	0.0018	87
0.0163	0.0017	88
0.0104	0.0014	89
0.0098	0.0014	90
0.0116	0.0013	91
0.0160	0.0015	92
0.0161	0.0016	93
0.0088	0.0015	94
0.0101	0.0015	95
0.0105	0.0015	96
0.0110	0.0015	97
0.0049	0.0014	98
0.0108	0.0014	99

Framework versions

• Transformers 4.45.2
• TensorFlow 2.17.0
• Datasets 2.20.0
• Tokenizers 0.20.1