vit5-base-sum-v2

This model is a fine-tuned version of VietAI/vit5-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.2966
• Rouge1: 0.2753
• Rouge2: 0.1857
• Rougel: 0.2404
• Rougelsum: 0.2405

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: 3e-05
• train_batch_size: 128
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.6768	1.0	383	1.4861	0.275	0.1809	0.2387	0.2387
1.5386	2.0	766	1.4077	0.2761	0.1836	0.2402	0.2402
1.4621	3.0	1149	1.3745	0.2756	0.1841	0.24	0.24
1.4093	4.0	1532	1.3557	0.2759	0.1847	0.2404	0.2404
1.3664	5.0	1915	1.3314	0.2752	0.1853	0.2404	0.2404
1.32	6.0	2298	1.3238	0.2741	0.1844	0.24	0.24
1.2963	7.0	2681	1.3134	0.2753	0.1861	0.2413	0.2413
1.2629	8.0	3064	1.3038	0.2753	0.1858	0.2409	0.2409
1.2327	9.0	3447	1.3003	0.274	0.1849	0.2398	0.2398
1.2225	10.0	3830	1.3029	0.2749	0.1858	0.2403	0.2403
1.2041	11.0	4213	1.2974	0.2752	0.1859	0.2407	0.2407
1.187	12.0	4596	1.2929	0.2758	0.1865	0.2411	0.2411
1.1743	13.0	4979	1.2949	0.2751	0.1861	0.2406	0.2406
1.15	14.0	5362	1.2941	0.2745	0.1853	0.24	0.2401
1.1382	15.0	5745	1.2966	0.2753	0.1857	0.2404	0.2405

Framework versions

• Transformers 4.38.2
• Pytorch 2.8.0+cu129
• Datasets 4.8.5
• Tokenizers 0.15.2