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	10.6609	0.0169	10.6609	3.2651
No log	2.0	2	9.5403	0.0018	9.5403	3.0887
No log	3.0	3	9.2323	0.0	9.2323	3.0385
No log	4.0	4	9.0575	0.0	9.0575	3.0096
No log	5.0	5	8.9028	0.0	8.9028	2.9838
No log	6.0	6	8.7534	0.0	8.7534	2.9586
No log	7.0	7	8.6044	0.0	8.6044	2.9333
No log	8.0	8	8.4499	0.0	8.4499	2.9069
No log	9.0	9	8.2843	0.0	8.2843	2.8782
No log	10.0	10	8.0945	0.0	8.0945	2.8451
No log	11.0	11	7.8656	0.0	7.8656	2.8046
No log	12.0	12	7.5693	0.0	7.5693	2.7512
No log	13.0	13	7.1797	0.0	7.1797	2.6795
No log	14.0	14	6.7520	0.0	6.7520	2.5985
No log	15.0	15	6.4109	0.0	6.4109	2.5320
No log	16.0	16	6.0832	0.0145	6.0832	2.4664
No log	17.0	17	5.6733	0.0115	5.6733	2.3819
No log	18.0	18	5.2570	0.0077	5.2570	2.2928
No log	19.0	19	4.8896	0.0	4.8896	2.2112
No log	20.0	20	4.5565	0.0	4.5565	2.1346
No log	21.0	21	4.2415	0.0	4.2415	2.0595
No log	22.0	22	3.9419	0.0	3.9419	1.9854
No log	23.0	23	3.6545	0.0	3.6545	1.9117
No log	24.0	24	3.3747	0.0	3.3747	1.8370
No log	25.0	25	3.0997	0.0	3.0997	1.7606
No log	26.0	26	2.8513	0.0	2.8513	1.6886
No log	27.0	27	2.6156	0.0	2.6156	1.6173
No log	28.0	28	2.4094	0.1230	2.4094	1.5522
No log	29.0	29	2.2031	0.0740	2.2031	1.4843
No log	30.0	30	2.0034	0.0447	2.0034	1.4154
No log	31.0	31	1.8346	0.0419	1.8346	1.3545
No log	32.0	32	1.6693	0.0419	1.6693	1.2920
No log	33.0	33	1.5376	0.0316	1.5376	1.2400
No log	34.0	34	1.4085	0.0316	1.4085	1.1868
No log	35.0	35	1.3605	0.0316	1.3605	1.1664
No log	36.0	36	1.3458	0.0316	1.3458	1.1601
No log	37.0	37	1.2358	0.0316	1.2358	1.1116
No log	38.0	38	1.0649	0.0316	1.0649	1.0320
No log	39.0	39	0.9823	0.0316	0.9823	0.9911
No log	40.0	40	0.9299	0.0316	0.9299	0.9643
No log	41.0	41	0.8518	0.1156	0.8518	0.9229
No log	42.0	42	0.8359	0.2607	0.8359	0.9143
No log	43.0	43	0.8618	0.1818	0.8618	0.9283
No log	44.0	44	0.8088	0.3265	0.8088	0.8993
No log	45.0	45	0.7121	0.4198	0.7121	0.8439
No log	46.0	46	0.6575	0.3736	0.6575	0.8109
No log	47.0	47	0.6594	0.3291	0.6594	0.8120
No log	48.0	48	0.6242	0.3932	0.6242	0.7901
No log	49.0	49	0.5820	0.4321	0.5820	0.7629
No log	50.0	50	0.5877	0.4579	0.5877	0.7666
No log	51.0	51	0.5864	0.4883	0.5864	0.7657
No log	52.0	52	0.5566	0.4564	0.5566	0.7461
No log	53.0	53	0.5394	0.4726	0.5394	0.7344
No log	54.0	54	0.5586	0.4384	0.5586	0.7474
No log	55.0	55	0.5826	0.4255	0.5826	0.7633
No log	56.0	56	0.5611	0.4514	0.5611	0.7491
No log	57.0	57	0.5064	0.4789	0.5064	0.7116
No log	58.0	58	0.4943	0.5175	0.4943	0.7030
No log	59.0	59	0.5027	0.5602	0.5027	0.7090
No log	60.0	60	0.5740	0.5792	0.5740	0.7576
No log	61.0	61	0.6301	0.5579	0.6301	0.7938
No log	62.0	62	0.6213	0.5699	0.6213	0.7882
No log	63.0	63	0.5343	0.6310	0.5343	0.7310
No log	64.0	64	0.4875	0.6130	0.4875	0.6982
No log	65.0	65	0.5085	0.6302	0.5085	0.7131
No log	66.0	66	0.4962	0.6142	0.4962	0.7044
No log	67.0	67	0.5177	0.6365	0.5177	0.7195
No log	68.0	68	0.6624	0.5576	0.6624	0.8138
No log	69.0	69	0.7591	0.5106	0.7591	0.8713
No log	70.0	70	0.7474	0.5154	0.7474	0.8645
No log	71.0	71	0.6447	0.5696	0.6447	0.8029
No log	72.0	72	0.5753	0.5929	0.5753	0.7585
No log	73.0	73	0.5554	0.5990	0.5554	0.7452
No log	74.0	74	0.5359	0.6237	0.5359	0.7320
No log	75.0	75	0.5559	0.6051	0.5559	0.7456
No log	76.0	76	0.5951	0.5953	0.5951	0.7714
No log	77.0	77	0.6799	0.5632	0.6799	0.8246
No log	78.0	78	0.7617	0.5327	0.7617	0.8727
No log	79.0	79	0.7716	0.5286	0.7716	0.8784
No log	80.0	80	0.7109	0.5570	0.7109	0.8432
No log	81.0	81	0.6231	0.5986	0.6231	0.7893
No log	82.0	82	0.5695	0.6331	0.5695	0.7547
No log	83.0	83	0.5617	0.6270	0.5617	0.7495
No log	84.0	84	0.5737	0.6240	0.5737	0.7574
No log	85.0	85	0.6167	0.5964	0.6167	0.7853
No log	86.0	86	0.6556	0.5835	0.6556	0.8097
No log	87.0	87	0.6624	0.5832	0.6624	0.8139
No log	88.0	88	0.6504	0.5832	0.6504	0.8065
No log	89.0	89	0.6479	0.5896	0.6479	0.8049
No log	90.0	90	0.6681	0.5731	0.6681	0.8174
No log	91.0	91	0.6918	0.5723	0.6918	0.8318
No log	92.0	92	0.6864	0.5672	0.6864	0.8285
No log	93.0	93	0.6804	0.5716	0.6804	0.8248
No log	94.0	94	0.6790	0.5696	0.6790	0.8240
No log	95.0	95	0.6718	0.5748	0.6718	0.8196
No log	96.0	96	0.6692	0.5762	0.6692	0.8180
No log	97.0	97	0.6687	0.5750	0.6687	0.8178

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k1_task1_organization_k1_k1_fold0

Version22NewTestASAP_FineTuningBERT_AugV22_k1_task1_organization_k1_k1_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version22NewTestASAP_FineTuningBERT_AugV22_k1_task1_organization_k1_k1_fold0