metadata

license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_keras_callback
model-index:
  - name: damand2061/innermore-x-indobert-base-uncased
    results: []

damand2061/innermore-x-indobert-base-uncased

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: 0.0053
Validation Loss: 0.1740
Validation Precision: 0.7319
Validation Recall: 0.7644
Validation F1: 0.7478
Validation Accuracy: 0.9582
Epoch: 14

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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 420, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Validation Precision	Validation Recall	Validation F1	Validation Accuracy	Epoch
0.7318	0.4161	0.1453	0.1156	0.1287	0.8751	0
0.3556	0.2296	0.5610	0.5111	0.5349	0.9324	1
0.2050	0.1668	0.6972	0.6756	0.6862	0.9521	2
0.1289	0.1603	0.6807	0.72	0.6998	0.9531	3
0.0875	0.1874	0.7281	0.7022	0.7149	0.9521	4
0.0754	0.1931	0.6653	0.7156	0.6895	0.9479	5
0.0416	0.1637	0.6935	0.7644	0.7273	0.9554	6
0.0238	0.1413	0.7598	0.7733	0.7665	0.9638	7
0.0152	0.1494	0.7479	0.8044	0.7752	0.9634	8
0.0152	0.1946	0.7061	0.7156	0.7108	0.9531	9
0.0128	0.1815	0.7241	0.7467	0.7352	0.9554	10
0.0072	0.1766	0.7210	0.7467	0.7336	0.9568	11
0.0080	0.1860	0.6987	0.7422	0.7198	0.9531	12
0.0089	0.1826	0.7227	0.7644	0.7430	0.9563	13
0.0053	0.1740	0.7319	0.7644	0.7478	0.9582	14

Framework versions

Transformers 4.38.2
TensorFlow 2.15.0
Tokenizers 0.15.2