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: 0.1107
Accuracy: 0.9783
F1: 0.9786

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: 128
eval_batch_size: 128
seed: 42
optimizer: Use 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
0.6925	0.1818	2	0.6898	0.5	0.6667
0.6876	0.3636	4	0.6744	0.5062	0.6694
0.6727	0.5455	6	0.6302	0.9379	0.9398
0.6264	0.7273	8	0.5569	0.9441	0.9467
0.5447	0.9091	10	0.4531	0.9472	0.9474
0.4741	1.0909	12	0.4022	0.9224	0.9280
0.3899	1.2727	14	0.3213	0.9627	0.9627
0.3416	1.4545	16	0.2857	0.9596	0.9579
0.3241	1.6364	18	0.2310	0.9752	0.9756
0.2627	1.8182	20	0.2517	0.9472	0.9496
0.3434	2.0	22	0.2472	0.9410	0.9440
0.2429	2.1818	24	0.2022	0.9627	0.9639
0.2228	2.3636	26	0.1595	0.9783	0.9782
0.1862	2.5455	28	0.1589	0.9720	0.9718
0.1903	2.7273	30	0.1396	0.9814	0.9816
0.2002	2.9091	32	0.1461	0.9752	0.9756
0.1296	3.0909	34	0.1522	0.9720	0.9726
0.1641	3.2727	36	0.1442	0.9720	0.9726
0.1211	3.4545	38	0.1315	0.9783	0.9785
0.159	3.6364	40	0.1290	0.9752	0.9753
0.1092	3.8182	42	0.1349	0.9752	0.9752
0.099	4.0	44	0.1645	0.9596	0.9590
0.2324	4.1818	46	0.1645	0.9596	0.9590
0.1707	4.3636	48	0.1625	0.9596	0.9590
0.1572	4.5455	50	0.1318	0.9720	0.9720
0.1652	4.7273	52	0.1165	0.9752	0.9753
0.0844	4.9091	54	0.1172	0.9783	0.9785
0.0724	5.0909	56	0.1134	0.9783	0.9785
0.1741	5.2727	58	0.1102	0.9814	0.9816
0.1313	5.4545	60	0.1118	0.9783	0.9786
0.1421	5.6364	62	0.1128	0.9783	0.9786
0.1236	5.8182	64	0.1119	0.9783	0.9786
0.085	6.0	66	0.1107	0.9783	0.9786

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

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

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

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