results_indobert-base-p2_with_preprocess_augmentasi – AI Model by Alfanatasya | AlphaNeural AI

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Alfanatasya/
results_indobert-base-p2_with_preprocess_augmentasi

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text-classification

generated_from_trainer

indobenchmark/indobert-base-p2

autotrain_compatible

endpoints_compatible

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results_indobert-base-p2_with_preprocess_augmentasi

This model is a fine-tuned version of indobenchmark/indobert-base-p2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8227
Accuracy: 0.7016
Precision: 0.7074
Recall: 0.7152
F1: 0.7085

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:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.4456	1.0	219	0.9600	0.6241	0.6109	0.6281	0.6153
0.9394	2.0	438	0.8233	0.6879	0.7187	0.6821	0.6887
0.7441	3.0	657	0.8227	0.7016	0.7074	0.7152	0.7085
0.5722	4.0	876	0.9086	0.6970	0.7004	0.7120	0.7035
0.4299	5.0	1095	1.0365	0.6902	0.7001	0.6986	0.6913

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 4.0.0
Tokenizers 0.21.0