Labira/LabiraEdu-v1.0x

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: 0.0206
• Validation Loss: 4.5266
• Epoch: 98

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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1100, '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
5.0565	3.9761	0
3.6621	3.2932	1
3.0961	3.2587	2
2.7357	3.2031	3
2.3059	3.2519	4
1.8933	3.4772	5
1.9076	3.1664	6
1.5492	3.4201	7
1.2578	3.5190	8
1.0478	3.4076	9
1.0130	3.5961	10
0.9073	3.4919	11
0.7071	3.5013	12
0.5616	4.0259	13
0.4798	3.9766	14
0.5938	3.8146	15
0.6476	3.7065	16
0.4264	4.1631	17
0.5290	3.7455	18
0.4637	3.6362	19
0.3826	3.8389	20
0.2876	3.7611	21
0.2221	4.0540	22
0.1752	4.0683	23
0.1544	4.0452	24
0.1600	4.0417	25
0.1390	4.0668	26
0.1134	4.0659	27
0.0965	4.0700	28
0.0820	4.2026	29
0.0810	4.3008	30
0.1166	4.0835	31
0.0776	4.0886	32
0.1033	4.1303	33
0.0512	4.1014	34
0.0484	4.1462	35
0.0565	4.2404	36
0.0652	4.2064	37
0.0538	4.1032	38
0.0516	4.0948	39
0.0611	4.2563	40
0.0523	4.3629	41
0.0571	4.3032	42
0.0479	4.3147	43
0.0308	4.3639	44
0.0370	4.3490	45
0.0406	4.3471	46
0.0300	4.4078	47
0.0270	4.4253	48
0.0283	4.4177	49
0.0228	4.4394	50
0.0538	4.4019	51
0.0342	4.3553	52
0.0249	4.3161	53
0.0657	4.4426	54
0.0309	4.5678	55
0.0467	4.4247	56
0.0356	4.5058	57
0.0431	4.4563	58
0.0366	4.5242	59
0.0624	4.3149	60
0.0471	4.3177	61
0.0248	4.3159	62
0.0388	4.3554	63
0.0262	4.3888	64
0.0360	4.4544	65
0.0319	4.4608	66
0.0269	4.4676	67
0.0373	4.3847	68
0.0205	4.3560	69
0.0223	4.3715	70
0.0306	4.3894	71
0.0235	4.4409	72
0.0189	4.4767	73
0.0280	4.5137	74
0.0165	4.5471	75
0.0098	4.5553	76
0.0173	4.5465	77
0.0234	4.5461	78
0.0231	4.5485	79
0.0237	4.5326	80
0.0158	4.5293	81
0.0178	4.5309	82
0.0225	4.5306	83
0.0191	4.5213	84
0.0213	4.5231	85
0.0144	4.5332	86
0.0191	4.5365	87
0.0188	4.5487	88
0.0272	4.5426	89
0.0126	4.5390	90
0.0224	4.5384	91
0.0218	4.5389	92
0.0083	4.5394	93
0.0246	4.5326	94
0.0199	4.5284	95
0.0174	4.5264	96
0.0130	4.5259	97
0.0206	4.5266	98

Framework versions

• Transformers 4.41.2
• TensorFlow 2.15.0
• Datasets 2.19.2
• Tokenizers 0.19.1