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	8.5734	0.0018	8.5734	2.9280
No log	2.0	6	5.5059	0.0371	5.5059	2.3465
No log	3.0	9	3.9402	0.0118	3.9402	1.9850
No log	4.0	12	2.8701	0.0040	2.8701	1.6941
No log	5.0	15	2.0250	0.1160	2.0250	1.4230
No log	6.0	18	1.5058	0.0495	1.5058	1.2271
No log	7.0	21	1.0882	0.0420	1.0882	1.0432
No log	8.0	24	0.8362	0.4076	0.8362	0.9145
No log	9.0	27	0.8468	0.1142	0.8468	0.9202
No log	10.0	30	0.7452	0.1745	0.7452	0.8633
No log	11.0	33	0.9677	0.0747	0.9677	0.9837
No log	12.0	36	1.1230	0.1514	1.1230	1.0597
No log	13.0	39	0.7502	0.3094	0.7502	0.8661
No log	14.0	42	1.1457	0.3198	1.1457	1.0704
No log	15.0	45	1.1815	0.3527	1.1815	1.0870
No log	16.0	48	0.5564	0.5872	0.5564	0.7459
No log	17.0	51	0.8379	0.5608	0.8379	0.9153
No log	18.0	54	0.7107	0.6195	0.7107	0.8430
No log	19.0	57	0.7797	0.6242	0.7797	0.8830
No log	20.0	60	0.6984	0.6316	0.6984	0.8357
No log	21.0	63	1.5348	0.4628	1.5348	1.2389
No log	22.0	66	1.1403	0.5284	1.1403	1.0678
No log	23.0	69	0.6395	0.6114	0.6395	0.7997
No log	24.0	72	0.8806	0.5788	0.8806	0.9384
No log	25.0	75	0.8179	0.6140	0.8179	0.9044
No log	26.0	78	0.7619	0.6328	0.7619	0.8729
No log	27.0	81	0.9773	0.5913	0.9773	0.9886
No log	28.0	84	0.7499	0.6343	0.7499	0.8660
No log	29.0	87	0.7548	0.6327	0.7548	0.8688
No log	30.0	90	0.7347	0.6415	0.7347	0.8572
No log	31.0	93	0.7359	0.6440	0.7359	0.8579
No log	32.0	96	0.9402	0.6091	0.9402	0.9697
No log	33.0	99	0.7283	0.6331	0.7283	0.8534
No log	34.0	102	0.9106	0.6077	0.9106	0.9543
No log	35.0	105	0.8465	0.6205	0.8465	0.9201
No log	36.0	108	1.0526	0.5623	1.0526	1.0260
No log	37.0	111	1.0934	0.5529	1.0934	1.0456
No log	38.0	114	0.6704	0.6522	0.6704	0.8188
No log	39.0	117	0.7281	0.6566	0.7281	0.8533
No log	40.0	120	0.9367	0.5804	0.9367	0.9678
No log	41.0	123	0.7010	0.6301	0.7010	0.8373
No log	42.0	126	0.8696	0.6008	0.8696	0.9325
No log	43.0	129	1.0117	0.5701	1.0117	1.0058
No log	44.0	132	0.9463	0.5807	0.9463	0.9728
No log	45.0	135	0.8021	0.6177	0.8021	0.8956
No log	46.0	138	1.2901	0.4964	1.2901	1.1358
No log	47.0	141	1.2888	0.5096	1.2888	1.1353
No log	48.0	144	0.6793	0.6502	0.6793	0.8242
No log	49.0	147	0.6277	0.6577	0.6277	0.7923
No log	50.0	150	1.1580	0.5417	1.1580	1.0761
No log	51.0	153	1.2279	0.5237	1.2279	1.1081
No log	52.0	156	0.6763	0.6488	0.6763	0.8224
No log	53.0	159	0.6863	0.6415	0.6863	0.8284
No log	54.0	162	0.9080	0.5888	0.9080	0.9529
No log	55.0	165	0.8864	0.6013	0.8864	0.9415
No log	56.0	168	0.9642	0.5742	0.9642	0.9820
No log	57.0	171	0.7972	0.6267	0.7972	0.8929
No log	58.0	174	0.8188	0.6452	0.8188	0.9049
No log	59.0	177	0.8288	0.6305	0.8288	0.9104
No log	60.0	180	0.6615	0.6458	0.6615	0.8133
No log	61.0	183	0.6544	0.6364	0.6544	0.8089
No log	62.0	186	0.9539	0.5733	0.9539	0.9767
No log	63.0	189	0.9674	0.5830	0.9674	0.9836
No log	64.0	192	0.7201	0.6455	0.7201	0.8486
No log	65.0	195	0.6594	0.6601	0.6594	0.8121
No log	66.0	198	0.7223	0.6406	0.7223	0.8499
No log	67.0	201	0.7865	0.6267	0.7865	0.8868
No log	68.0	204	0.6781	0.6311	0.6781	0.8235
No log	69.0	207	0.6995	0.6114	0.6995	0.8363
No log	70.0	210	0.9504	0.5780	0.9504	0.9749
No log	71.0	213	1.0010	0.5708	1.0010	1.0005
No log	72.0	216	0.8101	0.6072	0.8101	0.9001
No log	73.0	219	0.6846	0.6416	0.6846	0.8274
No log	74.0	222	0.7002	0.6364	0.7002	0.8368
No log	75.0	225	0.8613	0.6137	0.8613	0.9281
No log	76.0	228	0.9333	0.5952	0.9333	0.9661
No log	77.0	231	0.8303	0.6106	0.8303	0.9112
No log	78.0	234	0.7443	0.6462	0.7443	0.8627
No log	79.0	237	0.8306	0.6247	0.8306	0.9114
No log	80.0	240	0.8564	0.6152	0.8564	0.9254
No log	81.0	243	0.7686	0.6293	0.7686	0.8767
No log	82.0	246	0.7660	0.6187	0.7660	0.8752
No log	83.0	249	0.7919	0.6212	0.7919	0.8899
No log	84.0	252	0.8039	0.6209	0.8039	0.8966
No log	85.0	255	0.8394	0.6091	0.8394	0.9162

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version3ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold4

Version3ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version3ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold4