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tringuyen-uit/
VP_ViSoBERT_syl_ViWikiFC

Name: VP_ViSoBERT_syl_ViWikiFC
Rating: 2.5 (7 reviews)

generated_from_trainer

tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC

finetune

autotrain_compatible

endpoints_compatible

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VP_ViSoBERT_syl_ViWikiFC

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

Loss: 1.1555
Accuracy: 0.6445

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6994	0.05	100	0.9688	0.6158
0.6904	0.1	200	0.9753	0.6014
0.7969	0.14	300	0.9446	0.5871
0.6801	0.19	400	0.9912	0.6057
0.7089	0.24	500	0.9617	0.5861
0.6627	0.29	600	1.0585	0.5689
0.6792	0.33	700	1.0064	0.6230
0.6702	0.38	800	1.0593	0.5818
0.6252	0.43	900	0.9621	0.5967
0.6262	0.48	1000	1.0152	0.5957
0.6515	0.53	1100	0.9539	0.6225
0.6596	0.57	1200	0.9188	0.6067
0.6458	0.62	1300	0.9318	0.6201
0.6087	0.67	1400	0.9532	0.6172
0.6282	0.72	1500	1.0107	0.6244
0.6266	0.76	1600	1.0199	0.6096
0.6165	0.81	1700	1.0973	0.6096
0.5869	0.86	1800	0.9177	0.6325
0.596	0.91	1900	0.8821	0.6364
0.6073	0.96	2000	0.9350	0.6306
0.5921	1.0	2100	0.9606	0.6282
0.4551	1.05	2200	1.0386	0.6373
0.3922	1.1	2300	1.1936	0.6368
0.39	1.15	2400	1.1922	0.6316
0.442	1.19	2500	1.1599	0.6220
0.4092	1.24	2600	1.3106	0.6196
0.4582	1.29	2700	1.1817	0.6316
0.4356	1.34	2800	1.1257	0.6316
0.4145	1.39	2900	1.1899	0.6354
0.4379	1.43	3000	1.1385	0.6388
0.4222	1.48	3100	1.1844	0.6249
0.3758	1.53	3200	1.2444	0.6311
0.4114	1.58	3300	1.1908	0.6349
0.4449	1.62	3400	1.1483	0.6273
0.4046	1.67	3500	1.1977	0.6306
0.4274	1.72	3600	1.1520	0.6450
0.3785	1.77	3700	1.1665	0.6330
0.3854	1.82	3800	1.1680	0.6474
0.3562	1.86	3900	1.1616	0.6459
0.3938	1.91	4000	1.1823	0.6397
0.5083	1.96	4100	1.1555	0.6445

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2