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	10.3272	0.0	10.3252	3.2133
No log	2.0	6	9.2713	0.0	9.2694	3.0446
No log	3.0	9	8.0387	0.0	8.0370	2.8350
No log	4.0	12	6.9044	0.0	6.9029	2.6273
No log	5.0	15	5.5745	0.0076	5.5731	2.3607
No log	6.0	18	4.0418	0.0	4.0407	2.0101
No log	7.0	21	2.9706	0.0	2.9696	1.7232
No log	8.0	24	2.1406	0.0585	2.1398	1.4628
No log	9.0	27	1.4988	0.0	1.4981	1.2240
No log	10.0	30	1.1221	0.0	1.1215	1.0590
No log	11.0	33	0.9144	0.3194	0.9140	0.9560
No log	12.0	36	0.8340	0.0613	0.8336	0.9130
No log	13.0	39	0.8802	0.0613	0.8799	0.9380
No log	14.0	42	1.1260	0.0764	1.1258	1.0610
No log	15.0	45	1.2944	0.1960	1.2943	1.1377
No log	16.0	48	1.6083	0.2018	1.6086	1.2683
No log	17.0	51	1.2594	0.3621	1.2592	1.1221
No log	18.0	54	0.9600	0.5025	0.9593	0.9794
No log	19.0	57	0.6664	0.5908	0.6659	0.8160
No log	20.0	60	0.7861	0.5834	0.7855	0.8863
No log	21.0	63	0.6522	0.5970	0.6520	0.8075
No log	22.0	66	1.0967	0.5161	1.0959	1.0468
No log	23.0	69	0.5413	0.6093	0.5411	0.7356
No log	24.0	72	1.3634	0.4404	1.3626	1.1673
No log	25.0	75	0.9239	0.5165	0.9232	0.9608
No log	26.0	78	0.4963	0.6276	0.4959	0.7042
No log	27.0	81	1.1986	0.4710	1.1977	1.0944
No log	28.0	84	0.5751	0.6464	0.5747	0.7581
No log	29.0	87	0.6217	0.6560	0.6213	0.7882
No log	30.0	90	1.0411	0.5461	1.0403	1.0199
No log	31.0	93	0.6452	0.5874	0.6449	0.8031
No log	32.0	96	0.7164	0.6404	0.7158	0.8461
No log	33.0	99	0.6292	0.6244	0.6287	0.7929
No log	34.0	102	0.7283	0.5973	0.7276	0.8530
No log	35.0	105	0.7369	0.5895	0.7364	0.8581
No log	36.0	108	0.6729	0.6322	0.6723	0.8199
No log	37.0	111	0.6662	0.6190	0.6656	0.8159
No log	38.0	114	0.6036	0.6434	0.6030	0.7765
No log	39.0	117	0.8146	0.5354	0.8140	0.9022
No log	40.0	120	0.7062	0.6017	0.7056	0.8400
No log	41.0	123	0.6398	0.6260	0.6394	0.7996
No log	42.0	126	0.7037	0.6358	0.7031	0.8385
No log	43.0	129	0.7850	0.6080	0.7844	0.8857
No log	44.0	132	0.7473	0.6083	0.7467	0.8641
No log	45.0	135	0.7057	0.6213	0.7051	0.8397
No log	46.0	138	0.6081	0.6633	0.6076	0.7795
No log	47.0	141	0.7094	0.6313	0.7088	0.8419
No log	48.0	144	0.6830	0.6356	0.6823	0.8260
No log	49.0	147	0.6580	0.6322	0.6573	0.8107
No log	50.0	150	0.6223	0.6492	0.6217	0.7885
No log	51.0	153	0.8508	0.5930	0.8500	0.9220
No log	52.0	156	0.7068	0.6279	0.7062	0.8404
No log	53.0	159	0.7785	0.6040	0.7778	0.8819
No log	54.0	162	0.8118	0.5892	0.8112	0.9006
No log	55.0	165	0.6265	0.6526	0.6260	0.7912
No log	56.0	168	0.7657	0.6246	0.7650	0.8746
No log	57.0	171	0.7164	0.6415	0.7157	0.8460
No log	58.0	174	0.6141	0.6545	0.6135	0.7833
No log	59.0	177	0.7698	0.5998	0.7690	0.8769
No log	60.0	180	0.7296	0.6046	0.7289	0.8537
No log	61.0	183	0.6378	0.6512	0.6371	0.7982
No log	62.0	186	0.6411	0.6570	0.6404	0.8003
No log	63.0	189	0.6402	0.6476	0.6396	0.7997
No log	64.0	192	0.6789	0.6425	0.6782	0.8235
No log	65.0	195	0.6145	0.6489	0.6138	0.7835
No log	66.0	198	0.6562	0.6391	0.6555	0.8096

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold3

Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold3