learning_rate: 0.0005
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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_ratio: 0.1
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.9817	1.0	125	1.8978	0.3904	0.4364	0.3945	0.3671
1.4617	2.0	250	1.5796	0.4885	0.5129	0.4772	0.4655
1.5424	3.0	375	1.6331	0.4925	0.5168	0.4872	0.4734
1.4632	4.0	500	1.5948	0.4825	0.5403	0.4766	0.4804
1.7462	5.0	625	1.7594	0.4635	0.4898	0.4523	0.4429
1.6465	6.0	750	1.6923	0.4505	0.4989	0.4408	0.4308
1.56	7.0	875	1.6831	0.4715	0.4817	0.4604	0.4425
1.5607	8.0	1000	1.6873	0.4655	0.4706	0.4558	0.4463
1.3827	9.0	1125	1.7233	0.4474	0.5176	0.4442	0.4368
1.3536	10.0	1250	1.6615	0.4635	0.5110	0.4614	0.4508
1.2313	11.0	1375	1.5771	0.5155	0.5175	0.5065	0.5016
1.2788	12.0	1500	1.5114	0.5195	0.5487	0.5166	0.5122
1.1639	13.0	1625	1.5757	0.5065	0.5282	0.4983	0.4987
1.1907	14.0	1750	1.5826	0.5225	0.5410	0.5181	0.5153
1.1847	15.0	1875	1.4701	0.5405	0.5543	0.5347	0.5278
1.1288	16.0	2000	1.5231	0.5385	0.5349	0.5286	0.5261
1.1139	17.0	2125	1.4629	0.5405	0.5474	0.5379	0.5265
1.1159	18.0	2250	1.4014	0.5716	0.5841	0.5669	0.5652
0.8868	19.0	2375	1.5939	0.5145	0.5344	0.5192	0.5074
0.9136	20.0	2500	1.4781	0.5726	0.5796	0.5631	0.5616
0.9439	21.0	2625	1.4034	0.5846	0.5844	0.5749	0.5698
0.9159	22.0	2750	1.4365	0.5606	0.5696	0.5565	0.5551
0.8419	23.0	2875	1.5117	0.5656	0.5834	0.5652	0.5596
0.7093	24.0	3000	1.4506	0.5806	0.5909	0.5786	0.5758
0.7191	25.0	3125	1.5376	0.5526	0.5653	0.5473	0.5417
0.7089	26.0	3250	1.5345	0.5826	0.5983	0.5835	0.5785
0.5905	27.0	3375	1.5149	0.5826	0.5796	0.5772	0.5738
0.6271	28.0	3500	1.4676	0.5746	0.5765	0.5705	0.5671
0.576	29.0	3625	1.4894	0.6086	0.6098	0.6046	0.6004
0.6057	30.0	3750	1.4433	0.6256	0.6284	0.6215	0.6174
0.5378	31.0	3875	1.5149	0.6076	0.6095	0.6008	0.6008
0.5169	32.0	4000	1.4866	0.6026	0.6029	0.5958	0.5920
0.4413	33.0	4125	1.4170	0.6326	0.6383	0.6294	0.6261
0.4717	34.0	4250	1.5256	0.6186	0.6224	0.6153	0.6122
0.4661	35.0	4375	1.5266	0.6216	0.6239	0.6164	0.6159
0.3986	36.0	4500	1.4901	0.6286	0.6287	0.6204	0.6187
0.3874	37.0	4625	1.4581	0.6336	0.6290	0.6306	0.6272
0.3893	38.0	4750	1.5825	0.6266	0.6270	0.6248	0.6210
0.3332	39.0	4875	1.5173	0.6326	0.6287	0.6266	0.6242
0.3103	40.0	5000	1.5291	0.6236	0.6230	0.6152	0.6140
0.2876	41.0	5125	1.5591	0.6276	0.6351	0.6255	0.6207
0.2468	42.0	5250	1.5966	0.6296	0.6331	0.6256	0.6220
0.2982	43.0	5375	1.5453	0.6456	0.6468	0.6423	0.6398
0.2447	44.0	5500	1.4680	0.6527	0.6460	0.6445	0.6433
0.2156	45.0	5625	1.4925	0.6677	0.6651	0.6622	0.6613
0.2024	46.0	5750	1.5198	0.6476	0.6432	0.6443	0.6420
0.2126	47.0	5875	1.5174	0.6496	0.6453	0.6459	0.6430
0.1759	48.0	6000	1.5844	0.6517	0.6494	0.6485	0.6473
0.1558	49.0	6125	1.5302	0.6607	0.6512	0.6525	0.6501
0.2352	50.0	6250	1.5895	0.6496	0.6478	0.6446	0.6437

Framework versions

Transformers 4.55.1
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.4

zkdeng/50-vit-mae-base-finetuned-spiderTraining20-500

50-vit-mae-base-finetuned-spiderTraining20-500

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

zkdeng/
50-vit-mae-base-finetuned-spiderTraining20-500