results_indobert-large-p2_preprocessing_tuning – AI Model by Alfanatasya | AlphaNeural AI

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Alfanatasya/
results_indobert-large-p2_preprocessing_tuning

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text-classification

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indobenchmark/indobert-large-p2

autotrain_compatible

endpoints_compatible

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results_indobert-large-p2_preprocessing_tuning

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

Loss: 0.6673
Accuracy: 0.7841
Precision: 0.7920
Recall: 0.7918
F1: 0.7901

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: 2.3352320097915953e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.2207	1.0	111	0.7383	0.7409	0.7463	0.7491	0.7435
0.6702	2.0	222	0.6673	0.7841	0.7920	0.7918	0.7901
0.4953	3.0	333	0.7161	0.7636	0.7707	0.7722	0.7711
0.3754	4.0	444	0.8318	0.75	0.7552	0.7657	0.7569
0.2769	5.0	555	0.8916	0.7591	0.7587	0.7732	0.7642
0.2039	6.0	666	0.9693	0.7432	0.7533	0.7589	0.7524
0.1525	7.0	777	1.0838	0.7477	0.7431	0.7610	0.7471

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2