Model description

More information needed

Intended uses & limitations

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
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: cosine
lr_scheduler_warmup_ratio: 0.1
lr_scheduler_warmup_steps: 50
num_epochs: 5

Training Loss	Epoch	Step	Validation Loss
0.5518	0.1	250	0.5404
0.6025	0.2	500	0.5480
0.6125	0.3	750	0.5375
0.5424	0.4	1000	0.5382
0.5393	0.5	1250	0.5357
0.5091	0.6	1500	0.5414
0.5425	0.7	1750	0.5335
0.4942	0.8	2000	0.5646
0.5687	0.9	2250	0.5381
0.5462	1.0	2500	0.5609
0.5048	1.1	2750	0.4820
0.5025	1.2	3000	0.4848
0.4823	1.3	3250	0.4895
0.4914	1.4	3500	0.4482
0.4051	1.5	3750	0.4579
0.3931	1.6	4000	0.4225
0.3865	1.7	4250	0.4917
0.625	1.8	4500	0.4107
0.3882	1.9	4750	0.4676
0.2762	2.0	5000	0.4387
0.4132	2.1	5250	0.4282
0.527	2.2	5500	0.4122
0.4196	2.3	5750	0.3563
0.3263	2.4	6000	0.3680
0.354	2.5	6250	0.3632
0.4371	2.6	6500	0.3458
0.3613	2.7	6750	0.3582
0.3223	2.8	7000	0.3965
0.4591	2.9	7250	0.3279
0.4607	3.0	7500	0.3537
0.1142	3.1	7750	0.3854
0.4335	3.2	8000	0.3537
0.4776	3.3	8250	0.4166
0.3446	3.4	8500	0.3924
0.0918	3.5	8750	0.4337
0.3786	3.6	9000	0.4035
0.3945	3.7	9250	0.3965
0.1989	3.8	9500	0.4014
0.3032	3.9	9750	0.3335
0.2882	4.0	10000	0.3608
0.3639	4.1	10250	0.4448
0.283	4.2	10500	0.4376
0.2759	4.3	10750	0.4415
0.4924	4.4	11000	0.4275
0.3681	4.5	11250	0.4300
0.2862	4.6	11500	0.4283
0.2423	4.7	11750	0.4243
0.0904	4.8	12000	0.4217
0.2766	4.9	12250	0.4226
0.3408	5.0	12500	0.4226