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	4	6.2115	0.0	6.2093	2.4918
No log	2.0	8	4.3574	0.0	4.3553	2.0869
No log	3.0	12	2.5941	0.0	2.5922	1.6100
No log	4.0	16	1.5189	0.0315	1.5173	1.2318
No log	5.0	20	1.1183	0.0	1.1170	1.0569
No log	6.0	24	1.2076	0.0	1.2062	1.0983
No log	7.0	28	1.2845	0.0	1.2830	1.1327
No log	8.0	32	1.1712	0.0156	1.1703	1.0818
No log	9.0	36	0.8828	0.1870	0.8815	0.9389
No log	10.0	40	0.9408	0.2280	0.9402	0.9696
No log	11.0	44	0.8995	0.1932	0.8988	0.9480
No log	12.0	48	0.7488	0.2634	0.7478	0.8648
No log	13.0	52	0.8462	0.2108	0.8453	0.9194
No log	14.0	56	0.9618	0.1748	0.9612	0.9804
No log	15.0	60	1.1477	0.1729	1.1473	1.0711
No log	16.0	64	0.7169	0.3382	0.7161	0.8462
No log	17.0	68	0.8301	0.3126	0.8296	0.9108
No log	18.0	72	0.9996	0.2640	0.9991	0.9996
No log	19.0	76	0.9402	0.2943	0.9398	0.9694
No log	20.0	80	0.8745	0.3628	0.8741	0.9350
No log	21.0	84	0.9318	0.3653	0.9317	0.9652
No log	22.0	88	0.8051	0.4318	0.8049	0.8972
No log	23.0	92	0.6969	0.4930	0.6967	0.8347
No log	24.0	96	1.1081	0.2852	1.1078	1.0525
No log	25.0	100	1.0429	0.2547	1.0424	1.0210
No log	26.0	104	0.8506	0.3829	0.8502	0.9221
No log	27.0	108	0.9551	0.3471	0.9548	0.9772
No log	28.0	112	0.8642	0.4167	0.8641	0.9296
No log	29.0	116	0.9174	0.4101	0.9173	0.9578
No log	30.0	120	0.8584	0.4036	0.8582	0.9264
No log	31.0	124	0.8051	0.4852	0.8052	0.8973
No log	32.0	128	0.9928	0.4063	0.9930	0.9965
No log	33.0	132	0.8587	0.4494	0.8589	0.9267
No log	34.0	136	0.7556	0.5168	0.7556	0.8693
No log	35.0	140	0.9405	0.3486	0.9403	0.9697
No log	36.0	144	0.7997	0.4845	0.7996	0.8942
No log	37.0	148	0.7923	0.5031	0.7922	0.8901
No log	38.0	152	0.7670	0.4717	0.7668	0.8757
No log	39.0	156	0.7621	0.4492	0.7618	0.8728
No log	40.0	160	0.8152	0.4152	0.8149	0.9027
No log	41.0	164	0.7852	0.4534	0.7851	0.8860
No log	42.0	168	0.8545	0.3938	0.8543	0.9243
No log	43.0	172	0.8256	0.4370	0.8256	0.9086
No log	44.0	176	0.8011	0.4578	0.8009	0.8949
No log	45.0	180	0.8384	0.4166	0.8380	0.9154
No log	46.0	184	0.8824	0.3783	0.8821	0.9392
No log	47.0	188	0.7462	0.5225	0.7461	0.8638
No log	48.0	192	0.8059	0.4925	0.8059	0.8977
No log	49.0	196	0.8828	0.4080	0.8828	0.9396
No log	50.0	200	0.8419	0.4361	0.8419	0.9175
No log	51.0	204	0.8867	0.4356	0.8870	0.9418
No log	52.0	208	0.9004	0.4677	0.9009	0.9491
No log	53.0	212	0.8500	0.4810	0.8504	0.9222
No log	54.0	216	0.8350	0.4683	0.8352	0.9139
No log	55.0	220	0.9327	0.3981	0.9327	0.9658
No log	56.0	224	0.9636	0.3740	0.9635	0.9816
No log	57.0	228	0.9008	0.4461	0.9010	0.9492
No log	58.0	232	0.8533	0.4638	0.8535	0.9239
No log	59.0	236	0.8388	0.4495	0.8390	0.9160
No log	60.0	240	0.9562	0.4108	0.9563	0.9779
No log	61.0	244	0.8901	0.4170	0.8902	0.9435
No log	62.0	248	0.8108	0.4293	0.8108	0.9004
No log	63.0	252	0.8901	0.4413	0.8904	0.9436
No log	64.0	256	0.8771	0.4240	0.8773	0.9367
No log	65.0	260	0.7550	0.5215	0.7551	0.8690
No log	66.0	264	0.9154	0.3895	0.9155	0.9568
No log	67.0	268	0.8717	0.4068	0.8717	0.9336

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV9_k5_task1_organization_k5_k5_fold1

ASAP_FineTuningBERT_AugV9_k5_task1_organization_k5_k5_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV9_k5_task1_organization_k5_k5_fold1