learning_rate: 0.0006
train_batch_size: 16
eval_batch_size: 16
seed: 40817
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
4.8288	0.2915	1000	4.7444	0.2553
4.3395	0.5830	2000	4.2826	0.2987
4.144	0.8744	3000	4.1010	0.3144
3.9818	1.1659	4000	3.9919	0.3248
3.9331	1.4573	5000	3.9199	0.3311
3.885	1.7488	6000	3.8596	0.3359
3.7515	2.0402	7000	3.8174	0.3406
3.7611	2.3317	8000	3.7886	0.3433
3.7354	2.6232	9000	3.7566	0.3463
3.7297	2.9147	10000	3.7341	0.3487
3.6354	3.2061	11000	3.7196	0.3504
3.6386	3.4976	12000	3.7009	0.3523
3.6536	3.7890	13000	3.6821	0.3539
3.5371	4.0804	14000	3.6762	0.3548
3.5789	4.3719	15000	3.6657	0.3562
3.5845	4.6634	16000	3.6500	0.3577
3.5814	4.9549	17000	3.6343	0.3588
3.5152	5.2463	18000	3.6400	0.3590
3.5248	5.5378	19000	3.6301	0.3599
3.5228	5.8293	20000	3.6171	0.3610
3.4436	6.1207	21000	3.6210	0.3616
3.4777	6.4121	22000	3.6135	0.3620
3.4897	6.7036	23000	3.6027	0.3629
3.4959	6.9951	24000	3.5947	0.3638
3.4359	7.2865	25000	3.6021	0.3636
3.4481	7.5780	26000	3.5938	0.3643
3.466	7.8695	27000	3.5864	0.3645
3.3983	8.1609	28000	3.5934	0.3649
3.4215	8.4524	29000	3.5870	0.3653
3.4396	8.7438	30000	3.5782	0.3659
3.3296	9.0353	31000	3.5820	0.3663
3.3799	9.3267	32000	3.5794	0.3666
3.4011	9.6182	33000	3.5739	0.3670
3.4194	9.9097	34000	3.5641	0.3674
3.3417	10.2011	35000	3.5782	0.3673
3.3618	10.4926	36000	3.5727	0.3675
3.3869	10.7841	37000	3.5653	0.3681
3.2953	11.0755	38000	3.5769	0.3678
3.3422	11.3670	39000	3.5660	0.3687
3.3492	11.6584	40000	3.5607	0.3689
3.377	11.9499	41000	3.5544	0.3692
3.3162	12.2413	42000	3.5672	0.3688
3.3297	12.5328	43000	3.5598	0.3692
3.3463	12.8243	44000	3.5502	0.3698
3.2682	13.1157	45000	3.5678	0.3693
3.3141	13.4072	46000	3.5580	0.3698
3.3211	13.6987	47000	3.5547	0.3701
3.3334	13.9901	48000	3.5467	0.3705
3.2864	14.2816	49000	3.5620	0.3700
3.3158	14.5730	50000	3.5552	0.3703
3.3186	14.8645	51000	3.5447	0.3710
3.2453	15.1559	52000	3.5622	0.3703
3.2889	15.4474	53000	3.5549	0.3705
3.3127	15.7389	54000	3.5495	0.3714
3.201	16.0303	55000	3.5588	0.3707
3.2569	16.3218	56000	3.5543	0.3709
3.2891	16.6133	57000	3.5505	0.3713
3.2881	16.9047	58000	3.5432	0.3717
3.2088	17.1962	59000	3.5598	0.3710
3.2513	17.4876	60000	3.5491	0.3715
3.2763	17.7791	61000	3.5423	0.3721
3.1977	18.0705	62000	3.5583	0.3711
3.2324	18.3620	63000	3.5518	0.3718
3.2553	18.6535	64000	3.5457	0.3723
3.276	18.9450	65000	3.5395	0.3723
3.2186	19.2364	66000	3.5537	0.3717
3.2391	19.5279	67000	3.5497	0.3723
3.2677	19.8193	68000	3.5402	0.3727
3.1862	20.1108	69000	3.5608	0.3716
3.2186	20.4022	70000	3.5543	0.3720
3.2428	20.6937	71000	3.5437	0.3727
3.2571	20.9852	72000	3.5382	0.3730
3.1898	21.2766	73000	3.5569	0.3720
3.2163	21.5681	74000	3.5479	0.3728
3.2475	21.8596	75000	3.5406	0.3727
3.1724	22.1510	76000	3.5550	0.3724
3.2006	22.4425	77000	3.5527	0.3726
3.2285	22.7339	78000	3.5436	0.3730
3.1315	23.0254	79000	3.5558	0.3726
3.1727	23.3168	80000	3.5566	0.3721
3.2067	23.6083	81000	3.5447	0.3728
3.2238	23.8998	82000	3.5388	0.3736
3.1458	24.1912	83000	3.5557	0.3729
3.1786	24.4827	84000	3.5464	0.3732
3.1966	24.7742	85000	3.5415	0.3733
3.1231	25.0656	86000	3.5582	0.3727
3.165	25.3571	87000	3.5511	0.3731
3.1921	25.6485	88000	3.5473	0.3733
3.1958	25.9400	89000	3.5356	0.3738
3.1396	26.2314	90000	3.5572	0.3730
3.1869	26.5229	91000	3.5482	0.3732
3.1882	26.8144	92000	3.5413	0.3737
3.1096	27.1058	93000	3.5579	0.3728
3.1547	27.3973	94000	3.5497	0.3732
3.1715	27.6888	95000	3.5448	0.3740
3.1875	27.9802	96000	3.5388	0.3740
3.123	28.2717	97000	3.5565	0.3733
3.1556	28.5631	98000	3.5467	0.3735
3.1624	28.8546	99000	3.5422	0.3741
3.1011	29.1460	100000	3.5573	0.3733
3.1366	29.4375	101000	3.5537	0.3736
3.1538	29.7290	102000	3.5466	0.3741
3.0905	30.0204	103000	3.5543	0.3737
3.119	30.3119	104000	3.5559	0.3735
3.1389	30.6034	105000	3.5475	0.3739
3.1602	30.8948	106000	3.5397	0.3745
3.0896	31.1863	107000	3.5571	0.3736
3.1158	31.4777	108000	3.5511	0.3740
3.1415	31.7692	109000	3.5467	0.3741

Framework versions

Transformers 4.55.2
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

craa/exceptions_exp2_swap_last_to_hit_40817

exceptions_exp2_swap_last_to_hit_40817

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

craa/
exceptions_exp2_swap_last_to_hit_40817