limbxy_seq_t2_heads4_layers2

This model is a fine-tuned version of c14kevincardenas/beit-large-patch16-384-limb on the c14kevincardenas/beta_caller_284_limbxy_seq_2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0053
• Rmse: 0.0727

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 2014
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 250
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rmse
0.0146	1.0	150	0.0181	0.1345
0.011	2.0	300	0.0086	0.0930
0.0089	3.0	450	0.0080	0.0892
0.0058	4.0	600	0.0086	0.0929
0.0041	5.0	750	0.0089	0.0941
0.003	6.0	900	0.0063	0.0791
0.003	7.0	1050	0.0070	0.0836
0.0026	8.0	1200	0.0074	0.0859
0.002	9.0	1350	0.0063	0.0796
0.0021	10.0	1500	0.0062	0.0788
0.0018	11.0	1650	0.0066	0.0814
0.002	12.0	1800	0.0058	0.0759
0.0011	13.0	1950	0.0063	0.0795
0.0012	14.0	2100	0.0058	0.0763
0.0012	15.0	2250	0.0059	0.0770
0.0009	16.0	2400	0.0057	0.0756
0.001	17.0	2550	0.0058	0.0760
0.0008	18.0	2700	0.0056	0.0745
0.0008	19.0	2850	0.0058	0.0761
0.0006	20.0	3000	0.0057	0.0757
0.0007	21.0	3150	0.0056	0.0750
0.0005	22.0	3300	0.0057	0.0758
0.0005	23.0	3450	0.0056	0.0749
0.0006	24.0	3600	0.0054	0.0735
0.0005	25.0	3750	0.0060	0.0772
0.0005	26.0	3900	0.0057	0.0755
0.0005	27.0	4050	0.0056	0.0748
0.0006	28.0	4200	0.0055	0.0738
0.0004	29.0	4350	0.0056	0.0751
0.0004	30.0	4500	0.0054	0.0734
0.0004	31.0	4650	0.0053	0.0730
0.0003	32.0	4800	0.0053	0.0728
0.0003	33.0	4950	0.0054	0.0732
0.0003	34.0	5100	0.0054	0.0732
0.0003	35.0	5250	0.0053	0.0727
0.0002	36.0	5400	0.0054	0.0734
0.0003	37.0	5550	0.0054	0.0735
0.0002	38.0	5700	0.0054	0.0735
0.0002	39.0	5850	0.0055	0.0742
0.0002	40.0	6000	0.0054	0.0736
0.0002	41.0	6150	0.0054	0.0732
0.0001	42.0	6300	0.0053	0.0727
0.0001	43.0	6450	0.0054	0.0734
0.0001	44.0	6600	0.0054	0.0733
0.0001	45.0	6750	0.0053	0.0730
0.0001	46.0	6900	0.0053	0.0729
0.0001	47.0	7050	0.0053	0.0731
0.0001	48.0	7200	0.0053	0.0730
0.0001	49.0	7350	0.0053	0.0730
0.0001	50.0	7500	0.0053	0.0731

Framework versions

• Transformers 4.45.2
• Pytorch 2.5.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1