learning_rate: 0.0006
train_batch_size: 16
eval_batch_size: 16
seed: 5039
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
4.8389	0.2916	1000	4.7840	0.2508
4.3668	0.5831	2000	4.2972	0.2979
4.1742	0.8747	3000	4.1106	0.3136
4.0092	1.1662	4000	4.0018	0.3235
3.9283	1.4578	5000	3.9267	0.3306
3.9025	1.7493	6000	3.8681	0.3360
3.7392	2.0408	7000	3.8234	0.3399
3.7636	2.3324	8000	3.7944	0.3429
3.729	2.6239	9000	3.7647	0.3459
3.7381	2.9155	10000	3.7375	0.3482
3.6516	3.2070	11000	3.7226	0.3504
3.6536	3.4986	12000	3.7043	0.3518
3.6419	3.7901	13000	3.6872	0.3537
3.5465	4.0816	14000	3.6797	0.3549
3.5873	4.3732	15000	3.6692	0.3563
3.5885	4.6648	16000	3.6552	0.3569
3.592	4.9563	17000	3.6410	0.3584
3.5235	5.2478	18000	3.6458	0.3588
3.5286	5.5394	19000	3.6334	0.3599
3.5339	5.8310	20000	3.6228	0.3610
3.4402	6.1225	21000	3.6266	0.3614
3.488	6.4140	22000	3.6171	0.3620
3.4822	6.7056	23000	3.6088	0.3626
3.5088	6.9971	24000	3.5973	0.3632
3.4569	7.2886	25000	3.6074	0.3634
3.4598	7.5802	26000	3.6001	0.3639
3.4805	7.8718	27000	3.5892	0.3647
3.391	8.1633	28000	3.5987	0.3648
3.4192	8.4548	29000	3.5941	0.3649
3.4553	8.7464	30000	3.5835	0.3656
3.3433	9.0379	31000	3.5879	0.3658
3.3777	9.3295	32000	3.5863	0.3660
3.4124	9.6210	33000	3.5779	0.3669
3.4163	9.9126	34000	3.5696	0.3671
3.3552	10.2041	35000	3.5840	0.3665
3.3787	10.4957	36000	3.5789	0.3674
3.3957	10.7872	37000	3.5704	0.3675
3.3018	11.0787	38000	3.5795	0.3677
3.3555	11.3703	39000	3.5742	0.3679
3.3832	11.6618	40000	3.5648	0.3685
3.3791	11.9534	41000	3.5585	0.3690
3.3167	12.2449	42000	3.5723	0.3683
3.3413	12.5365	43000	3.5661	0.3689
3.3628	12.8280	44000	3.5583	0.3694
3.2734	13.1195	45000	3.5724	0.3691
3.3231	13.4111	46000	3.5654	0.3693
3.3429	13.7027	47000	3.5575	0.3698
3.3593	13.9942	48000	3.5502	0.3706
3.2888	14.2857	49000	3.5649	0.3696
3.3147	14.5773	50000	3.5584	0.3701
3.3218	14.8689	51000	3.5499	0.3707
3.2606	15.1604	52000	3.5661	0.3699
3.2897	15.4519	53000	3.5600	0.3704
3.3127	15.7435	54000	3.5523	0.3707
3.2188	16.0350	55000	3.5637	0.3702
3.2612	16.3265	56000	3.5612	0.3705
3.2943	16.6181	57000	3.5506	0.3713
3.3085	16.9097	58000	3.5449	0.3714
3.2314	17.2012	59000	3.5642	0.3707
3.2629	17.4927	60000	3.5536	0.3713
3.2787	17.7843	61000	3.5458	0.3717
3.2018	18.0758	62000	3.5633	0.3710
3.2435	18.3674	63000	3.5584	0.3713
3.2724	18.6589	64000	3.5488	0.3718
3.284	18.9505	65000	3.5413	0.3723
3.2149	19.2420	66000	3.5605	0.3713
3.2538	19.5336	67000	3.5527	0.3717
3.2765	19.8251	68000	3.5437	0.3726
3.1922	20.1166	69000	3.5602	0.3712
3.2177	20.4082	70000	3.5591	0.3715
3.2451	20.6997	71000	3.5503	0.3720
3.2648	20.9913	72000	3.5401	0.3729
3.206	21.2828	73000	3.5584	0.3716
3.2347	21.5744	74000	3.5520	0.3721
3.2503	21.8659	75000	3.5425	0.3728
3.1884	22.1574	76000	3.5599	0.3720
3.201	22.4490	77000	3.5554	0.3722
3.2315	22.7406	78000	3.5435	0.3729
3.1297	23.0321	79000	3.5574	0.3722
3.181	23.3236	80000	3.5602	0.3721
3.2171	23.6152	81000	3.5474	0.3729
3.2329	23.9068	82000	3.5409	0.3731
3.1634	24.1983	83000	3.5631	0.3721
3.2056	24.4898	84000	3.5525	0.3726
3.2099	24.7814	85000	3.5461	0.3732
3.137	25.0729	86000	3.5613	0.3723
3.1779	25.3645	87000	3.5577	0.3723
3.1919	25.6560	88000	3.5501	0.3730
3.2131	25.9476	89000	3.5394	0.3736
3.1418	26.2391	90000	3.5594	0.3726
3.1775	26.5306	91000	3.5563	0.3727
3.1992	26.8222	92000	3.5488	0.3734
3.1247	27.1137	93000	3.5643	0.3725
3.1699	27.4053	94000	3.5572	0.3729
3.1893	27.6968	95000	3.5476	0.3734
3.1944	27.9884	96000	3.5439	0.3739
3.1462	28.2799	97000	3.5595	0.3729
3.1687	28.5715	98000	3.5537	0.3733
3.1761	28.8630	99000	3.5453	0.3738
3.1207	29.1545	100000	3.5631	0.3731
3.1545	29.4461	101000	3.5610	0.3732
3.1726	29.7377	102000	3.5489	0.3735
3.0964	30.0292	103000	3.5597	0.3731
3.1257	30.3207	104000	3.5614	0.3733
3.1518	30.6123	105000	3.5540	0.3735
3.1596	30.9038	106000	3.5481	0.3741
3.1093	31.1953	107000	3.5633	0.3731
3.131	31.4869	108000	3.5570	0.3737
3.1623	31.7785	109000	3.5498	0.3740

Framework versions

Transformers 4.55.2
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

craa/exceptions_exp2_swap_0.3_cost_to_drop_5039

exceptions_exp2_swap_0.3_cost_to_drop_5039

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

craa/
exceptions_exp2_swap_0.3_cost_to_drop_5039