indobert-base-uncased-kisi-mcq-relevance-scorer – AI Model by hawalurahman | AlphaNeural AI

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hawalurahman/
indobert-base-uncased-kisi-mcq-relevance-scorer

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text-classification

generated_from_trainer

indolem/indobert-base-uncased

text-embeddings-inference

endpoints_compatible

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indobert-base-uncased-kisi-mcq-relevance-scorer

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

Loss: 0.0062
Mse: 0.0062
Rmse: 0.0787
Mae: 0.0406

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: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Rmse	Mae
0.0368	1.0	665	0.0102	0.0102	0.1011	0.0598
0.0135	2.0	1330	0.0083	0.0083	0.0913	0.0487
0.0086	3.0	1995	0.0090	0.0090	0.0949	0.0510
0.006	4.0	2660	0.0067	0.0067	0.0819	0.0427
0.0044	5.0	3325	0.0062	0.0062	0.0787	0.0406

Framework versions

Transformers 4.56.2
Pytorch 2.8.0+cu129
Datasets 4.2.0
Tokenizers 0.22.1