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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	1	11.3342	0.0018	11.3319	3.3663
No log	2.0	2	10.0779	-0.0050	10.0755	3.1742
No log	3.0	3	8.4797	0.0	8.4781	2.9117
No log	4.0	4	7.5814	0.0	7.5798	2.7531
No log	5.0	5	6.9793	0.0	6.9777	2.6415
No log	6.0	6	6.5114	0.0	6.5098	2.5514
No log	7.0	7	6.0401	-0.0292	6.0385	2.4573
No log	8.0	8	5.5446	0.0	5.5431	2.3544
No log	9.0	9	5.0263	0.0	5.0250	2.2416
No log	10.0	10	4.5211	0.0	4.5198	2.1260
No log	11.0	11	4.0227	0.0	4.0215	2.0054
No log	12.0	12	3.5605	0.0	3.5593	1.8866
No log	13.0	13	3.1667	0.0	3.1657	1.7792
No log	14.0	14	2.8334	0.0	2.8324	1.6830
No log	15.0	15	2.5422	0.0529	2.5413	1.5941
No log	16.0	16	2.2792	0.1138	2.2784	1.5094
No log	17.0	17	2.0367	0.0858	2.0359	1.4269
No log	18.0	18	1.8563	0.1164	1.8556	1.3622
No log	19.0	19	1.6779	0.1108	1.6772	1.2951
No log	20.0	20	1.4613	0.0621	1.4607	1.2086
No log	21.0	21	1.3172	0.0621	1.3166	1.1474
No log	22.0	22	1.2064	0.1030	1.2059	1.0981
No log	23.0	23	1.0828	0.0916	1.0823	1.0403
No log	24.0	24	0.9933	0.1117	0.9929	0.9964
No log	25.0	25	0.9393	0.1397	0.9389	0.9690
No log	26.0	26	0.8259	0.3080	0.8255	0.9086
No log	27.0	27	0.7596	0.4945	0.7592	0.8713
No log	28.0	28	0.9196	0.4135	0.9193	0.9588
No log	29.0	29	0.7430	0.5116	0.7427	0.8618
No log	30.0	30	0.6589	0.5077	0.6586	0.8115
No log	31.0	31	0.6455	0.5210	0.6452	0.8032
No log	32.0	32	0.6086	0.5784	0.6084	0.7800
No log	33.0	33	0.8333	0.4748	0.8332	0.9128
No log	34.0	34	0.8365	0.4721	0.8364	0.9145
No log	35.0	35	0.5957	0.5627	0.5956	0.7717
No log	36.0	36	0.5406	0.5322	0.5405	0.7352
No log	37.0	37	0.5447	0.5153	0.5446	0.7379
No log	38.0	38	0.4977	0.5695	0.4976	0.7054
No log	39.0	39	0.5226	0.5755	0.5225	0.7228
No log	40.0	40	0.5161	0.5782	0.5160	0.7183
No log	41.0	41	0.4831	0.5838	0.4830	0.6950
No log	42.0	42	0.5652	0.5614	0.5652	0.7518
No log	43.0	43	0.5633	0.5730	0.5632	0.7504
No log	44.0	44	0.4899	0.6754	0.4898	0.6998
No log	45.0	45	0.5367	0.6286	0.5367	0.7326
No log	46.0	46	0.5374	0.6203	0.5374	0.7331
No log	47.0	47	0.5018	0.6932	0.5017	0.7083
No log	48.0	48	0.5201	0.6748	0.5200	0.7211
No log	49.0	49	0.4969	0.6875	0.4968	0.7049
No log	50.0	50	0.4979	0.6855	0.4979	0.7056
No log	51.0	51	0.5251	0.6695	0.5250	0.7246
No log	52.0	52	0.5172	0.6761	0.5171	0.7191
No log	53.0	53	0.5212	0.6691	0.5211	0.7219
No log	54.0	54	0.5240	0.6688	0.5238	0.7238
No log	55.0	55	0.5413	0.6545	0.5412	0.7356
No log	56.0	56	0.7533	0.5936	0.7532	0.8679
No log	57.0	57	0.8182	0.5809	0.8181	0.9045
No log	58.0	58	0.6827	0.6355	0.6826	0.8262
No log	59.0	59	0.5334	0.6661	0.5333	0.7303
No log	60.0	60	0.5473	0.6656	0.5472	0.7398
No log	61.0	61	0.5385	0.6598	0.5384	0.7338
No log	62.0	62	0.6071	0.6447	0.6070	0.7791
No log	63.0	63	0.6849	0.6327	0.6848	0.8275
No log	64.0	64	0.6414	0.6482	0.6413	0.8008
No log	65.0	65	0.6017	0.6559	0.6016	0.7756
No log	66.0	66	0.6492	0.6513	0.6490	0.8056
No log	67.0	67	0.8579	0.6038	0.8577	0.9261
No log	68.0	68	0.8908	0.5908	0.8906	0.9437
No log	69.0	69	0.7679	0.6293	0.7677	0.8762
No log	70.0	70	0.7035	0.6436	0.7033	0.8386
No log	71.0	71	0.6195	0.6526	0.6193	0.7869
No log	72.0	72	0.6501	0.6483	0.6499	0.8061
No log	73.0	73	0.7279	0.6358	0.7277	0.8530
No log	74.0	74	0.8774	0.5945	0.8771	0.9366
No log	75.0	75	0.8554	0.6072	0.8551	0.9247
No log	76.0	76	0.7099	0.6453	0.7097	0.8424
No log	77.0	77	0.6397	0.6666	0.6395	0.7997

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version24NewTestASAP_FineTuningBERT_AugV24_k1_task1_organization_k1_k1_fold3

Version24NewTestASAP_FineTuningBERT_AugV24_k1_task1_organization_k1_k1_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version24NewTestASAP_FineTuningBERT_AugV24_k1_task1_organization_k1_k1_fold3