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	6	5.6494	0.0595	5.6494	2.3768
No log	2.0	12	2.4210	0.0654	2.4210	1.5560
No log	3.0	18	1.2872	0.0420	1.2872	1.1346
No log	4.0	24	0.9119	0.0982	0.9119	0.9550
No log	5.0	30	0.9532	0.1618	0.9532	0.9763
No log	6.0	36	0.7306	0.3492	0.7306	0.8547
No log	7.0	42	1.7609	0.1787	1.7609	1.3270
No log	8.0	48	1.2529	0.1999	1.2529	1.1193
No log	9.0	54	0.6905	0.3845	0.6905	0.8309
No log	10.0	60	1.3494	0.2541	1.3494	1.1616
No log	11.0	66	0.9687	0.2672	0.9687	0.9842
No log	12.0	72	1.1282	0.2803	1.1282	1.0622
No log	13.0	78	0.9509	0.3424	0.9509	0.9751
No log	14.0	84	0.8315	0.3941	0.8315	0.9119
No log	15.0	90	0.6832	0.4353	0.6832	0.8266
No log	16.0	96	0.8018	0.3801	0.8018	0.8954
No log	17.0	102	0.7527	0.3722	0.7527	0.8676
No log	18.0	108	0.8291	0.4090	0.8291	0.9105
No log	19.0	114	1.2881	0.3855	1.2881	1.1350
No log	20.0	120	0.6714	0.5021	0.6714	0.8194
No log	21.0	126	0.8992	0.3998	0.8992	0.9482
No log	22.0	132	0.7395	0.4782	0.7395	0.8600
No log	23.0	138	0.9035	0.4086	0.9035	0.9505
No log	24.0	144	0.7171	0.4949	0.7171	0.8468
No log	25.0	150	0.7036	0.5091	0.7036	0.8388
No log	26.0	156	0.7293	0.5099	0.7293	0.8540
No log	27.0	162	0.8334	0.4028	0.8334	0.9129
No log	28.0	168	0.7269	0.5064	0.7269	0.8526
No log	29.0	174	0.8273	0.5000	0.8273	0.9095
No log	30.0	180	0.8247	0.4760	0.8247	0.9081
No log	31.0	186	0.9377	0.4374	0.9377	0.9683
No log	32.0	192	0.7972	0.4763	0.7972	0.8929
No log	33.0	198	0.7935	0.4865	0.7935	0.8908
No log	34.0	204	0.7099	0.4909	0.7099	0.8425
No log	35.0	210	1.1527	0.3622	1.1527	1.0737
No log	36.0	216	0.8209	0.4903	0.8209	0.9061
No log	37.0	222	0.7750	0.5072	0.7750	0.8804
No log	38.0	228	0.8699	0.4582	0.8699	0.9327
No log	39.0	234	0.8483	0.4594	0.8483	0.9210
No log	40.0	240	0.7659	0.5003	0.7659	0.8752
No log	41.0	246	0.8754	0.4906	0.8754	0.9356
No log	42.0	252	0.9897	0.4133	0.9897	0.9948
No log	43.0	258	0.8568	0.4881	0.8568	0.9256
No log	44.0	264	0.7623	0.4881	0.7623	0.8731
No log	45.0	270	0.9235	0.4599	0.9235	0.9610
No log	46.0	276	0.7761	0.4983	0.7761	0.8810
No log	47.0	282	0.9767	0.4237	0.9767	0.9883
No log	48.0	288	0.8170	0.4870	0.8170	0.9039
No log	49.0	294	0.7416	0.5006	0.7416	0.8612
No log	50.0	300	0.8229	0.4841	0.8229	0.9071
No log	51.0	306	0.7905	0.5024	0.7905	0.8891
No log	52.0	312	0.8441	0.4989	0.8441	0.9188
No log	53.0	318	0.8039	0.5207	0.8039	0.8966
No log	54.0	324	0.8281	0.4858	0.8281	0.9100
No log	55.0	330	0.9117	0.4284	0.9117	0.9548
No log	56.0	336	0.8336	0.4668	0.8336	0.9130
No log	57.0	342	0.7537	0.4777	0.7537	0.8682
No log	58.0	348	0.8783	0.4524	0.8783	0.9372
No log	59.0	354	0.8148	0.4743	0.8148	0.9027
No log	60.0	360	0.8037	0.4670	0.8037	0.8965
No log	61.0	366	0.7640	0.4895	0.7640	0.8741
No log	62.0	372	0.7375	0.5075	0.7375	0.8588
No log	63.0	378	0.7678	0.4993	0.7678	0.8762
No log	64.0	384	0.8266	0.4989	0.8266	0.9092
No log	65.0	390	0.8950	0.4424	0.8950	0.9461
No log	66.0	396	0.7540	0.5361	0.7540	0.8684
No log	67.0	402	0.8306	0.4653	0.8306	0.9114
No log	68.0	408	0.8320	0.4555	0.8320	0.9121
No log	69.0	414	0.7779	0.4956	0.7779	0.8820
No log	70.0	420	0.8441	0.4668	0.8441	0.9187
No log	71.0	426	0.7862	0.5035	0.7862	0.8867
No log	72.0	432	0.8342	0.4810	0.8342	0.9134
No log	73.0	438	0.7997	0.5085	0.7997	0.8943
No log	74.0	444	0.7858	0.5020	0.7858	0.8865
No log	75.0	450	0.8165	0.4707	0.8165	0.9036
No log	76.0	456	0.7953	0.4933	0.7953	0.8918
No log	77.0	462	0.8438	0.4591	0.8438	0.9186
No log	78.0	468	0.8706	0.4643	0.8706	0.9331
No log	79.0	474	0.9057	0.4472	0.9057	0.9517
No log	80.0	480	0.8095	0.4788	0.8095	0.8997
No log	81.0	486	0.8738	0.4624	0.8738	0.9348
No log	82.0	492	0.8108	0.4894	0.8108	0.9005
No log	83.0	498	0.8221	0.4630	0.8221	0.9067
0.2979	84.0	504	0.8202	0.4643	0.8202	0.9056
0.2979	85.0	510	0.8332	0.4702	0.8332	0.9128
0.2979	86.0	516	0.8435	0.4558	0.8435	0.9184
0.2979	87.0	522	0.8381	0.4663	0.8381	0.9155
0.2979	88.0	528	0.8089	0.4631	0.8089	0.8994
0.2979	89.0	534	0.8367	0.4702	0.8367	0.9147
0.2979	90.0	540	0.8470	0.4707	0.8470	0.9203
0.2979	91.0	546	0.8266	0.4606	0.8266	0.9091
0.2979	92.0	552	0.8006	0.4726	0.8006	0.8948
0.2979	93.0	558	0.7924	0.4810	0.7924	0.8902
0.2979	94.0	564	0.8249	0.4695	0.8249	0.9082
0.2979	95.0	570	0.8153	0.4832	0.8153	0.9029
0.2979	96.0	576	0.8092	0.4870	0.8092	0.8995

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k10_task1_organization_k10_k10_fold4

Version22NewTestASAP_FineTuningBERT_AugV22_k10_task1_organization_k10_k10_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version22NewTestASAP_FineTuningBERT_AugV22_k10_task1_organization_k10_k10_fold4