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	2	10.5348	0.0018	10.5348	3.2457
No log	2.0	4	9.2422	0.0	9.2424	3.0401
No log	3.0	6	8.1097	0.0	8.1100	2.8478
No log	4.0	8	6.5013	0.0015	6.5016	2.5498
No log	5.0	10	5.1228	0.0274	5.1231	2.2634
No log	6.0	12	4.0575	0.0	4.0577	2.0144
No log	7.0	14	3.2527	0.0	3.2529	1.8036
No log	8.0	16	2.2773	0.1937	2.2777	1.5092
No log	9.0	18	1.7284	0.1110	1.7287	1.3148
No log	10.0	20	1.1967	0.0551	1.1971	1.0941
No log	11.0	22	0.9793	0.0384	0.9797	0.9898
No log	12.0	24	0.8720	0.0762	0.8723	0.9340
No log	13.0	26	0.6792	0.4406	0.6795	0.8243
No log	14.0	28	0.7016	0.4023	0.7017	0.8377
No log	15.0	30	0.8818	0.2918	0.8819	0.9391
No log	16.0	32	0.9326	0.3415	0.9327	0.9657
No log	17.0	34	0.6989	0.4592	0.6990	0.8360
No log	18.0	36	0.6964	0.4667	0.6965	0.8345
No log	19.0	38	0.8852	0.3942	0.8852	0.9408
No log	20.0	40	0.7876	0.4451	0.7876	0.8874
No log	21.0	42	0.6431	0.4711	0.6431	0.8019
No log	22.0	44	0.8530	0.3980	0.8531	0.9236
No log	23.0	46	1.0561	0.3385	1.0560	1.0276
No log	24.0	48	0.5933	0.5162	0.5932	0.7702
No log	25.0	50	0.5803	0.5248	0.5800	0.7616
No log	26.0	52	0.9393	0.3964	0.9389	0.9690
No log	27.0	54	1.1635	0.3223	1.1631	1.0785
No log	28.0	56	0.9156	0.4406	0.9151	0.9566
No log	29.0	58	0.8284	0.4809	0.8278	0.9098
No log	30.0	60	0.9914	0.4683	0.9904	0.9952
No log	31.0	62	1.1237	0.4226	1.1227	1.0596
No log	32.0	64	0.9845	0.4339	0.9836	0.9918
No log	33.0	66	0.9994	0.4257	0.9987	0.9993
No log	34.0	68	1.1391	0.3483	1.1385	1.0670
No log	35.0	70	1.1842	0.3488	1.1836	1.0879
No log	36.0	72	1.0360	0.4050	1.0355	1.0176
No log	37.0	74	0.9930	0.4328	0.9924	0.9962
No log	38.0	76	0.9465	0.4524	0.9459	0.9726
No log	39.0	78	0.8480	0.4793	0.8473	0.9205
No log	40.0	80	1.0340	0.4319	1.0333	1.0165
No log	41.0	82	1.0455	0.4149	1.0448	1.0221
No log	42.0	84	0.8110	0.4955	0.8104	0.9002
No log	43.0	86	0.6992	0.5316	0.6986	0.8358
No log	44.0	88	0.7626	0.5016	0.7620	0.8730
No log	45.0	90	0.8901	0.4584	0.8895	0.9431
No log	46.0	92	0.7767	0.4906	0.7761	0.8810
No log	47.0	94	0.7299	0.5122	0.7294	0.8540
No log	48.0	96	0.7389	0.5163	0.7384	0.8593
No log	49.0	98	0.8029	0.5191	0.8022	0.8956
No log	50.0	100	0.8958	0.4721	0.8951	0.9461
No log	51.0	102	0.8641	0.4828	0.8634	0.9292
No log	52.0	104	0.7681	0.5167	0.7675	0.8761
No log	53.0	106	0.7203	0.5244	0.7198	0.8484
No log	54.0	108	0.7583	0.4897	0.7578	0.8705
No log	55.0	110	0.7445	0.5118	0.7439	0.8625
No log	56.0	112	0.7536	0.5217	0.7530	0.8677
No log	57.0	114	0.8118	0.4835	0.8112	0.9007
No log	58.0	116	0.9055	0.4451	0.9049	0.9513
No log	59.0	118	0.8844	0.4819	0.8837	0.9400
No log	60.0	120	0.7808	0.5196	0.7802	0.8833
No log	61.0	122	0.7605	0.5094	0.7599	0.8717
No log	62.0	124	0.8414	0.4856	0.8408	0.9170
No log	63.0	126	0.8820	0.4697	0.8814	0.9388

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2

ASAP_nosemanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_nosemanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2