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	4.6869	0.0322	4.6869	2.1649
No log	2.0	16	1.7589	0.0590	1.7589	1.3262
No log	3.0	24	1.1649	0.0316	1.1649	1.0793
No log	4.0	32	1.6523	0.0893	1.6523	1.2854
No log	5.0	40	1.2940	0.0316	1.2940	1.1375
No log	6.0	48	1.6372	0.0445	1.6372	1.2795
No log	7.0	56	1.6575	0.1328	1.6575	1.2874
No log	8.0	64	1.0801	0.1265	1.0801	1.0393
No log	9.0	72	0.8441	0.3524	0.8441	0.9188
No log	10.0	80	1.0623	0.3031	1.0623	1.0307
No log	11.0	88	0.8950	0.3839	0.8950	0.9460
No log	12.0	96	0.9102	0.3693	0.9102	0.9540
No log	13.0	104	0.9426	0.4456	0.9426	0.9709
No log	14.0	112	1.1072	0.4136	1.1072	1.0523
No log	15.0	120	1.1245	0.4082	1.1245	1.0604
No log	16.0	128	0.8657	0.4617	0.8657	0.9304
No log	17.0	136	0.9162	0.4825	0.9162	0.9572
No log	18.0	144	0.8191	0.4784	0.8191	0.9050
No log	19.0	152	1.3088	0.3520	1.3088	1.1440
No log	20.0	160	0.8875	0.4678	0.8875	0.9421
No log	21.0	168	0.8179	0.4420	0.8179	0.9044
No log	22.0	176	0.8973	0.4292	0.8973	0.9472
No log	23.0	184	0.7487	0.5047	0.7487	0.8653
No log	24.0	192	0.8770	0.4614	0.8770	0.9365
No log	25.0	200	0.8536	0.4605	0.8536	0.9239
No log	26.0	208	0.7743	0.4912	0.7743	0.8799
No log	27.0	216	0.7554	0.4816	0.7554	0.8691
No log	28.0	224	0.8253	0.4729	0.8253	0.9085
No log	29.0	232	0.7628	0.4847	0.7628	0.8734
No log	30.0	240	0.7629	0.4932	0.7629	0.8734
No log	31.0	248	0.9077	0.4512	0.9077	0.9527
No log	32.0	256	0.8445	0.4729	0.8445	0.9190
No log	33.0	264	0.7408	0.4581	0.7408	0.8607
No log	34.0	272	0.7057	0.5152	0.7057	0.8400
No log	35.0	280	0.6909	0.5273	0.6909	0.8312
No log	36.0	288	0.7082	0.5227	0.7082	0.8415
No log	37.0	296	0.6743	0.5104	0.6743	0.8212
No log	38.0	304	0.8484	0.4611	0.8484	0.9211
No log	39.0	312	0.8366	0.4499	0.8366	0.9147
No log	40.0	320	0.7971	0.4627	0.7971	0.8928
No log	41.0	328	0.7313	0.5019	0.7313	0.8552
No log	42.0	336	0.6783	0.5221	0.6783	0.8236
No log	43.0	344	0.7031	0.4617	0.7031	0.8385
No log	44.0	352	0.7786	0.4455	0.7786	0.8824
No log	45.0	360	0.6861	0.5146	0.6861	0.8283
No log	46.0	368	0.6654	0.5233	0.6654	0.8157
No log	47.0	376	0.6729	0.5294	0.6729	0.8203
No log	48.0	384	0.6360	0.5535	0.6360	0.7975
No log	49.0	392	0.7929	0.4387	0.7929	0.8904
No log	50.0	400	0.7627	0.4782	0.7627	0.8733
No log	51.0	408	0.6472	0.5528	0.6472	0.8045
No log	52.0	416	0.6778	0.5332	0.6778	0.8233
No log	53.0	424	0.7070	0.4891	0.7070	0.8409
No log	54.0	432	0.6930	0.5193	0.6930	0.8324
No log	55.0	440	0.6584	0.5298	0.6584	0.8114
No log	56.0	448	0.6683	0.5453	0.6683	0.8175
No log	57.0	456	0.6724	0.5325	0.6724	0.8200
No log	58.0	464	0.6255	0.5459	0.6255	0.7909
No log	59.0	472	0.6534	0.5454	0.6534	0.8083
No log	60.0	480	0.6768	0.5353	0.6768	0.8227
No log	61.0	488	0.6730	0.5300	0.6730	0.8204
No log	62.0	496	0.7054	0.4764	0.7054	0.8399
0.4611	63.0	504	0.7028	0.4801	0.7028	0.8383
0.4611	64.0	512	0.6814	0.5245	0.6814	0.8255
0.4611	65.0	520	0.6369	0.5519	0.6369	0.7980
0.4611	66.0	528	0.6430	0.5486	0.6430	0.8019
0.4611	67.0	536	0.6796	0.5044	0.6796	0.8244
0.4611	68.0	544	0.6885	0.5031	0.6885	0.8298

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV5_k10_task1_organization_k10_k10_fold4

Version2ASAP_FineTuningBERT_AugV5_k10_task1_organization_k10_k10_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV5_k10_task1_organization_k10_k10_fold4