learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	8	4.4378	0.0114	4.4368	2.1064
No log	2.0	16	1.8042	0.0427	1.8034	1.3429
No log	3.0	24	1.3162	0.0220	1.3154	1.1469
No log	4.0	32	1.0570	0.0102	1.0564	1.0278
No log	5.0	40	1.9292	0.0632	1.9282	1.3886
No log	6.0	48	1.2466	0.0202	1.2458	1.1162
No log	7.0	56	1.5970	0.1003	1.5962	1.2634
No log	8.0	64	1.1620	0.0391	1.1616	1.0778
No log	9.0	72	1.0027	0.0220	1.0024	1.0012
No log	10.0	80	1.2804	0.1152	1.2795	1.1311
No log	11.0	88	0.9198	0.3580	0.9198	0.9591
No log	12.0	96	1.2957	0.1402	1.2949	1.1379
No log	13.0	104	0.7620	0.3593	0.7620	0.8729
No log	14.0	112	0.7296	0.3770	0.7294	0.8541
No log	15.0	120	1.2950	0.2341	1.2944	1.1377
No log	16.0	128	1.1351	0.2614	1.1352	1.0655
No log	17.0	136	0.6913	0.3382	0.6914	0.8315
No log	18.0	144	0.6891	0.4497	0.6890	0.8301
No log	19.0	152	0.6452	0.5084	0.6451	0.8032
No log	20.0	160	0.6923	0.3841	0.6926	0.8322
No log	21.0	168	1.2032	0.3793	1.2032	1.0969
No log	22.0	176	0.6019	0.4165	0.6021	0.7760
No log	23.0	184	0.6054	0.4828	0.6056	0.7782
No log	24.0	192	0.7270	0.4801	0.7270	0.8526
No log	25.0	200	0.6068	0.5267	0.6069	0.7790
No log	26.0	208	0.6208	0.5019	0.6207	0.7878
No log	27.0	216	0.6059	0.5597	0.6059	0.7784
No log	28.0	224	0.6477	0.5304	0.6476	0.8047
No log	29.0	232	0.6135	0.5793	0.6135	0.7833
No log	30.0	240	0.6675	0.5582	0.6674	0.8169
No log	31.0	248	0.6878	0.5582	0.6877	0.8293
No log	32.0	256	0.6981	0.5745	0.6980	0.8355
No log	33.0	264	0.8041	0.5591	0.8039	0.8966
No log	34.0	272	0.6771	0.5669	0.6770	0.8228
No log	35.0	280	0.6713	0.5869	0.6709	0.8191
No log	36.0	288	0.6502	0.5821	0.6500	0.8062
No log	37.0	296	0.6719	0.5864	0.6717	0.8196
No log	38.0	304	0.7837	0.5251	0.7833	0.8851
No log	39.0	312	0.6594	0.5934	0.6591	0.8119
No log	40.0	320	0.6610	0.5943	0.6608	0.8129
No log	41.0	328	0.7585	0.5605	0.7582	0.8707
No log	42.0	336	0.7301	0.5850	0.7300	0.8544
No log	43.0	344	0.6926	0.6027	0.6923	0.8321
No log	44.0	352	0.7051	0.6127	0.7047	0.8395
No log	45.0	360	0.7740	0.5845	0.7737	0.8796
No log	46.0	368	0.7670	0.5804	0.7667	0.8756
No log	47.0	376	0.7341	0.5734	0.7338	0.8566
No log	48.0	384	0.7319	0.5717	0.7316	0.8553
No log	49.0	392	0.7715	0.5540	0.7712	0.8782
No log	50.0	400	0.7744	0.5658	0.7741	0.8798
No log	51.0	408	0.7749	0.5911	0.7746	0.8801
No log	52.0	416	0.8117	0.5644	0.8114	0.9008
No log	53.0	424	0.8387	0.5605	0.8384	0.9157
No log	54.0	432	0.7873	0.5764	0.7869	0.8871
No log	55.0	440	0.8458	0.5426	0.8455	0.9195
No log	56.0	448	0.8357	0.5267	0.8353	0.9139
No log	57.0	456	0.7410	0.5852	0.7408	0.8607
No log	58.0	464	0.8736	0.5368	0.8734	0.9346
No log	59.0	472	0.6831	0.5784	0.6828	0.8263
No log	60.0	480	0.7250	0.5885	0.7248	0.8514
No log	61.0	488	0.9347	0.5405	0.9343	0.9666
No log	62.0	496	0.7912	0.5734	0.7909	0.8893
1.7267	63.0	504	0.7649	0.5765	0.7646	0.8744
1.7267	64.0	512	0.7462	0.5757	0.7459	0.8636

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold3

ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold3