ts_subcate – AI Model by kmcs-casulit | AlphaNeural AI

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kmcs-casulit/
ts_subcate

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tangminhanh/ts_cate

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ts_subcate

This model is a fine-tuned version of tangminhanh/ts_cate on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0084
Accuracy: 0.7013
F1: 0.7812
Precision: 0.8815
Recall: 0.7014

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: 32
eval_batch_size: 64
seed: 42
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	Precision	Recall
No log	1.0	403	0.0261	0.0	0.0	0.0	0.0
0.0805	2.0	806	0.0174	0.3328	0.4904	0.9438	0.3313
0.0203	3.0	1209	0.0131	0.4702	0.6217	0.9229	0.4687
0.0138	4.0	1612	0.0111	0.5592	0.6939	0.9170	0.5581
0.0104	5.0	2015	0.0100	0.6297	0.7417	0.8998	0.6308
0.0104	6.0	2418	0.0091	0.6659	0.7628	0.8909	0.6669
0.0084	7.0	2821	0.0088	0.6815	0.7712	0.8867	0.6823
0.0072	8.0	3224	0.0086	0.6889	0.7764	0.8874	0.6900
0.0063	9.0	3627	0.0084	0.6982	0.7803	0.8832	0.6989
0.006	10.0	4030	0.0084	0.7013	0.7812	0.8815	0.7014

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1