9df9cb527dfb0744928ca8af0ab19886

This model is a fine-tuned version of studio-ousia/luke-japanese-base on the fancyzhx/dbpedia_14 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1115
• Data Size: 1.0
• Epoch Runtime: 1533.8296
• Accuracy: 0.9800
• F1 Macro: 0.9800
• Rouge1: 0.9801
• Rouge2: 0.0
• Rougel: 0.9800
• Rougelsum: 0.9800

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 32
• total_eval_batch_size: 32
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Data Size	Epoch Runtime	Accuracy	F1 Macro	Rouge1	Rouge2	Rougel	Rougelsum
No log	0	0	2.6537	0	58.0991	0.0898	0.0223	0.0898	0.0	0.0898	0.0898
1.0298	1	17500	0.5403	0.0078	69.5959	0.8271	0.7995	0.8271	0.0	0.8272	0.8271
0.3179	2	35000	0.2342	0.0156	81.7502	0.9386	0.9388	0.9385	0.0	0.9385	0.9386
0.2029	3	52500	0.2053	0.0312	103.9326	0.9508	0.9509	0.9508	0.0	0.9508	0.9509
0.1375	4	70000	0.1540	0.0625	149.5479	0.9603	0.9604	0.9603	0.0	0.9603	0.9603
0.1168	5	87500	0.1179	0.125	242.3395	0.9716	0.9717	0.9717	0.0	0.9716	0.9717
0.1148	6	105000	0.0922	0.25	424.7129	0.9797	0.9797	0.9797	0.0	0.9797	0.9797
0.0008	7	122500	0.1041	0.5	793.5609	0.9766	0.9767	0.9767	0.0	0.9766	0.9766
0.0985	8.0	140000	0.0807	1.0	1536.5280	0.9837	0.9837	0.9837	0.0	0.9837	0.9837
0.1065	9.0	157500	0.1143	1.0	1537.4133	0.9798	0.9798	0.9799	0.0	0.9798	0.9798
0.1287	10.0	175000	0.1108	1.0	1535.5634	0.9815	0.9815	0.9815	0.0	0.9815	0.9815
0.1117	11.0	192500	0.1233	1.0	1534.1917	0.9784	0.9785	0.9784	0.0	0.9784	0.9784
0.1266	12.0	210000	0.1115	1.0	1533.8296	0.9800	0.9800	0.9801	0.0	0.9800	0.9800

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.1