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	2	9.6602	0.0018	9.6602	3.1081
No log	2.0	4	7.9627	0.0	7.9627	2.8218
No log	3.0	6	6.5743	0.0	6.5743	2.5640
No log	4.0	8	5.2672	0.0133	5.2672	2.2950
No log	5.0	10	4.3436	0.0040	4.3436	2.0841
No log	6.0	12	3.4737	0.0040	3.4737	1.8638
No log	7.0	14	2.7450	0.0040	2.7450	1.6568
No log	8.0	16	2.1764	0.1580	2.1764	1.4753
No log	9.0	18	1.6635	0.0547	1.6635	1.2898
No log	10.0	20	1.4682	0.0522	1.4682	1.2117
No log	11.0	22	1.3127	0.0522	1.3127	1.1457
No log	12.0	24	1.0197	0.0420	1.0197	1.0098
No log	13.0	26	0.9268	0.0420	0.9268	0.9627
No log	14.0	28	0.8869	0.0905	0.8869	0.9418
No log	15.0	30	1.2397	0.1812	1.2397	1.1134
No log	16.0	32	0.9708	0.2443	0.9708	0.9853
No log	17.0	34	0.8504	0.4045	0.8504	0.9222
No log	18.0	36	1.1208	0.2602	1.1208	1.0587
No log	19.0	38	1.0114	0.3822	1.0114	1.0057
No log	20.0	40	0.7983	0.5057	0.7983	0.8935
No log	21.0	42	0.8543	0.4362	0.8543	0.9243
No log	22.0	44	0.8600	0.4398	0.8600	0.9274
No log	23.0	46	0.8569	0.4412	0.8569	0.9257
No log	24.0	48	1.0835	0.3180	1.0835	1.0409
No log	25.0	50	1.1373	0.3443	1.1373	1.0665
No log	26.0	52	0.7848	0.4535	0.7848	0.8859
No log	27.0	54	0.7203	0.4535	0.7203	0.8487
No log	28.0	56	0.7286	0.4609	0.7286	0.8536
No log	29.0	58	0.7287	0.4775	0.7287	0.8536
No log	30.0	60	0.8950	0.4078	0.8950	0.9461
No log	31.0	62	0.7493	0.5108	0.7493	0.8656
No log	32.0	64	0.8495	0.4741	0.8495	0.9217
No log	33.0	66	1.1040	0.4116	1.1040	1.0507
No log	34.0	68	0.8351	0.5039	0.8351	0.9139
No log	35.0	70	0.9092	0.4976	0.9092	0.9535
No log	36.0	72	1.0137	0.4626	1.0137	1.0068
No log	37.0	74	0.8485	0.5138	0.8485	0.9211
No log	38.0	76	0.8776	0.5092	0.8776	0.9368
No log	39.0	78	0.9387	0.5071	0.9387	0.9689
No log	40.0	80	0.9944	0.4660	0.9944	0.9972
No log	41.0	82	0.9807	0.4815	0.9807	0.9903
No log	42.0	84	0.7161	0.5456	0.7161	0.8462
No log	43.0	86	0.7085	0.5872	0.7085	0.8417
No log	44.0	88	0.8358	0.5189	0.8358	0.9142
No log	45.0	90	0.9410	0.5033	0.9410	0.9701
No log	46.0	92	0.8669	0.5116	0.8669	0.9311
No log	47.0	94	0.8364	0.5255	0.8364	0.9145
No log	48.0	96	0.8397	0.5008	0.8397	0.9164
No log	49.0	98	0.7984	0.5671	0.7984	0.8935
No log	50.0	100	0.8993	0.5376	0.8993	0.9483
No log	51.0	102	0.9314	0.5324	0.9314	0.9651
No log	52.0	104	0.9763	0.5071	0.9763	0.9881
No log	53.0	106	1.0237	0.5364	1.0237	1.0118
No log	54.0	108	1.1659	0.5226	1.1659	1.0798
No log	55.0	110	1.1058	0.5405	1.1058	1.0516
No log	56.0	112	0.8742	0.5540	0.8742	0.9350
No log	57.0	114	0.8172	0.5648	0.8172	0.9040
No log	58.0	116	0.9117	0.5689	0.9117	0.9548
No log	59.0	118	0.8710	0.5703	0.8710	0.9333
No log	60.0	120	0.7504	0.5517	0.7504	0.8663
No log	61.0	122	0.7488	0.5305	0.7488	0.8654
No log	62.0	124	0.8237	0.5672	0.8237	0.9076
No log	63.0	126	1.2083	0.5029	1.2083	1.0992

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV11_k1_task1_organization_k1_k1_fold4

ASAP_FineTuningBERT_AugV11_k1_task1_organization_k1_k1_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV11_k1_task1_organization_k1_k1_fold4