cs_subcate – AI Model by kmcs-casulit | AlphaNeural AI

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kmcs-casulit/
cs_subcate

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tangminhanh/ts_subcate

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cs_subcate

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

Loss: 0.0517
Accuracy: 0.6283
F1: 0.6777
Precision: 0.7292
Recall: 0.6330

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	195	0.0649	0.2715	0.4110	0.8554	0.2704
No log	2.0	390	0.0532	0.5113	0.6149	0.7639	0.5145
0.0785	3.0	585	0.0515	0.5688	0.6404	0.7225	0.5750
0.0785	4.0	780	0.0496	0.5979	0.6606	0.7225	0.6085
0.0785	5.0	975	0.0492	0.6147	0.6753	0.7367	0.6233
0.0386	6.0	1170	0.0499	0.6141	0.6701	0.7151	0.6304
0.0386	7.0	1365	0.0503	0.6206	0.6754	0.7265	0.6310
0.0283	8.0	1560	0.0512	0.6199	0.6717	0.7129	0.6349
0.0283	9.0	1755	0.0515	0.6193	0.6720	0.7228	0.6278
0.0283	10.0	1950	0.0517	0.6283	0.6777	0.7292	0.6330

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1