fine-tuned-IndoNLI-Basic-with-indobert-large-p2-LR-1e-05 – AI Model by muhammadravi251001 | AlphaNeural AI

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fine-tuned-IndoNLI-Basic-with-indobert-large-p2-LR-1e-05

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fine-tuned-IndoNLI-Basic-with-indobert-large-p2-LR-1e-05

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: 0.6963
Accuracy: 0.7724
F1: 0.7724

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.3098	0.5	40	0.8899	0.6231	0.6268
1.0199	0.99	80	0.7268	0.6996	0.6999
0.767	1.49	120	0.6616	0.7406	0.7418
0.6649	1.98	160	0.6224	0.7547	0.7557
0.5796	2.48	200	0.6114	0.7656	0.7645
0.5796	2.97	240	0.6236	0.7524	0.7540
0.54	3.47	280	0.6223	0.7615	0.7624
0.4757	3.96	320	0.5965	0.7706	0.7721
0.4492	4.46	360	0.6216	0.7679	0.7681
0.3981	4.95	400	0.6347	0.7651	0.7669
0.3981	5.45	440	0.6373	0.7715	0.7727
0.352	5.94	480	0.6505	0.7674	0.7690
0.3294	6.44	520	0.6627	0.7720	0.7731
0.3058	6.93	560	0.6743	0.7660	0.7674
0.2692	7.43	600	0.6846	0.7665	0.7678
0.2692	7.93	640	0.6963	0.7724	0.7724

Framework versions

Transformers 4.29.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.2.0
Tokenizers 0.13.2