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	1	12.0435	-0.0003	12.0435	3.4704
No log	2.0	2	10.7040	0.0066	10.7040	3.2717
No log	3.0	3	9.7690	0.0018	9.7690	3.1255
No log	4.0	4	9.1236	0.0	9.1236	3.0205
No log	5.0	5	8.7061	0.0	8.7061	2.9506
No log	6.0	6	8.3470	0.0	8.3470	2.8891
No log	7.0	7	7.9300	0.0	7.9300	2.8160
No log	8.0	8	7.4175	0.0	7.4175	2.7235
No log	9.0	9	6.8154	0.0	6.8154	2.6106
No log	10.0	10	6.1717	0.0158	6.1717	2.4843
No log	11.0	11	5.5714	0.0115	5.5714	2.3604
No log	12.0	12	5.0469	0.0115	5.0469	2.2465
No log	13.0	13	4.5843	0.0115	4.5843	2.1411
No log	14.0	14	4.1156	0.0115	4.1156	2.0287
No log	15.0	15	3.6655	0.0115	3.6655	1.9145
No log	16.0	16	3.1832	0.0039	3.1832	1.7842
No log	17.0	17	2.8463	0.0017	2.8463	1.6871
No log	18.0	18	2.6639	0.0152	2.6639	1.6321
No log	19.0	19	2.3088	0.0636	2.3088	1.5195
No log	20.0	20	1.9534	0.0574	1.9534	1.3976
No log	21.0	21	1.7090	0.0344	1.7090	1.3073
No log	22.0	22	1.5218	0.0316	1.5218	1.2336
No log	23.0	23	1.3981	0.0316	1.3981	1.1824
No log	24.0	24	1.1756	0.0316	1.1756	1.0843
No log	25.0	25	1.0415	0.0316	1.0415	1.0205
No log	26.0	26	0.9560	0.0316	0.9560	0.9777
No log	27.0	27	0.9002	0.1176	0.9002	0.9488
No log	28.0	28	0.8756	0.1556	0.8756	0.9357
No log	29.0	29	0.7695	0.3465	0.7695	0.8772
No log	30.0	30	0.7375	0.3689	0.7375	0.8588
No log	31.0	31	0.7328	0.3641	0.7328	0.8561
No log	32.0	32	0.6723	0.3844	0.6723	0.8200
No log	33.0	33	0.6321	0.3855	0.6321	0.7951
No log	34.0	34	0.6131	0.3843	0.6131	0.7830
No log	35.0	35	0.6097	0.4007	0.6097	0.7808
No log	36.0	36	0.5966	0.3957	0.5966	0.7724
No log	37.0	37	0.5582	0.3912	0.5582	0.7471
No log	38.0	38	0.5489	0.4171	0.5489	0.7409
No log	39.0	39	0.5703	0.4310	0.5703	0.7552
No log	40.0	40	0.5416	0.4313	0.5416	0.7359
No log	41.0	41	0.5226	0.4672	0.5226	0.7229
No log	42.0	42	0.5340	0.5076	0.5340	0.7308
No log	43.0	43	0.5799	0.5015	0.5799	0.7615
No log	44.0	44	0.5905	0.5170	0.5905	0.7685
No log	45.0	45	0.5772	0.5649	0.5772	0.7597
No log	46.0	46	0.5680	0.5735	0.5680	0.7537
No log	47.0	47	0.6285	0.5433	0.6285	0.7928
No log	48.0	48	0.5994	0.5828	0.5994	0.7742
No log	49.0	49	0.5800	0.6084	0.5800	0.7616
No log	50.0	50	0.6616	0.5489	0.6616	0.8134
No log	51.0	51	0.7505	0.5098	0.7505	0.8663
No log	52.0	52	0.6483	0.5561	0.6483	0.8052
No log	53.0	53	0.5771	0.6112	0.5771	0.7596
No log	54.0	54	0.6037	0.5846	0.6037	0.7770
No log	55.0	55	0.7942	0.4972	0.7942	0.8912
No log	56.0	56	0.7788	0.5070	0.7788	0.8825
No log	57.0	57	0.6336	0.5726	0.6336	0.7960
No log	58.0	58	0.6317	0.5713	0.6317	0.7948
No log	59.0	59	0.7233	0.5418	0.7233	0.8505
No log	60.0	60	0.8315	0.4975	0.8315	0.9119
No log	61.0	61	0.7543	0.5470	0.7543	0.8685
No log	62.0	62	0.6318	0.5843	0.6318	0.7949
No log	63.0	63	0.6254	0.5794	0.6254	0.7908
No log	64.0	64	0.6745	0.5541	0.6745	0.8213
No log	65.0	65	0.7049	0.5385	0.7049	0.8396
No log	66.0	66	0.7261	0.5382	0.7261	0.8521
No log	67.0	67	0.6982	0.5372	0.6982	0.8356
No log	68.0	68	0.6361	0.5525	0.6361	0.7976
No log	69.0	69	0.6132	0.5710	0.6132	0.7830
No log	70.0	70	0.6371	0.5447	0.6371	0.7982
No log	71.0	71	0.7448	0.5104	0.7448	0.8630
No log	72.0	72	0.7907	0.5014	0.7907	0.8892
No log	73.0	73	0.7364	0.5252	0.7364	0.8582

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version_weird_ASAP_FineTuningBERT_AugV12_k2_task1_organization_k2_k2_fold0

Version_weird_ASAP_FineTuningBERT_AugV12_k2_task1_organization_k2_k2_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version_weird_ASAP_FineTuningBERT_AugV12_k2_task1_organization_k2_k2_fold0