This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3236
Accuracy: 0.905
F1: 0.9046

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: 0.0001
train_batch_size: 256
eval_batch_size: 256
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: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6883	0.6667	4	0.6686	0.9503	0.9503
0.6493	1.3333	8	0.5799	0.9472	0.9471
0.5233	2.0	12	0.4287	0.9534	0.9534
0.3949	2.6667	16	0.3071	0.9752	0.9752
0.28	3.3333	20	0.2451	0.9783	0.9783
0.2601	4.0	24	0.2120	0.9814	0.9814
0.2205	4.6667	28	0.1972	0.9783	0.9783
0.1873	5.3333	32	0.1823	0.9814	0.9814
0.187	6.0	36	0.1799	0.9783	0.9783

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

livinNector/m-minilm-l12-h384-dra-tam-mal-ai-gen-review-classification-finetune-2

m-minilm-l12-h384-dra-tam-mal-ai-gen-review-classification-finetune-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

livinNector/
m-minilm-l12-h384-dra-tam-mal-ai-gen-review-classification-finetune-2