exceptions_exp2_swap_0.3_cost_to_drop_3591

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

• Loss: 3.5628
• Accuracy: 0.3685

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: 0.0006
• train_batch_size: 16
• eval_batch_size: 16
• seed: 3591
• 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	Accuracy	Validation Loss
4.8365	0.2916	1000	0.2548	4.7491
4.3471	0.5831	2000	0.2988	4.2858
4.1508	0.8747	3000	0.3151	4.0989
3.9896	1.1662	4000	0.3244	3.9914
3.9465	1.4578	5000	0.3314	3.9160
3.8798	1.7493	6000	0.3365	3.8590
3.7577	2.0408	7000	0.3405	3.8170
3.7548	2.3324	8000	0.3436	3.7865
3.7612	2.6239	9000	0.3464	3.7582
3.7282	2.9155	10000	0.3491	3.7295
3.6436	3.2070	11000	0.3508	3.7194
3.6459	3.4986	12000	0.3524	3.6989
3.6481	3.7901	13000	0.3541	3.6826
3.5535	4.0816	14000	0.3553	3.6771
3.5787	4.3732	15000	0.3564	3.6656
3.5765	4.6648	16000	0.3577	3.6524
3.584	4.9563	17000	0.3591	3.6387
3.5087	5.2478	18000	0.3591	3.6413
3.5284	5.5394	19000	0.3603	3.6312
3.5434	5.8310	20000	0.3609	3.6183
3.4492	6.1225	21000	0.3614	3.6239
3.487	6.4140	22000	0.3624	3.6121
3.4875	6.7056	23000	0.3631	3.6044
3.5	6.9971	24000	0.3638	3.5953
3.4285	7.2886	25000	0.3637	3.6057
3.4546	7.5802	26000	0.3645	3.5960
3.4594	7.8718	27000	0.3652	3.5860
3.3818	8.1633	28000	0.3648	3.5961
3.4168	8.4548	29000	0.3655	3.5885
3.4465	8.7464	30000	0.3661	3.5820
3.3283	9.0379	31000	0.3660	3.5848
3.3822	9.3295	32000	0.3662	3.5840
3.402	9.6210	33000	0.3671	3.5742
3.4218	9.9126	34000	0.3676	3.5681
3.3471	10.2041	35000	0.3673	3.5807
3.3754	10.4957	36000	0.3676	3.5724
3.3889	10.7872	37000	0.3681	3.5649
3.3111	11.0787	38000	0.3678	3.5778
3.3445	11.3703	39000	0.3684	3.5678
3.3621	11.6618	40000	0.3685	3.5628
3.3843	11.9534	41000	0.3692	3.5562
3.3039	12.2449	42000	0.3684	3.5673
3.3371	12.5365	43000	0.3692	3.5636
3.357	12.8280	44000	0.3698	3.5513
3.2723	13.1195	45000	0.3691	3.5671
3.3072	13.4111	46000	0.3695	3.5618
3.3428	13.7027	47000	0.3702	3.5526
3.3393	13.9942	48000	0.3705	3.5471
3.2803	14.2857	49000	0.3699	3.5606
3.3086	14.5773	50000	0.3704	3.5528
3.3236	14.8689	51000	0.3710	3.5478
3.248	15.1604	52000	0.3703	3.5615
3.2804	15.4519	53000	0.3706	3.5556
3.3118	15.7435	54000	0.3709	3.5484
3.2093	16.0350	55000	0.3705	3.5584
3.2675	16.3265	56000	0.3710	3.5577
3.2897	16.6181	57000	0.3714	3.5507
3.2994	16.9097	58000	0.3717	3.5419
3.235	17.2012	59000	0.3709	3.5576
3.2628	17.4927	60000	0.3708	3.5542
3.2733	17.7843	61000	0.3716	3.5446
3.1962	18.0758	62000	0.3713	3.5552
3.2368	18.3674	63000	0.3713	3.5570
3.2627	18.6589	64000	0.3719	3.5456
3.2811	18.9505	65000	0.3722	3.5395
3.2051	19.2420	66000	0.3717	3.5561
3.2372	19.5336	67000	0.3720	3.5484
3.2646	19.8251	68000	0.3726	3.5410
3.1758	20.1166	69000	0.3721	3.5565
3.2262	20.4082	70000	0.3720	3.5522
3.2448	20.6997	71000	0.3727	3.5422
3.2535	20.9913	72000	0.3731	3.5363
3.2035	21.2828	73000	0.3719	3.5573
3.227	21.5744	74000	0.3725	3.5450
3.2327	21.8659	75000	0.3731	3.5384
3.1776	22.1574	76000	0.3723	3.5553
3.2118	22.4490	77000	0.3730	3.5483
3.2266	22.7406	78000	0.3730	3.5435
3.1331	23.0321	79000	0.3724	3.5540
3.1916	23.3236	80000	0.3729	3.5507
3.187	23.6152	81000	3.5549	0.3726
3.2057	23.9068	82000	3.5462	0.3730
3.1548	24.1986	83000	3.5578	0.3727
3.1716	24.4901	84000	3.5530	0.3727
3.2119	24.7817	85000	3.5454	0.3734
3.1349	25.0732	86000	3.5582	0.3726
3.1728	25.3647	87000	3.5531	0.3734
3.1959	25.6563	88000	3.5434	0.3732
3.2124	25.9479	89000	3.5389	0.3738
3.144	26.2394	90000	3.5542	0.3731
3.1763	26.5309	91000	3.5478	0.3733
3.1947	26.8225	92000	3.5423	0.3738
3.1318	27.1140	93000	3.5578	0.3730
3.1523	27.4056	94000	3.5547	0.3735
3.1788	27.6971	95000	3.5462	0.3738
3.1949	27.9887	96000	3.5371	0.3745
3.1268	28.2802	97000	3.5564	0.3731
3.1661	28.5718	98000	3.5473	0.3737
3.1665	28.8633	99000	3.5398	0.3741
3.1139	29.1548	100000	3.5575	0.3734

Framework versions

• Transformers 4.55.2
• Pytorch 2.8.0+cu128
• Datasets 4.0.0
• Tokenizers 0.21.4