metadata

license: mit
base_model: indobenchmark/indobert-base-p1
tags:
  - generated_from_keras_callback
model-index:
  - name: damand2061/innermore-x-indobert-base-p1
    results: []

damand2061/innermore-x-indobert-base-p1

This model is a fine-tuned version of indobenchmark/indobert-base-p1 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.0007
Validation Loss: 0.2387
Validation Precision: 0.7583
Validation Recall: 0.6987
Validation F1: 0.7273
Validation Accuracy: 0.9535
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
0.5438	0.2878	0.5065	0.5109	0.5087	0.9161	0
0.1798	0.1890	0.6416	0.6332	0.6374	0.9425	1
0.0764	0.2122	0.5833	0.5502	0.5663	0.9338	2
0.0491	0.1986	0.7729	0.6987	0.7339	0.9545	3
0.0333	0.2071	0.75	0.6812	0.7140	0.9545	4
0.0252	0.1806	0.7456	0.7424	0.7440	0.9530	5
0.0138	0.2283	0.7018	0.6987	0.7002	0.9497	6
0.0073	0.2202	0.7318	0.7031	0.7171	0.9530	7
0.0065	0.2174	0.7762	0.7118	0.7426	0.9540	8
0.0037	0.2373	0.7619	0.6987	0.7289	0.9516	9
0.0021	0.2343	0.7594	0.7031	0.7302	0.9535	10
0.0015	0.2478	0.7546	0.7118	0.7326	0.9530	11
0.0011	0.2405	0.7630	0.7031	0.7318	0.9540	12
0.0006	0.2388	0.7583	0.6987	0.7273	0.9535	13
0.0007	0.2387	0.7583	0.6987	0.7273	0.9535	14

Framework versions

Transformers 4.38.2
TensorFlow 2.15.0
Tokenizers 0.15.2