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	3	8.0786	0.0	8.0786	2.8423
No log	2.0	6	6.7947	0.0	6.7947	2.6067
No log	3.0	9	5.4864	0.0112	5.4864	2.3423
No log	4.0	12	4.2698	0.0039	4.2698	2.0663
No log	5.0	15	3.1079	0.0	3.1079	1.7629
No log	6.0	18	2.1363	0.0627	2.1363	1.4616
No log	7.0	21	1.5514	0.0316	1.5514	1.2455
No log	8.0	24	1.6098	0.0601	1.6098	1.2688
No log	9.0	27	1.1764	0.0484	1.1764	1.0846
No log	10.0	30	1.4033	0.1229	1.4033	1.1846
No log	11.0	33	1.3772	0.1474	1.3772	1.1735
No log	12.0	36	1.0826	0.1173	1.0826	1.0405
No log	13.0	39	0.9002	0.2422	0.9002	0.9488
No log	14.0	42	1.1396	0.1756	1.1396	1.0675
No log	15.0	45	0.7428	0.2299	0.7428	0.8619
No log	16.0	48	0.7305	0.3521	0.7305	0.8547
No log	17.0	51	0.7182	0.3433	0.7182	0.8475
No log	18.0	54	0.7450	0.3446	0.7450	0.8631
No log	19.0	57	0.7241	0.3632	0.7241	0.8509
No log	20.0	60	0.8364	0.3227	0.8364	0.9145
No log	21.0	63	0.8534	0.3173	0.8534	0.9238
No log	22.0	66	0.9537	0.2844	0.9537	0.9766
No log	23.0	69	0.9119	0.3049	0.9119	0.9549
No log	24.0	72	0.7791	0.4123	0.7791	0.8827
No log	25.0	75	0.9546	0.3029	0.9546	0.9770
No log	26.0	78	0.8774	0.3444	0.8774	0.9367
No log	27.0	81	0.8827	0.3527	0.8827	0.9395
No log	28.0	84	0.7725	0.4058	0.7725	0.8789
No log	29.0	87	0.7055	0.4658	0.7055	0.8399
No log	30.0	90	0.7151	0.4616	0.7151	0.8456
No log	31.0	93	0.6922	0.4380	0.6922	0.8320
No log	32.0	96	0.7366	0.4255	0.7366	0.8583
No log	33.0	99	0.7520	0.4512	0.7520	0.8672
No log	34.0	102	0.7040	0.4473	0.7040	0.8391
No log	35.0	105	0.7275	0.4349	0.7275	0.8529
No log	36.0	108	0.7549	0.4291	0.7549	0.8688
No log	37.0	111	0.7311	0.4738	0.7311	0.8550
No log	38.0	114	0.6969	0.4772	0.6969	0.8348
No log	39.0	117	0.7125	0.4574	0.7125	0.8441
No log	40.0	120	0.6651	0.4917	0.6651	0.8156
No log	41.0	123	0.6934	0.4744	0.6934	0.8327
No log	42.0	126	0.7938	0.4143	0.7938	0.8909
No log	43.0	129	0.7793	0.4520	0.7793	0.8828
No log	44.0	132	0.7247	0.4625	0.7247	0.8513
No log	45.0	135	0.7244	0.4467	0.7244	0.8511
No log	46.0	138	0.6831	0.4677	0.6831	0.8265
No log	47.0	141	0.6809	0.4903	0.6809	0.8252
No log	48.0	144	0.7496	0.4557	0.7496	0.8658
No log	49.0	147	0.7997	0.4363	0.7997	0.8943
No log	50.0	150	0.7688	0.4498	0.7688	0.8768
No log	51.0	153	0.7152	0.4640	0.7152	0.8457
No log	52.0	156	0.6881	0.4976	0.6881	0.8295
No log	53.0	159	0.7120	0.4713	0.7120	0.8438
No log	54.0	162	0.7267	0.4826	0.7267	0.8525
No log	55.0	165	0.7123	0.4666	0.7123	0.8440
No log	56.0	168	0.7040	0.4609	0.7040	0.8390
No log	57.0	171	0.6831	0.4894	0.6831	0.8265
No log	58.0	174	0.6835	0.4789	0.6835	0.8267
No log	59.0	177	0.7130	0.4700	0.7130	0.8444
No log	60.0	180	0.7098	0.4994	0.7098	0.8425
No log	61.0	183	0.6860	0.4928	0.6860	0.8283
No log	62.0	186	0.6841	0.4731	0.6841	0.8271
No log	63.0	189	0.6602	0.4880	0.6602	0.8125
No log	64.0	192	0.6542	0.4912	0.6542	0.8088
No log	65.0	195	0.6700	0.5024	0.6700	0.8186
No log	66.0	198	0.7219	0.4704	0.7219	0.8497
No log	67.0	201	0.7143	0.4656	0.7143	0.8452
No log	68.0	204	0.6997	0.4788	0.6997	0.8365
No log	69.0	207	0.6871	0.4704	0.6871	0.8289
No log	70.0	210	0.6965	0.4903	0.6965	0.8345
No log	71.0	213	0.7363	0.4683	0.7363	0.8581
No log	72.0	216	0.7036	0.4951	0.7036	0.8388
No log	73.0	219	0.6888	0.4861	0.6888	0.8299
No log	74.0	222	0.6757	0.5025	0.6757	0.8220
No log	75.0	225	0.6885	0.4890	0.6885	0.8298
No log	76.0	228	0.7331	0.5126	0.7331	0.8562
No log	77.0	231	0.7086	0.4866	0.7086	0.8418
No log	78.0	234	0.6999	0.4774	0.6999	0.8366
No log	79.0	237	0.6710	0.4839	0.6710	0.8192
No log	80.0	240	0.6632	0.4937	0.6632	0.8144
No log	81.0	243	0.6771	0.4688	0.6771	0.8229
No log	82.0	246	0.6515	0.4901	0.6515	0.8072
No log	83.0	249	0.6528	0.4817	0.6528	0.8079
No log	84.0	252	0.6621	0.4861	0.6621	0.8137
No log	85.0	255	0.6750	0.4874	0.6750	0.8216
No log	86.0	258	0.6781	0.4807	0.6781	0.8235
No log	87.0	261	0.6873	0.4894	0.6873	0.8290
No log	88.0	264	0.6911	0.5028	0.6911	0.8313
No log	89.0	267	0.7015	0.4734	0.7015	0.8376
No log	90.0	270	0.6760	0.4957	0.6760	0.8222
No log	91.0	273	0.6723	0.5009	0.6723	0.8199
No log	92.0	276	0.6813	0.4946	0.6813	0.8254
No log	93.0	279	0.6868	0.4928	0.6868	0.8287
No log	94.0	282	0.6781	0.4987	0.6781	0.8234
No log	95.0	285	0.6812	0.5010	0.6812	0.8254
No log	96.0	288	0.6845	0.5010	0.6845	0.8274
No log	97.0	291	0.6813	0.4868	0.6813	0.8254
No log	98.0	294	0.6770	0.4865	0.6770	0.8228
No log	99.0	297	0.6768	0.4963	0.6768	0.8227
No log	100.0	300	0.6766	0.4963	0.6766	0.8225

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold0

Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold0