learning_rate: 0.0006
train_batch_size: 16
eval_batch_size: 16
seed: 2128
gradient_accumulation_steps: 5
total_train_batch_size: 80
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 50.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.8515	0.2917	1000	4.7801	0.2516
4.3428	0.5834	2000	4.2957	0.2985
4.1525	0.8750	3000	4.1082	0.3144
3.9928	1.1665	4000	3.9991	0.3236
3.9544	1.4582	5000	3.9244	0.3307
3.8856	1.7499	6000	3.8674	0.3359
3.7603	2.0414	7000	3.8245	0.3403
3.7671	2.3331	8000	3.7918	0.3431
3.7536	2.6248	9000	3.7651	0.3457
3.7264	2.9165	10000	3.7362	0.3484
3.6487	3.2080	11000	3.7267	0.3501
3.6643	3.4996	12000	3.7049	0.3519
3.6401	3.7913	13000	3.6886	0.3536
3.5486	4.0828	14000	3.6832	0.3544
3.5843	4.3745	15000	3.6713	0.3557
3.5938	4.6662	16000	3.6563	0.3572
3.5889	4.9579	17000	3.6429	0.3582
3.5183	5.2494	18000	3.6475	0.3588
3.5361	5.5411	19000	3.6348	0.3598
3.5298	5.8327	20000	3.6238	0.3605
3.4503	6.1243	21000	3.6309	0.3607
3.4847	6.4159	22000	3.6220	0.3615
3.5044	6.7076	23000	3.6105	0.3624
3.5079	6.9993	24000	3.6000	0.3632
3.4473	7.2908	25000	3.6113	0.3630
3.4639	7.5825	26000	3.5991	0.3638
3.4571	7.8742	27000	3.5924	0.3645
3.3906	8.1657	28000	3.5993	0.3647
3.4219	8.4574	29000	3.5920	0.3649
3.4316	8.7490	30000	3.5814	0.3659
3.3316	9.0405	31000	3.5920	0.3654
3.3984	9.3322	32000	3.5865	0.3664
3.4144	9.6239	33000	3.5803	0.3668
3.4152	9.9156	34000	3.5718	0.3670
3.3601	10.2071	35000	3.5833	0.3668
3.3756	10.4988	36000	3.5755	0.3674
3.3847	10.7905	37000	3.5667	0.3678
3.3057	11.0820	38000	3.5765	0.3678
3.3587	11.3736	39000	3.5761	0.3679
3.3723	11.6653	40000	3.5677	0.3683
3.3778	11.9570	41000	3.5586	0.3689
3.3082	12.2485	42000	3.5741	0.3682
3.3506	12.5402	43000	3.5653	0.3686
3.3579	12.8319	44000	3.5576	0.3694
3.2799	13.1234	45000	3.5692	0.3690
3.3212	13.4151	46000	3.5659	0.3694
3.342	13.7067	47000	3.5554	0.3700
3.3466	13.9984	48000	3.5507	0.3701
3.2857	14.2899	49000	3.5663	0.3693
3.3127	14.5816	50000	3.5587	0.3700
3.3409	14.8733	51000	3.5520	0.3705
3.2616	15.1648	52000	3.5650	0.3699
3.2942	15.4565	53000	3.5595	0.3704
3.316	15.7482	54000	3.5505	0.3710
3.2177	16.0397	55000	3.5616	0.3704
3.2589	16.3313	56000	3.5593	0.3706
3.2948	16.6230	57000	3.5550	0.3709
3.302	16.9147	58000	3.5431	0.3714
3.2448	17.2062	59000	3.5620	0.3705
3.2549	17.4979	60000	3.5558	0.3711
3.3044	17.7896	61000	3.5452	0.3714
3.2085	18.0811	62000	3.5579	0.3713
3.2492	18.3728	63000	3.5574	0.3711
3.2529	18.6644	64000	3.5478	0.3718
3.2768	18.9561	65000	3.5392	0.3722
3.226	19.2476	66000	3.5608	0.3711
3.2417	19.5393	67000	3.5528	0.3719
3.2717	19.8310	68000	3.5444	0.3722
3.1841	20.1225	69000	3.5608	0.3715
3.2286	20.4142	70000	3.5516	0.3720
3.2528	20.7059	71000	3.5460	0.3723
3.2614	20.9975	72000	3.5407	0.3726
3.2022	21.2891	73000	3.5603	0.3718
3.2232	21.5807	74000	3.5498	0.3725
3.2561	21.8724	75000	3.5419	0.3727
3.1736	22.1639	76000	3.5595	0.3721
3.2185	22.4556	77000	3.5529	0.3724
3.2191	22.7473	78000	3.5429	0.3729
3.1369	23.0388	79000	3.5581	0.3722
3.1955	23.3305	80000	3.5550	0.3723
3.2039	23.6222	81000	3.5514	0.3723
3.2286	23.9138	82000	3.5425	0.3733
3.1609	24.2053	83000	3.5575	0.3722
3.1949	24.4970	84000	3.5499	0.3728
3.2264	24.7887	85000	3.5446	0.3732

Framework versions

Transformers 4.55.2
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

craa/exceptions_exp2_swap_0.7_cost_to_hit_2128

exceptions_exp2_swap_0.7_cost_to_hit_2128

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

craa/
exceptions_exp2_swap_0.7_cost_to_hit_2128