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	3	8.1173	0.0	8.1173	2.8491
No log	2.0	6	7.2012	0.0	7.2012	2.6835
No log	3.0	9	5.8014	0.0163	5.8014	2.4086
No log	4.0	12	4.6023	0.0077	4.6023	2.1453
No log	5.0	15	3.5697	0.0039	3.5697	1.8894
No log	6.0	18	2.4789	0.0640	2.4789	1.5745
No log	7.0	21	1.6659	0.0316	1.6659	1.2907
No log	8.0	24	1.3158	0.0316	1.3158	1.1471
No log	9.0	27	0.9478	0.0316	0.9478	0.9736
No log	10.0	30	0.9945	0.0576	0.9945	0.9972
No log	11.0	33	0.7328	0.4276	0.7328	0.8561
No log	12.0	36	0.9305	0.2699	0.9305	0.9646
No log	13.0	39	0.5772	0.4874	0.5772	0.7598
No log	14.0	42	1.4532	0.2188	1.4532	1.2055
No log	15.0	45	0.5669	0.4629	0.5669	0.7529
No log	16.0	48	0.6345	0.4955	0.6345	0.7966
No log	17.0	51	0.6647	0.4807	0.6647	0.8153
No log	18.0	54	0.6133	0.4390	0.6133	0.7831
No log	19.0	57	0.6032	0.4700	0.6032	0.7766
No log	20.0	60	0.6272	0.4982	0.6272	0.7919
No log	21.0	63	0.7128	0.4813	0.7128	0.8443
No log	22.0	66	0.6083	0.5215	0.6083	0.7799
No log	23.0	69	0.6281	0.5426	0.6281	0.7925
No log	24.0	72	0.6556	0.5336	0.6556	0.8097
No log	25.0	75	0.7033	0.5252	0.7033	0.8386
No log	26.0	78	0.6338	0.5432	0.6338	0.7961
No log	27.0	81	0.6686	0.5228	0.6686	0.8177
No log	28.0	84	0.6080	0.5272	0.6080	0.7797
No log	29.0	87	0.6327	0.5284	0.6327	0.7955
No log	30.0	90	0.7635	0.5065	0.7635	0.8738
No log	31.0	93	0.9300	0.4389	0.9300	0.9644
No log	32.0	96	0.8892	0.4449	0.8892	0.9430
No log	33.0	99	0.8956	0.4635	0.8956	0.9464
No log	34.0	102	0.5956	0.5149	0.5956	0.7717
No log	35.0	105	0.8163	0.4446	0.8163	0.9035
No log	36.0	108	0.5911	0.5169	0.5911	0.7688
No log	37.0	111	0.6160	0.5245	0.6160	0.7849
No log	38.0	114	0.6632	0.5009	0.6632	0.8144
No log	39.0	117	0.7386	0.4693	0.7386	0.8594
No log	40.0	120	0.6123	0.4903	0.6123	0.7825
No log	41.0	123	0.5975	0.4959	0.5975	0.7730
No log	42.0	126	0.7174	0.4707	0.7174	0.8470
No log	43.0	129	0.6461	0.5104	0.6461	0.8038
No log	44.0	132	0.6445	0.5331	0.6445	0.8028
No log	45.0	135	0.7187	0.5155	0.7187	0.8477
No log	46.0	138	0.6500	0.5247	0.6500	0.8062
No log	47.0	141	0.6104	0.5428	0.6104	0.7813
No log	48.0	144	0.6239	0.5465	0.6239	0.7899
No log	49.0	147	0.6434	0.5244	0.6434	0.8021
No log	50.0	150	0.6427	0.5143	0.6427	0.8017
No log	51.0	153	0.6073	0.4993	0.6073	0.7793
No log	52.0	156	0.6720	0.4801	0.6720	0.8197
No log	53.0	159	0.7216	0.5029	0.7216	0.8495
No log	54.0	162	0.6642	0.5373	0.6642	0.8150
No log	55.0	165	0.5799	0.5502	0.5799	0.7615
No log	56.0	168	0.5574	0.5326	0.5574	0.7466
No log	57.0	171	0.5589	0.5490	0.5589	0.7476
No log	58.0	174	0.6205	0.5491	0.6205	0.7877
No log	59.0	177	0.6315	0.5451	0.6315	0.7947
No log	60.0	180	0.7065	0.5208	0.7065	0.8405
No log	61.0	183	0.5941	0.5322	0.5941	0.7708
No log	62.0	186	0.6569	0.5222	0.6569	0.8105
No log	63.0	189	0.6194	0.5301	0.6194	0.7870
No log	64.0	192	0.6558	0.5312	0.6558	0.8098
No log	65.0	195	0.6397	0.5283	0.6397	0.7998
No log	66.0	198	0.5820	0.5467	0.5820	0.7629
No log	67.0	201	0.6836	0.5262	0.6836	0.8268
No log	68.0	204	0.5736	0.5464	0.5736	0.7574
No log	69.0	207	0.5768	0.5524	0.5768	0.7595
No log	70.0	210	0.6291	0.5471	0.6291	0.7932
No log	71.0	213	0.5713	0.5524	0.5713	0.7558
No log	72.0	216	0.6264	0.5426	0.6264	0.7915
No log	73.0	219	0.5729	0.5493	0.5729	0.7569
No log	74.0	222	0.6287	0.5363	0.6287	0.7929
No log	75.0	225	0.6097	0.5413	0.6097	0.7808
No log	76.0	228	0.6532	0.5481	0.6532	0.8082
