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	10.0424	0.0083	10.0410	3.1688
No log	2.0	6	7.3090	0.0	7.3079	2.7033
No log	3.0	9	4.4791	0.0175	4.4780	2.1161
No log	4.0	12	3.1130	0.0079	3.1123	1.7642
No log	5.0	15	1.8146	0.1049	1.8137	1.3467
No log	6.0	18	1.3924	0.0539	1.3918	1.1798
No log	7.0	21	1.0323	0.0428	1.0317	1.0157
No log	8.0	24	1.0149	0.1166	1.0145	1.0072
No log	9.0	27	1.4320	0.0155	1.4311	1.1963
No log	10.0	30	0.9300	0.2785	0.9297	0.9642
No log	11.0	33	1.7796	0.1743	1.7792	1.3339
No log	12.0	36	1.2634	0.0432	1.2631	1.1239
No log	13.0	39	1.0687	0.0118	1.0682	1.0335
No log	14.0	42	0.9960	0.0937	0.9958	0.9979
No log	15.0	45	1.0046	0.2618	1.0048	1.0024
No log	16.0	48	0.8007	0.4389	0.8007	0.8948
No log	17.0	51	1.1631	0.3408	1.1633	1.0786
No log	18.0	54	1.0019	0.3392	1.0029	1.0014
No log	19.0	57	1.0814	0.3470	1.0818	1.0401
No log	20.0	60	1.0237	0.3031	1.0249	1.0124
No log	21.0	63	0.9128	0.3629	0.9139	0.9560
No log	22.0	66	0.8064	0.3253	0.8072	0.8984
No log	23.0	69	0.7999	0.4032	0.8008	0.8949
No log	24.0	72	0.8609	0.3833	0.8620	0.9284
No log	25.0	75	0.7430	0.4577	0.7438	0.8624
No log	26.0	78	0.7737	0.4162	0.7745	0.8801
No log	27.0	81	0.8003	0.4478	0.8012	0.8951
No log	28.0	84	0.8574	0.3661	0.8585	0.9266
No log	29.0	87	0.9623	0.4102	0.9636	0.9816
No log	30.0	90	0.8941	0.3550	0.8954	0.9463
No log	31.0	93	0.7417	0.5008	0.7425	0.8617
No log	32.0	96	0.9464	0.3689	0.9479	0.9736
No log	33.0	99	0.8544	0.4870	0.8557	0.9250
No log	34.0	102	0.8067	0.4796	0.8080	0.8989
No log	35.0	105	0.8421	0.3767	0.8433	0.9183
No log	36.0	108	0.7672	0.4684	0.7683	0.8765
No log	37.0	111	0.8306	0.3997	0.8319	0.9121
No log	38.0	114	0.7671	0.5073	0.7681	0.8764
No log	39.0	117	0.8983	0.3878	0.8998	0.9486
No log	40.0	120	0.7894	0.4970	0.7905	0.8891
No log	41.0	123	0.8055	0.4922	0.8067	0.8982
No log	42.0	126	0.8162	0.4537	0.8175	0.9042
No log	43.0	129	0.8142	0.4715	0.8154	0.9030
No log	44.0	132	0.8428	0.4358	0.8442	0.9188
No log	45.0	135	0.8099	0.4422	0.8113	0.9007
No log	46.0	138	0.8619	0.4103	0.8634	0.9292
No log	47.0	141	0.8994	0.4541	0.9008	0.9491
No log	48.0	144	0.8636	0.4248	0.8652	0.9301
No log	49.0	147	0.8365	0.4697	0.8377	0.9153
No log	50.0	150	0.8195	0.4866	0.8208	0.9060
No log	51.0	153	0.9044	0.4189	0.9060	0.9518
No log	52.0	156	0.8132	0.4812	0.8145	0.9025
No log	53.0	159	0.8556	0.4674	0.8570	0.9257
No log	54.0	162	0.8467	0.4786	0.8480	0.9209
No log	55.0	165	0.8450	0.4640	0.8464	0.9200
No log	56.0	168	0.8727	0.4663	0.8742	0.9350
No log	57.0	171	0.9431	0.4650	0.9444	0.9718
No log	58.0	174	0.8715	0.4664	0.8730	0.9343
No log	59.0	177	0.8813	0.4961	0.8828	0.9395
No log	60.0	180	0.9025	0.4559	0.9039	0.9508
No log	61.0	183	0.9537	0.4754	0.9549	0.9772
No log	62.0	186	0.9863	0.4690	0.9876	0.9938
No log	63.0	189	0.9528	0.4933	0.9544	0.9769
No log	64.0	192	0.9518	0.5034	0.9533	0.9764
No log	65.0	195	1.0135	0.4743	1.0147	1.0073
No log	66.0	198	0.9984	0.4433	0.9997	0.9999
No log	67.0	201	0.9954	0.4555	0.9968	0.9984
No log	68.0	204	0.9524	0.4629	0.9539	0.9767

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold3

Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold3