model_feature_concat_svd_64_DEFI

phunganhsang/model_feature_concat_svd_64_DEFI

model_feature_concat_svd_64_DEFI is a machine learning model from phunganhsang. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as agpl-3.0.

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

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At a glance

Library: transformers
License: agpl-3.0
Model type: roberta
Access: Public
Created: Jun 29, 2026
Updated: Jun 29, 2026
SHA: 7b30ec1c

Base models

vinai/phobert-base-v2

Model card

From the Hugging Face model README

model_feature_concat_svd_64_DEFI

This model is a fine-tuned version of vinai/phobert-base-v2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1051
Accuracy: 0.9706
F1: 0.9657

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: 64
eval_batch_size: 64
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
lr_scheduler_warmup_steps: 2645
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.1419	150	0.5298	0.8174	0.7377
No log	0.2838	300	0.2781	0.9414	0.9298
No log	0.4257	450	0.1972	0.9552	0.9474
No log	0.5676	600	0.1558	0.9607	0.9542
No log	0.7096	750	0.1317	0.9622	0.9564
No log	0.8515	900	0.1245	0.9640	0.9578
No log	0.9934	1050	0.1147	0.9672	0.9618
0.2694	1.1353	1200	0.1128	0.9661	0.9609
0.2694	1.2772	1350	0.1085	0.9639	0.9588
0.2694	1.4191	1500	0.0941	0.9674	0.9622
0.2694	1.5610	1650	0.0945	0.9687	0.9639
0.2694	1.7029	1800	0.0947	0.9684	0.9633
0.2694	1.8448	1950	0.0903	0.9697	0.9649
0.2694	1.9868	2100	0.0915	0.9681	0.9635
0.0953	2.1287	2250	0.1088	0.9657	0.9608
0.0953	2.2706	2400	0.0998	0.9669	0.9620
0.0953	2.4125	2550	0.0858	0.9696	0.9650
0.0953	2.5544	2700	0.0888	0.9707	0.9658
0.0953	2.6963	2850	0.1056	0.9641	0.9592
0.0953	2.8382	3000	0.0861	0.9691	0.9642
0.0953	2.9801	3150	0.1012	0.9663	0.9616
0.0752	3.1220	3300	0.0923	0.9716	0.9669
0.0752	3.2640	3450	0.0930	0.9695	0.9644
0.0752	3.4059	3600	0.1105	0.9673	0.9626
0.0752	3.5478	3750	0.0998	0.9701	0.9653
0.0752	3.6897	3900	0.1026	0.9694	0.9643
0.0752	3.8316	4050	0.1051	0.9706	0.9657

Framework versions

Transformers 5.12.1
Pytorch 2.7.1+cu118
Datasets 5.0.0
Tokenizers 0.22.2

Library: transformers
Type: roberta
License: agpl-3.0
Created: Jun 29, 2026
Updated: Jun 29, 2026