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	12.5266	-0.0016	12.5266	3.5393
No log	2.0	2	10.7762	0.0034	10.7762	3.2827
No log	3.0	3	9.6854	0.0036	9.6854	3.1121
No log	4.0	4	9.3343	0.0018	9.3343	3.0552
No log	5.0	5	8.5460	0.0	8.5460	2.9234
No log	6.0	6	7.7476	0.0	7.7476	2.7835
No log	7.0	7	7.1962	0.0	7.1962	2.6826
No log	8.0	8	6.8006	0.0	6.8006	2.6078
No log	9.0	9	6.2382	0.0021	6.2382	2.4976
No log	10.0	10	5.8005	0.0156	5.8005	2.4084
No log	11.0	11	5.4824	0.0115	5.4824	2.3415
No log	12.0	12	5.0294	0.0115	5.0294	2.2426
No log	13.0	13	4.5060	0.0115	4.5060	2.1227
No log	14.0	14	4.1128	0.0077	4.1128	2.0280
No log	15.0	15	3.7835	0.0077	3.7835	1.9451
No log	16.0	16	3.4860	0.0077	3.4860	1.8671
No log	17.0	17	3.2364	0.0077	3.2364	1.7990
No log	18.0	18	2.9920	0.0077	2.9920	1.7297
No log	19.0	19	2.7302	0.0077	2.7302	1.6523
No log	20.0	20	2.4850	0.0725	2.4850	1.5764
No log	21.0	21	2.2733	0.0574	2.2733	1.5077
No log	22.0	22	2.1140	0.0419	2.1140	1.4539
No log	23.0	23	1.9553	0.0419	1.9553	1.3983
No log	24.0	24	1.7473	0.0419	1.7473	1.3219
No log	25.0	25	1.6109	0.0419	1.6109	1.2692
No log	26.0	26	1.4883	0.0419	1.4883	1.2200
No log	27.0	27	1.3912	0.0316	1.3912	1.1795
No log	28.0	28	1.2925	0.0316	1.2925	1.1369
No log	29.0	29	1.1620	0.0316	1.1620	1.0780
No log	30.0	30	1.0429	0.0316	1.0429	1.0212
No log	31.0	31	0.9547	0.0419	0.9547	0.9771
No log	32.0	32	0.8755	0.0423	0.8755	0.9357
No log	33.0	33	0.8396	0.2438	0.8396	0.9163
No log	34.0	34	0.7807	0.2943	0.7807	0.8836
No log	35.0	35	0.7142	0.3371	0.7142	0.8451
No log	36.0	36	0.6923	0.4384	0.6923	0.8320
No log	37.0	37	0.6497	0.4209	0.6497	0.8060
No log	38.0	38	0.6231	0.3664	0.6231	0.7894
No log	39.0	39	0.6134	0.3694	0.6134	0.7832
No log	40.0	40	0.5772	0.3998	0.5772	0.7598
No log	41.0	41	0.5549	0.4567	0.5549	0.7449
No log	42.0	42	0.5466	0.4844	0.5466	0.7393
No log	43.0	43	0.5296	0.4672	0.5296	0.7277
No log	44.0	44	0.5507	0.4531	0.5507	0.7421
No log	45.0	45	0.5419	0.4712	0.5419	0.7361
No log	46.0	46	0.5340	0.5123	0.5340	0.7308
No log	47.0	47	0.5740	0.5447	0.5740	0.7577
No log	48.0	48	0.5426	0.5492	0.5426	0.7366
No log	49.0	49	0.5296	0.5646	0.5296	0.7278
No log	50.0	50	0.5794	0.5673	0.5794	0.7612
No log	51.0	51	0.5691	0.5775	0.5691	0.7544
No log	52.0	52	0.5207	0.6081	0.5207	0.7216
No log	53.0	53	0.5113	0.6264	0.5113	0.7151
No log	54.0	54	0.5222	0.6170	0.5222	0.7226
No log	55.0	55	0.5777	0.5874	0.5777	0.7601
No log	56.0	56	0.5685	0.6046	0.5685	0.7540
No log	57.0	57	0.5297	0.6301	0.5297	0.7278
No log	58.0	58	0.5304	0.6427	0.5304	0.7283
No log	59.0	59	0.5535	0.6287	0.5535	0.7440
No log	60.0	60	0.5889	0.6183	0.5889	0.7674
No log	61.0	61	0.5910	0.6232	0.5910	0.7688
No log	62.0	62	0.5610	0.6368	0.5610	0.7490
No log	63.0	63	0.5423	0.6522	0.5423	0.7364
No log	64.0	64	0.5503	0.6450	0.5503	0.7418
No log	65.0	65	0.5987	0.6234	0.5987	0.7738
No log	66.0	66	0.6998	0.5797	0.6998	0.8366
No log	67.0	67	0.6916	0.5756	0.6916	0.8316
No log	68.0	68	0.5905	0.6322	0.5905	0.7684
No log	69.0	69	0.5641	0.6462	0.5641	0.7511
No log	70.0	70	0.5484	0.6419	0.5484	0.7405
No log	71.0	71	0.5712	0.6512	0.5712	0.7558
No log	72.0	72	0.6323	0.6352	0.6323	0.7952
No log	73.0	73	0.6260	0.6365	0.6260	0.7912
No log	74.0	74	0.6376	0.6352	0.6376	0.7985
No log	75.0	75	0.6217	0.6442	0.6217	0.7885
No log	76.0	76	0.5795	0.6579	0.5795	0.7612
No log	77.0	77	0.5684	0.6624	0.5684	0.7539
No log	78.0	78	0.5776	0.6497	0.5776	0.7600
No log	79.0	79	0.6143	0.6533	0.6143	0.7838
No log	80.0	80	0.6492	0.6271	0.6492	0.8058
No log	81.0	81	0.6467	0.6264	0.6467	0.8041
No log	82.0	82	0.6818	0.6048	0.6818	0.8257
No log	83.0	83	0.6572	0.6133	0.6572	0.8107
No log	84.0	84	0.6003	0.6465	0.6003	0.7748
No log	85.0	85	0.5748	0.6396	0.5748	0.7582
No log	86.0	86	0.5771	0.6352	0.5771	0.7597
No log	87.0	87	0.6018	0.6417	0.6018	0.7758
No log	88.0	88	0.6104	0.6449	0.6104	0.7813
No log	89.0	89	0.6104	0.6412	0.6104	0.7813
No log	90.0	90	0.6070	0.6324	0.6070	0.7791
No log	91.0	91	0.6001	0.6323	0.6001	0.7747
No log	92.0	92	0.6038	0.6278	0.6038	0.7770
No log	93.0	93	0.6139	0.6302	0.6139	0.7835
No log	94.0	94	0.6321	0.6408	0.6321	0.7951
No log	95.0	95	0.6344	0.6296	0.6344	0.7965
No log	96.0	96	0.6288	0.6421	0.6288	0.7929
No log	97.0	97	0.6164	0.6357	0.6164	0.7851

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0