learning_rate: 5e-05
train_batch_size: 1024
eval_batch_size: 1024
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 156250
num_epochs: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.4587	0.3774	500	1.7455
0.3676	0.7547	1000	1.3984
0.3343	1.1321	1500	1.2729
0.3118	1.5094	2000	1.1772
0.2953	1.8868	2500	1.0904
0.2771	2.2642	3000	1.0169
0.2605	2.6415	3500	0.9581
0.2501	3.0189	4000	0.8991
0.2351	3.3962	4500	0.8535
0.2245	3.7736	5000	0.8164
0.2168	4.1509	5500	0.7843
0.2121	4.5283	6000	0.7684
0.205	4.9057	6500	0.7447
0.1999	5.2830	7000	0.7284
0.196	5.6604	7500	0.7089
0.1894	6.0377	8000	0.7045
0.188	6.4151	8500	0.6867
0.1826	6.7925	9000	0.6750
0.1821	7.1698	9500	0.6672
0.1753	7.5472	10000	0.6650
0.1746	7.9245	10500	0.6485
0.1714	8.3019	11000	0.6420
0.1726	8.6792	11500	0.6365
0.169	9.0566	12000	0.6300
0.1659	9.4340	12500	0.6244
0.1653	9.8113	13000	0.6164
0.1646	10.1887	13500	0.6122
0.1623	10.5660	14000	0.6070
0.1629	10.9434	14500	0.6045
0.1603	11.3208	15000	0.5999
0.16	11.6981	15500	0.5948
0.1582	12.0755	16000	0.5898
0.1565	12.4528	16500	0.5868
0.1541	12.8302	17000	0.5844
0.1553	13.2075	17500	0.5798
0.152	13.5849	18000	0.5791
0.1536	13.9623	18500	0.5745
0.1525	14.3396	19000	0.5722
0.1516	14.7170	19500	0.5718
0.151	15.0943	20000	0.5675
0.1502	15.4717	20500	0.5672
0.1505	15.8491	21000	0.5639
0.1497	16.2264	21500	0.5607
0.1495	16.6038	22000	0.5583
0.1463	16.9811	22500	0.5547
0.1478	17.3585	23000	0.5556
0.1468	17.7358	23500	0.5534
0.1468	18.1132	24000	0.5509
0.1447	18.4906	24500	0.5480
0.1451	18.8679	25000	0.5479
0.1449	19.2453	25500	0.5453
0.1433	19.6226	26000	0.5449
0.1434	20.0	26500	0.5423
0.1434	20.3774	27000	0.5404
0.1428	20.7547	27500	0.5393
0.1435	21.1321	28000	0.5391
0.142	21.5094	28500	0.5371
0.142	21.8868	29000	0.5342
0.1418	22.2642	29500	0.5340
0.1417	22.6415	30000	0.5322
0.1405	23.0189	30500	0.5309
0.1412	23.3962	31000	0.5300
0.1395	23.7736	31500	0.5295
0.1383	24.1509	32000	0.5289
0.1373	24.5283	32500	0.5272
0.139	24.9057	33000	0.5260

Framework versions

Transformers 4.51.1
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

suku9/pretrain

pretrain

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

suku9/
pretrain