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	2	7.6515	0.0	7.6491	2.7657
No log	2.0	4	7.2769	0.0	7.2746	2.6972
No log	3.0	6	6.6878	0.0	6.6856	2.5856
No log	4.0	8	5.5007	-0.0249	5.4987	2.3449
No log	5.0	10	4.1832	0.0	4.1812	2.0448
No log	6.0	12	3.4541	0.0	3.4520	1.8579
No log	7.0	14	2.6346	0.0	2.6328	1.6226
No log	8.0	16	2.0707	0.0828	2.0691	1.4384
No log	9.0	18	2.0477	0.0071	2.0459	1.4303
No log	10.0	20	1.4560	0.0	1.4544	1.2060
No log	11.0	22	1.3488	0.0	1.3473	1.1607
No log	12.0	24	1.2278	0.0	1.2263	1.1074
No log	13.0	26	1.2362	0.0	1.2348	1.1112
No log	14.0	28	1.0369	0.0	1.0356	1.0176
No log	15.0	30	1.0128	0.0106	1.0115	1.0057
No log	16.0	32	1.1013	0.0106	1.0999	1.0488
No log	17.0	34	0.9689	0.0303	0.9676	0.9837
No log	18.0	36	0.9658	0.0816	0.9645	0.9821
No log	19.0	38	0.8938	0.1866	0.8925	0.9447
No log	20.0	40	0.8526	0.2314	0.8512	0.9226
No log	21.0	42	0.7950	0.3968	0.7938	0.8909
No log	22.0	44	0.6826	0.4813	0.6816	0.8256
No log	23.0	46	0.5992	0.4616	0.5982	0.7735
No log	24.0	48	0.5924	0.4955	0.5916	0.7692
No log	25.0	50	0.6859	0.4972	0.6853	0.8278
No log	26.0	52	0.6387	0.4266	0.6380	0.7988
No log	27.0	54	0.9284	0.2726	0.9272	0.9629
No log	28.0	56	0.9471	0.2883	0.9459	0.9726
No log	29.0	58	0.7218	0.4343	0.7213	0.8493
No log	30.0	60	0.6607	0.4495	0.6603	0.8126
No log	31.0	62	0.7538	0.5183	0.7531	0.8678
No log	32.0	64	0.7170	0.5374	0.7164	0.8464
No log	33.0	66	0.5822	0.5051	0.5816	0.7627
No log	34.0	68	0.6273	0.5324	0.6268	0.7917
No log	35.0	70	0.8652	0.5094	0.8645	0.9298
No log	36.0	72	1.1595	0.3698	1.1588	1.0765
No log	37.0	74	0.9627	0.4785	0.9620	0.9808
No log	38.0	76	0.6399	0.5643	0.6395	0.7997
No log	39.0	78	0.6513	0.5569	0.6509	0.8068
No log	40.0	80	0.8194	0.5260	0.8188	0.9049
No log	41.0	82	0.6770	0.5666	0.6765	0.8225
No log	42.0	84	0.5916	0.5937	0.5914	0.7690
No log	43.0	86	0.6076	0.6000	0.6073	0.7793
No log	44.0	88	0.7626	0.5499	0.7620	0.8730
No log	45.0	90	0.7830	0.5313	0.7824	0.8845
No log	46.0	92	0.8039	0.5347	0.8033	0.8962
No log	47.0	94	0.7900	0.5326	0.7893	0.8884
No log	48.0	96	0.8261	0.5173	0.8253	0.9085
No log	49.0	98	0.7813	0.5330	0.7806	0.8835
No log	50.0	100	0.7682	0.5381	0.7676	0.8761
No log	51.0	102	0.8787	0.5212	0.8781	0.9371
No log	52.0	104	0.9945	0.4984	0.9939	0.9969
No log	53.0	106	0.9439	0.5222	0.9434	0.9713
No log	54.0	108	0.9474	0.5278	0.9470	0.9732
No log	55.0	110	1.3297	0.4664	1.3290	1.1528
No log	56.0	112	1.3331	0.4726	1.3324	1.1543
No log	57.0	114	1.0667	0.5039	1.0662	1.0326
No log	58.0	116	1.0038	0.5175	1.0033	1.0016
No log	59.0	118	1.0211	0.5124	1.0206	1.0103
No log	60.0	120	1.0448	0.5003	1.0443	1.0219
No log	61.0	122	1.2581	0.4791	1.2575	1.1214
No log	62.0	124	1.2258	0.4850	1.2253	1.1069
No log	63.0	126	1.0197	0.4921	1.0192	1.0096

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1