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CodeIsAbstract/
HybridModelScratch_lightn_Conti

Name: HybridModelScratch_lightn_Conti
Rating: 2.5 (9 reviews)

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HybridModelScratch_lightn_Conti

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

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The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 120
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 150
training_steps: 50000

Training Loss	Epoch	Step	Accuracy	Validation Loss
4.5799	0.02	1000	0.2697	4.5051
4.1703	0.04	2000	0.3056	4.1036
4.0044	0.06	3000	0.3195	3.9451
3.8638	0.08	4000	0.3281	3.8499
3.7991	0.1	5000	0.3350	3.7702
3.6996	0.12	6000	0.3414	3.7112
3.6319	0.14	7000	0.3448	3.6666
3.6062	0.16	8000	0.3476	3.6405
3.6643	0.18	9000	0.3498	3.6175
3.5992	0.2	10000	0.3535	3.5797
3.5671	0.22	11000	0.3559	3.5543
3.5230	0.24	12000	0.3578	3.5308
3.5930	0.26	13000	0.3593	3.5190
3.5475	0.28	14000	0.3616	3.4961
3.5220	0.3	15000	0.3633	3.4778
3.5050	0.32	16000	0.3644	3.4654
3.4776	0.34	17000	0.3666	3.4458
3.4829	0.36	18000	0.3674	3.4346
3.4483	0.38	19000	0.3694	3.4189
3.4418	0.4	20000	0.3704	3.4063
3.4183	0.42	21000	0.3720	3.3956
3.3858	0.44	22000	0.3725	3.3870
3.3654	0.46	23000	0.3728	3.3824
3.3943	0.48	24000	0.3734	3.3756
3.3957	0.5	25000	0.3757	3.3548
3.3787	0.52	26000	0.3769	3.3452
3.3605	0.54	27000	0.3777	3.3393
3.3707	0.56	28000	0.3787	3.3302
3.3508	0.58	29000	0.3793	3.3221
3.3566	0.6	30000	0.3802	3.3148
3.3441	0.62	31000	0.3809	3.3054
3.3268	0.64	32000	0.3814	3.3028
3.0620	0.02	33000	3.3109	0.3804
3.0159	0.04	34000	3.3074	0.3810
3.0230	0.06	35000	3.3025	0.3815
3.0335	0.08	36000	3.2988	0.3821
3.0098	0.1	37000	3.2959	0.3826
3.0096	0.12	38000	3.2932	0.3826
2.9760	0.14	39000	3.2904	0.3831
2.9701	0.16	40000	3.2869	0.3832
2.9654	0.18	41000	3.2847	0.3837
2.9752	0.2	42000	3.2823	0.3839
2.9480	0.22	43000	3.2810	0.3840
2.9637	0.24	44000	3.2790	0.3842
2.9721	0.26	45000	3.2772	0.3843
2.9694	0.28	46000	3.2765	0.3847
2.9577	0.3	47000	3.2756	0.3847
2.9573	0.32	48000	3.2751	0.3847
2.9537	0.34	49000	3.2749	0.3848
2.9677	0.36	50000	3.2749	0.3848