No log	77.0	231	0.5964	0.5459	0.5964	0.7723
No log	78.0	234	0.6144	0.5465	0.6144	0.7838
No log	79.0	237	0.5728	0.5416	0.5728	0.7569
No log	80.0	240	0.5676	0.5542	0.5676	0.7534
No log	81.0	243	0.5366	0.5408	0.5366	0.7325
No log	82.0	246	0.5260	0.5490	0.5260	0.7252
No log	83.0	249	0.5986	0.5503	0.5986	0.7737
No log	84.0	252	0.5809	0.5625	0.5809	0.7622
No log	85.0	255	0.7380	0.5277	0.7380	0.8590
No log	86.0	258	0.6227	0.5523	0.6227	0.7891
No log	87.0	261	0.6323	0.5450	0.6323	0.7952
No log	88.0	264	0.5835	0.5545	0.5835	0.7639
No log	89.0	267	0.5395	0.5430	0.5395	0.7345
No log	90.0	270	0.5880	0.5647	0.5880	0.7668
No log	91.0	273	0.5633	0.5455	0.5633	0.7505
No log	92.0	276	0.5863	0.5535	0.5863	0.7657
No log	93.0	279	0.5644	0.5467	0.5644	0.7513
No log	94.0	282	0.5577	0.5451	0.5577	0.7468
No log	95.0	285	0.5735	0.5450	0.5735	0.7573
No log	96.0	288	0.5971	0.5679	0.5971	0.7727
No log	97.0	291	0.6143	0.5700	0.6143	0.7838
No log	98.0	294	0.6667	0.5494	0.6667	0.8165
No log	99.0	297	0.5593	0.5679	0.5593	0.7479
No log	100.0	300	0.5684	0.5723	0.5684	0.7539
No log	101.0	303	0.6921	0.5312	0.6921	0.8320
No log	102.0	306	0.6088	0.5687	0.6088	0.7803
No log	103.0	309	0.5532	0.5428	0.5532	0.7438
No log	104.0	312	0.5457	0.5328	0.5457	0.7387
No log	105.0	315	0.6322	0.5389	0.6322	0.7951
No log	106.0	318	0.6107	0.5363	0.6107	0.7815
No log	107.0	321	0.5306	0.5229	0.5306	0.7284
No log	108.0	324	0.5402	0.5406	0.5402	0.7350
No log	109.0	327	0.5859	0.5632	0.5859	0.7655
No log	110.0	330	0.6183	0.5590	0.6183	0.7863
No log	111.0	333	0.5917	0.5753	0.5917	0.7693
No log	112.0	336	0.5933	0.5635	0.5933	0.7702
No log	113.0	339	0.5623	0.5781	0.5623	0.7499
No log	114.0	342	0.6116	0.5584	0.6116	0.7820
No log	115.0	345	0.6586	0.5493	0.6586	0.8116
No log	116.0	348	0.6047	0.5623	0.6047	0.7776
No log	117.0	351	0.6029	0.5496	0.6029	0.7765
No log	118.0	354	0.6557	0.5462	0.6557	0.8097
No log	119.0	357	0.6523	0.5444	0.6523	0.8077
No log	120.0	360	0.5949	0.5565	0.5949	0.7713
No log	121.0	363	0.6037	0.5593	0.6037	0.7770
No log	122.0	366	0.6469	0.5613	0.6469	0.8043
No log	123.0	369	0.6296	0.5646	0.6296	0.7935
No log	124.0	372	0.5644	0.5529	0.5644	0.7513
No log	125.0	375	0.5505	0.5478	0.5505	0.7420
No log	126.0	378	0.5601	0.5533	0.5601	0.7484
No log	127.0	381	0.6227	0.5451	0.6227	0.7891
No log	128.0	384	0.6264	0.5494	0.6264	0.7915
No log	129.0	387	0.5853	0.5600	0.5853	0.7650
No log	130.0	390	0.5846	0.5613	0.5846	0.7646
No log	131.0	393	0.6228	0.5508	0.6228	0.7892
No log	132.0	396	0.7085	0.5292	0.7085	0.8417
No log	133.0	399	0.7231	0.5227	0.7231	0.8504
No log	134.0	402	0.6496	0.5519	0.6496	0.8060
No log	135.0	405	0.5969	0.5612	0.5969	0.7726
No log	136.0	408	0.5952	0.5560	0.5952	0.7715
No log	137.0	411	0.6117	0.5494	0.6117	0.7821
No log	138.0	414	0.6015	0.5550	0.6015	0.7755
No log	139.0	417	0.5765	0.5597	0.5765	0.7593
No log	140.0	420	0.5679	0.5584	0.5679	0.7536
No log	141.0	423	0.5821	0.5564	0.5821	0.7630
No log	142.0	426	0.6078	0.5633	0.6078	0.7796
No log	143.0	429	0.6296	0.5542	0.6296	0.7935

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version25NewTestASAP_FineTuningBERT_AugV25_k3_task1_organization_k3_k3_fold0

Version25NewTestASAP_FineTuningBERT_AugV25_k3_task1_organization_k3_k3_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version25NewTestASAP_FineTuningBERT_AugV25_k3_task1_organization_k3_k3_fold0