UIT-NO-PREdeberta-v3-base-finetuned

sercetexam9/UIT-NO-PREdeberta-v3-base-finetuned

UIT-NO-PREdeberta-v3-base-finetuned is a text classification model from sercetexam9. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: deberta-v2
Access: Public
Created: Jan 23, 2025
Updated: Jan 23, 2025
SHA: 73c32f4b

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Base models

microsoft/deberta-v3-base

Model card

From the Hugging Face model README

UIT-NO-PREdeberta-v3-base-finetuned

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7415
F1: 0.7580
Roc Auc: 0.8223
Accuracy: 0.4964

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: 16
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: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.5738	1.0	139	0.5473	0.1947	0.5461	0.1751
0.4173	2.0	278	0.4194	0.5997	0.7189	0.3736
0.3402	3.0	417	0.3829	0.6887	0.7809	0.4314
0.1937	4.0	556	0.3563	0.7213	0.7914	0.4747
0.1756	5.0	695	0.3888	0.7271	0.7883	0.4856
0.1307	6.0	834	0.4043	0.7351	0.7959	0.4964
0.0963	7.0	973	0.4520	0.7434	0.8055	0.4982
0.0645	8.0	1112	0.4992	0.7259	0.7935	0.4892
0.0396	9.0	1251	0.5118	0.7570	0.8209	0.4964
0.0298	10.0	1390	0.5700	0.7516	0.8107	0.4892
0.0403	11.0	1529	0.6019	0.7440	0.8071	0.4892
0.014	12.0	1668	0.6476	0.7355	0.8008	0.4964
0.0095	13.0	1807	0.6871	0.7364	0.8007	0.4477
0.0127	14.0	1946	0.6843	0.7489	0.8097	0.4928
0.0098	15.0	2085	0.7105	0.7504	0.8082	0.4928
0.0039	16.0	2224	0.7187	0.7512	0.8119	0.4928
0.0044	17.0	2363	0.7415	0.7580	0.8223	0.4964
0.0035	18.0	2502	0.7614	0.7419	0.8051	0.4874
0.0033	19.0	2641	0.7527	0.7460	0.8137	0.4874
0.0027	20.0	2780	0.7640	0.7491	0.8113	0.5036
0.0014	21.0	2919	0.7682	0.7490	0.8113	0.4964
0.0014	22.0	3058	0.7804	0.7514	0.8124	0.5018
0.002	23.0	3197	0.7642	0.7567	0.8197	0.5072
0.0018	24.0	3336	0.7774	0.7537	0.8159	0.5018
0.0016	25.0	3475	0.7758	0.7545	0.8165	0.5054
0.0016	26.0	3614	0.7811	0.7580	0.8195	0.5108
0.0017	27.0	3753	0.7819	0.7558	0.8167	0.5108
0.0015	28.0	3892	0.7833	0.7574	0.8180	0.5126
0.0014	29.0	4031	0.7844	0.7565	0.8169	0.5126
0.001	30.0	4170	0.7846	0.7562	0.8165	0.5108

Framework versions

Transformers 4.48.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: deberta-v2
License: mit
Created: Jan 23, 2025
Updated: Jan 23, 2025