LabiraPJOK_1_100_Full – AI Model by Labira | AlphaNeural AI

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Labira/
LabiraPJOK_1_100_Full

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generated_from_keras_callback

indolem/indobert-base-uncased

endpoints_compatible

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Labira/LabiraPJOK_1_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: 0.0992
Validation Loss: 0.1051
Epoch: 48

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': 400, '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
5.7712	5.1521	0
4.8965	4.2303	1
4.1582	3.5226	2
3.4388	2.9489	3
2.8492	2.4067	4
2.5532	2.0347	5
2.1511	1.5826	6
1.7456	1.1703	7
1.3312	1.1496	8
1.1274	0.7130	9
0.9445	0.6299	10
0.8513	0.4422	11
0.8144	0.6906	12
0.6876	0.3412	13
0.6116	0.2922	14
0.5507	0.3807	15
0.5042	0.2770	16
0.2865	0.1692	17
0.4181	0.1840	18
0.3010	0.2490	19
0.3049	0.1310	20
0.4021	0.1106	21
0.4394	0.5673	22
0.3956	0.4617	23
0.3175	0.1589	24
0.2753	0.1354	25
0.1207	0.1039	26
0.1652	0.0881	27
0.2387	0.1040	28
0.1674	0.1306	29
0.1609	0.1319	30
0.1121	0.1215	31
0.1457	0.1124	32
0.1767	0.1144	33
0.1225	0.1322	34
0.1313	0.1385	35
0.1473	0.1586	36
0.2459	0.1693	37
0.1657	0.1705	38
0.1716	0.1314	39
0.1124	0.1108	40
0.1493	0.1058	41
0.1312	0.1161	42
0.1232	0.1264	43
0.1169	0.1181	44
0.1268	0.1092	45
0.0955	0.1074	46
0.1465	0.1051	47
0.0992	0.1051	48

Framework versions

Transformers 4.46.2
TensorFlow 2.17.0
Datasets 3.1.0
Tokenizers 0.20.3