Model description

More information needed

Intended uses & limitations

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training Loss	Epoch	Step	Validation Loss
0.2035	0.2332	500	0.1948
0.2163	0.4664	1000	0.1840
0.2017	0.6996	1500	0.1708
0.1812	0.9328	2000	0.1635
0.1083	1.1660	2500	0.1655
0.1022	1.3993	3000	0.1597
0.1031	1.6325	3500	0.1551
0.0986	1.8657	4000	0.1479
0.0296	2.0989	4500	0.1816
0.0293	2.3321	5000	0.1825
0.0324	2.5653	5500	0.1819
0.0253	2.7985	6000	0.1833