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	4	7.6208	0.0	7.6208	2.7606
No log	2.0	8	5.9781	0.0331	5.9781	2.4450
No log	3.0	12	4.3498	0.0039	4.3498	2.0856
No log	4.0	16	2.8171	0.0	2.8171	1.6784
No log	5.0	20	1.6496	0.0316	1.6496	1.2844
No log	6.0	24	1.0411	0.0106	1.0411	1.0203
No log	7.0	28	0.7682	0.2314	0.7682	0.8765
No log	8.0	32	0.7899	0.3200	0.7899	0.8888
No log	9.0	36	1.2270	0.1712	1.2270	1.1077
No log	10.0	40	0.7612	0.4197	0.7612	0.8725
No log	11.0	44	0.5671	0.4916	0.5671	0.7531
No log	12.0	48	0.9357	0.3395	0.9357	0.9673
No log	13.0	52	0.5474	0.4953	0.5474	0.7399
No log	14.0	56	0.8483	0.3993	0.8483	0.9210
No log	15.0	60	0.6948	0.4714	0.6948	0.8335
No log	16.0	64	1.0242	0.3306	1.0242	1.0120
No log	17.0	68	0.5827	0.5350	0.5827	0.7634
No log	18.0	72	1.0694	0.3391	1.0694	1.0341
No log	19.0	76	0.5975	0.5477	0.5975	0.7730
No log	20.0	80	1.0474	0.3648	1.0474	1.0234
No log	21.0	84	0.5638	0.5271	0.5638	0.7508
No log	22.0	88	0.7114	0.4635	0.7114	0.8434
No log	23.0	92	0.5970	0.5592	0.5970	0.7727
No log	24.0	96	0.7908	0.4550	0.7908	0.8892
No log	25.0	100	0.5652	0.5248	0.5652	0.7518
No log	26.0	104	0.6927	0.4918	0.6927	0.8323
No log	27.0	108	0.5952	0.5623	0.5952	0.7715
No log	28.0	112	0.6085	0.5636	0.6085	0.7801
No log	29.0	116	0.5639	0.5384	0.5639	0.7509
No log	30.0	120	0.5717	0.5339	0.5717	0.7561
No log	31.0	124	0.5695	0.5495	0.5695	0.7546
No log	32.0	128	0.5819	0.5616	0.5819	0.7628
No log	33.0	132	0.6042	0.5285	0.6042	0.7773
No log	34.0	136	0.5585	0.5206	0.5585	0.7473
No log	35.0	140	0.5609	0.5483	0.5609	0.7489
No log	36.0	144	0.5593	0.5769	0.5593	0.7478
No log	37.0	148	0.5621	0.5518	0.5621	0.7497
No log	38.0	152	0.5489	0.5303	0.5489	0.7409
No log	39.0	156	0.5520	0.5559	0.5520	0.7429
No log	40.0	160	0.5602	0.5335	0.5602	0.7484
No log	41.0	164	0.5583	0.5184	0.5583	0.7472
No log	42.0	168	0.5379	0.5467	0.5379	0.7334
No log	43.0	172	0.5384	0.5843	0.5384	0.7338
No log	44.0	176	0.5372	0.5519	0.5372	0.7329
No log	45.0	180	0.5399	0.5801	0.5399	0.7348
No log	46.0	184	0.5371	0.5919	0.5371	0.7329
No log	47.0	188	0.6365	0.5389	0.6365	0.7978
No log	48.0	192	0.5511	0.5665	0.5511	0.7424
No log	49.0	196	0.5874	0.5513	0.5874	0.7664
No log	50.0	200	0.5346	0.5925	0.5346	0.7312
No log	51.0	204	0.5222	0.5836	0.5222	0.7227
No log	52.0	208	0.5580	0.5644	0.5580	0.7470
No log	53.0	212	0.5838	0.5516	0.5838	0.7641
No log	54.0	216	0.5700	0.5454	0.5700	0.7550
No log	55.0	220	0.5388	0.5849	0.5388	0.7341
No log	56.0	224	0.5327	0.5761	0.5327	0.7299
No log	57.0	228	0.5349	0.5733	0.5349	0.7314
No log	58.0	232	0.5151	0.5410	0.5151	0.7177
No log	59.0	236	0.5296	0.5582	0.5296	0.7278
No log	60.0	240	0.5622	0.5605	0.5622	0.7498
No log	61.0	244	0.5380	0.5733	0.5380	0.7335
No log	62.0	248	0.5431	0.5712	0.5431	0.7370
No log	63.0	252	0.5748	0.5736	0.5748	0.7581
No log	64.0	256	0.5368	0.5786	0.5368	0.7327
No log	65.0	260	0.5668	0.5665	0.5668	0.7529
No log	66.0	264	0.5379	0.5937	0.5379	0.7334
No log	67.0	268	0.6094	0.5298	0.6094	0.7806
No log	68.0	272	0.5475	0.5509	0.5475	0.7399
No log	69.0	276	0.6033	0.5288	0.6033	0.7767
No log	70.0	280	0.5491	0.5569	0.5491	0.7410
No log	71.0	284	0.5804	0.5444	0.5804	0.7619
No log	72.0	288	0.5346	0.5618	0.5346	0.7312
No log	73.0	292	0.5515	0.5605	0.5515	0.7427
No log	74.0	296	0.5436	0.5581	0.5436	0.7373
No log	75.0	300	0.5567	0.5469	0.5567	0.7461
No log	76.0	304	0.5593	0.5553	0.5593	0.7478
No log	77.0	308	0.5414	0.5807	0.5414	0.7358
No log	78.0	312	0.5379	0.5532	0.5379	0.7334
No log	79.0	316	0.5245	0.5462	0.5245	0.7242
No log	80.0	320	0.5352	0.5624	0.5352	0.7315
No log	81.0	324	0.5373	0.5562	0.5373	0.7330
No log	82.0	328	0.5856	0.5594	0.5856	0.7653
No log	83.0	332	0.5407	0.5283	0.5407	0.7353
No log	84.0	336	0.6061	0.5429	0.6061	0.7786
No log	85.0	340	0.5471	0.5466	0.5471	0.7396
No log	86.0	344	0.5814	0.5471	0.5814	0.7625
No log	87.0	348	0.5400	0.5504	0.5400	0.7349
No log	88.0	352	0.5286	0.5603	0.5286	0.7271
No log	89.0	356	0.5385	0.5479	0.5385	0.7338
No log	90.0	360	0.5449	0.5592	0.5449	0.7382
No log	91.0	364	0.5238	0.5438	0.5238	0.7237
No log	92.0	368	0.5317	0.5426	0.5317	0.7292
No log	93.0	372	0.5208	0.5607	0.5208	0.7217
No log	94.0	376	0.5363	0.5642	0.5363	0.7323
No log	95.0	380	0.5312	0.5658	0.5312	0.7288
No log	96.0	384	0.5520	0.5653	0.5520	0.7430

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold0

Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold0