overall-rating-bert – AI Model by rmtariq | AlphaNeural AI

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rmtariq/
overall-rating-bert

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text-classification

generated_from_trainer

rmtariq/ft-Malay-bert

autotrain_compatible

endpoints_compatible

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overall-rating-bert

This model is a fine-tuned version of rmtariq/ft-Malay-bert on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.9928
Accuracy: 0.5614
F1 Macro: 0.1798

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: 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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
1.0097	1.0	3565	0.9937	0.5614	0.1798
0.9945	2.0	7130	0.9958	0.5614	0.1798
0.9939	3.0	10695	0.9938	0.5614	0.1798
1.002	4.0	14260	0.9944	0.5614	0.1798
0.9975	5.0	17825	0.9930	0.5614	0.1798
0.9971	6.0	21390	0.9938	0.5614	0.1798
0.9905	7.0	24955	0.9932	0.5614	0.1798
1.0051	8.0	28520	0.9928	0.5614	0.1798

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1