exceptions_exp2_swap_0.7_cost_to_push_1032

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

• Loss: 3.5626
• Accuracy: 0.3686

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: 1032
• 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
4.8221	0.2917	1000	4.7593	0.2542
4.3417	0.5834	2000	4.2879	0.2986
4.147	0.8750	3000	4.1020	0.3142
3.9978	1.1665	4000	3.9990	0.3241
3.933	1.4582	5000	3.9232	0.3306
3.89	1.7499	6000	3.8674	0.3358
3.7573	2.0414	7000	3.8255	0.3394
3.7649	2.3331	8000	3.7925	0.3429
3.7441	2.6248	9000	3.7650	0.3455
3.724	2.9165	10000	3.7359	0.3479
3.6396	3.2080	11000	3.7250	0.3501
3.6656	3.4996	12000	3.7067	0.3515
3.6496	3.7913	13000	3.6870	0.3535
3.5592	4.0828	14000	3.6806	0.3546
3.5816	4.3745	15000	3.6709	0.3556
3.5843	4.6662	16000	3.6570	0.3569
3.5853	4.9579	17000	3.6444	0.3581
3.5179	5.2494	18000	3.6456	0.3589
3.5276	5.5411	19000	3.6335	0.3597
3.5528	5.8327	20000	3.6229	0.3604
3.4565	6.1243	21000	3.6277	0.3612
3.4832	6.4159	22000	3.6183	0.3617
3.5	6.7076	23000	3.6094	0.3624
3.5027	6.9993	24000	3.6001	0.3633
3.4485	7.2908	25000	3.6078	0.3633
3.4507	7.5825	26000	3.5988	0.3638
3.468	7.8742	27000	3.5906	0.3645
3.4022	8.1657	28000	3.5997	0.3647
3.4318	8.4574	29000	3.5896	0.3651
3.4339	8.7490	30000	3.5842	0.3657
3.3313	9.0405	31000	3.5895	0.3658
3.3822	9.3322	32000	3.5894	0.3659
3.4078	9.6239	33000	3.5783	0.3666
3.4199	9.9156	34000	3.5703	0.3670
3.3396	10.2071	35000	3.5825	0.3667
3.3698	10.4988	36000	3.5768	0.3675
3.3957	10.7905	37000	3.5688	0.3679
3.3101	11.0820	38000	3.5771	0.3676
3.3343	11.3736	39000	3.5724	0.3680
3.3702	11.6653	40000	3.5626	0.3686
3.3747	11.9570	41000	3.5580	0.3690
3.3259	12.2485	42000	3.5699	0.3687
3.3345	12.5402	43000	3.5636	0.3691
3.3483	12.8319	44000	3.5551	0.3698
3.2723	13.1234	45000	3.5680	0.3688
3.3186	13.4151	46000	3.5634	0.3691
3.3391	13.7067	47000	3.5570	0.3697
3.3475	13.9984	48000	3.5473	0.3703
3.2852	14.2899	49000	3.5636	0.3697
3.305	14.5816	50000	3.5543	0.3702
3.3277	14.8733	51000	3.5498	0.3706
3.26	15.1648	52000	3.5639	0.3699
3.2878	15.4565	53000	3.5564	0.3704
3.3047	15.7482	54000	3.5522	0.3705
3.2262	16.0397	55000	3.5611	0.3704
3.2727	16.3313	56000	3.5555	0.3706
3.2713	16.6230	57000	3.5496	0.3710
3.3144	16.9147	58000	3.5434	0.3720
3.2465	17.2062	59000	3.5589	0.3709
3.2631	17.4979	60000	3.5505	0.3713
3.294	17.7896	61000	3.5473	0.3717
3.1983	18.0811	62000	3.5589	0.3712
3.2483	18.3728	63000	3.5553	0.3713
3.2677	18.6644	64000	3.5493	0.3716
3.2871	18.9561	65000	3.5399	0.3723
3.2203	19.2476	66000	3.5564	0.3714
3.2523	19.5393	67000	3.5514	0.3717
3.2629	19.8310	68000	3.5415	0.3723
3.1843	20.1225	69000	3.5562	0.3716
3.2293	20.4142	70000	3.5539	0.3718
3.2643	20.7059	71000	3.5472	0.3725
3.2582	20.9975	72000	3.5392	0.3727
3.2087	21.2891	73000	3.5542	0.3722
3.2295	21.5807	74000	3.5483	0.3725
3.2496	21.8724	75000	3.5425	0.3730
3.1755	22.1639	76000	3.5606	0.3719
3.2194	22.4556	77000	3.5504	0.3725
3.2294	22.7473	78000	3.5416	0.3730
3.1497	23.0388	79000	3.5590	0.3722
3.1863	23.3305	80000	3.5539	0.3723
3.219	23.6222	81000	3.5459	0.3729
3.2317	23.9138	82000	3.5394	0.3736
3.1605	24.2053	83000	3.5567	0.3725
3.1906	24.4970	84000	3.5497	0.3730
3.2165	24.7887	85000	3.5424	0.3733
3.1377	25.0802	86000	3.5580	0.3729
3.1862	25.3719	87000	3.5547	0.3728
3.1973	25.6636	88000	3.5481	0.3732
3.2138	25.9553	89000	3.5411	0.3739
3.1555	26.2468	90000	3.5553	0.3730
3.1946	26.5384	91000	3.5513	0.3735
3.1931	26.8301	92000	3.5409	0.3739

Framework versions

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