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	3	8.4304	0.0	8.4304	2.9035
No log	2.0	6	6.4087	-0.0012	6.4087	2.5315
No log	3.0	9	4.1812	0.0	4.1812	2.0448
No log	4.0	12	2.9254	0.0	2.9254	1.7104
No log	5.0	15	2.1254	0.0881	2.1254	1.4579
No log	6.0	18	1.5379	0.0449	1.5379	1.2401
No log	7.0	21	1.2266	0.0449	1.2266	1.1075
No log	8.0	24	1.3577	0.0832	1.3577	1.1652
No log	9.0	27	1.2338	0.1088	1.2338	1.1108
No log	10.0	30	1.2369	0.1123	1.2369	1.1121
No log	11.0	33	0.8401	0.3199	0.8401	0.9166
No log	12.0	36	1.5087	0.1960	1.5087	1.2283
No log	13.0	39	0.8752	0.2683	0.8752	0.9355
No log	14.0	42	0.8432	0.4304	0.8432	0.9183
No log	15.0	45	1.0643	0.3353	1.0643	1.0317
No log	16.0	48	0.7843	0.4065	0.7843	0.8856
No log	17.0	51	0.7547	0.4444	0.7547	0.8688
No log	18.0	54	0.7349	0.4086	0.7349	0.8572
No log	19.0	57	0.6920	0.4488	0.6920	0.8319
No log	20.0	60	0.7489	0.4670	0.7489	0.8654
No log	21.0	63	0.7704	0.5131	0.7704	0.8777
No log	22.0	66	0.7151	0.5066	0.7151	0.8456
No log	23.0	69	0.7953	0.5427	0.7953	0.8918
No log	24.0	72	0.7393	0.4300	0.7393	0.8598
No log	25.0	75	0.8110	0.4426	0.8110	0.9005
No log	26.0	78	0.9687	0.3782	0.9687	0.9842
No log	27.0	81	0.8117	0.4646	0.8117	0.9010
No log	28.0	84	0.7794	0.5120	0.7794	0.8828
No log	29.0	87	1.0035	0.4273	1.0035	1.0018
No log	30.0	90	0.7311	0.5599	0.7311	0.8550
No log	31.0	93	1.0642	0.4180	1.0642	1.0316
No log	32.0	96	0.7758	0.5062	0.7758	0.8808
No log	33.0	99	0.9467	0.4063	0.9467	0.9730
No log	34.0	102	0.8081	0.4811	0.8081	0.8990
No log	35.0	105	0.8573	0.4460	0.8573	0.9259
No log	36.0	108	0.7456	0.4757	0.7456	0.8635
No log	37.0	111	0.8085	0.4131	0.8085	0.8992
No log	38.0	114	0.9081	0.4634	0.9081	0.9530
No log	39.0	117	0.7487	0.4623	0.7487	0.8653
No log	40.0	120	0.7190	0.4930	0.7190	0.8480
No log	41.0	123	0.7786	0.4675	0.7786	0.8824
No log	42.0	126	0.8147	0.4978	0.8147	0.9026
No log	43.0	129	0.7630	0.4959	0.7630	0.8735
No log	44.0	132	0.7326	0.5586	0.7326	0.8559
No log	45.0	135	0.7650	0.5275	0.7650	0.8747
No log	46.0	138	0.7613	0.4737	0.7613	0.8725
No log	47.0	141	0.6824	0.5113	0.6824	0.8261
No log	48.0	144	0.7210	0.5035	0.7210	0.8491
No log	49.0	147	0.6769	0.5483	0.6769	0.8227
No log	50.0	150	0.6776	0.5673	0.6776	0.8231
No log	51.0	153	0.6880	0.5543	0.6880	0.8294
No log	52.0	156	0.7524	0.4476	0.7524	0.8674
No log	53.0	159	0.7432	0.4662	0.7432	0.8621
No log	54.0	162	0.7087	0.5126	0.7087	0.8419
No log	55.0	165	0.7002	0.5327	0.7002	0.8368
No log	56.0	168	0.6921	0.5327	0.6921	0.8319
No log	57.0	171	0.7070	0.4702	0.7070	0.8409
No log	58.0	174	0.7096	0.4954	0.7096	0.8424
No log	59.0	177	0.7258	0.4888	0.7258	0.8519
No log	60.0	180	0.8553	0.4520	0.8553	0.9248
No log	61.0	183	0.6959	0.5041	0.6959	0.8342
No log	62.0	186	0.6856	0.4788	0.6856	0.8280
No log	63.0	189	0.7760	0.4837	0.7760	0.8809
No log	64.0	192	0.7160	0.4513	0.7160	0.8462
No log	65.0	195	0.8844	0.4459	0.8844	0.9404
No log	66.0	198	0.8221	0.4950	0.8221	0.9067
No log	67.0	201	0.7447	0.5077	0.7447	0.8630
No log	68.0	204	0.7823	0.4823	0.7823	0.8845
No log	69.0	207	0.7088	0.4895	0.7088	0.8419
No log	70.0	210	0.7235	0.5245	0.7235	0.8506

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12NewTestASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

Version12NewTestASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12NewTestASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4