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	3	10.7318	0.0032	10.7320	3.2760
No log	2.0	6	7.5245	0.0	7.5246	2.7431
No log	3.0	9	5.0971	0.0078	5.0974	2.2577
No log	4.0	12	3.6200	0.0039	3.6204	1.9027
No log	5.0	15	2.5936	0.0	2.5941	1.6106
No log	6.0	18	1.8909	0.0213	1.8913	1.3753
No log	7.0	21	1.4499	0.0107	1.4503	1.2043
No log	8.0	24	1.2370	0.0280	1.2374	1.1124
No log	9.0	27	1.1493	0.0345	1.1498	1.0723
No log	10.0	30	1.0123	0.0602	1.0127	1.0063
No log	11.0	33	1.3159	0.1695	1.3164	1.1473
No log	12.0	36	1.1045	0.2394	1.1050	1.0512
No log	13.0	39	1.1301	0.3056	1.1304	1.0632
No log	14.0	42	1.1139	0.3252	1.1140	1.0555
No log	15.0	45	0.8206	0.3947	0.8206	0.9059
No log	16.0	48	0.9813	0.3654	0.9814	0.9906
No log	17.0	51	0.8364	0.3918	0.8362	0.9145
No log	18.0	54	0.8397	0.3537	0.8393	0.9161
No log	19.0	57	1.1984	0.3001	1.1982	1.0946
No log	20.0	60	0.8701	0.3613	0.8695	0.9325
No log	21.0	63	1.3493	0.2817	1.3493	1.1616
No log	22.0	66	0.8570	0.3724	0.8567	0.9256
No log	23.0	69	1.3660	0.2671	1.3659	1.1687
No log	24.0	72	1.2033	0.2623	1.2032	1.0969
No log	25.0	75	1.0505	0.2949	1.0504	1.0249
No log	26.0	78	1.2513	0.2681	1.2515	1.1187
No log	27.0	81	0.9021	0.4068	0.9017	0.9496
No log	28.0	84	1.2650	0.2764	1.2651	1.1247
No log	29.0	87	0.9016	0.3882	0.9013	0.9494
No log	30.0	90	0.9823	0.3329	0.9821	0.9910
No log	31.0	93	0.9257	0.3668	0.9256	0.9621
No log	32.0	96	0.8609	0.3728	0.8608	0.9278
No log	33.0	99	1.0047	0.3335	1.0048	1.0024
No log	34.0	102	0.8655	0.4281	0.8654	0.9303
No log	35.0	105	0.9553	0.4020	0.9553	0.9774
No log	36.0	108	0.8910	0.4144	0.8910	0.9440
No log	37.0	111	0.8586	0.4072	0.8586	0.9266
No log	38.0	114	0.9181	0.3765	0.9183	0.9583
No log	39.0	117	0.8401	0.4133	0.8402	0.9166
No log	40.0	120	0.9251	0.4329	0.9254	0.9620
No log	41.0	123	0.9372	0.4368	0.9375	0.9682
No log	42.0	126	0.8471	0.4477	0.8471	0.9204
No log	43.0	129	0.8581	0.4433	0.8583	0.9264
No log	44.0	132	0.9161	0.4071	0.9159	0.9570
No log	45.0	135	0.8567	0.4652	0.8569	0.9257
No log	46.0	138	0.8231	0.4648	0.8232	0.9073
No log	47.0	141	0.9116	0.4208	0.9114	0.9547
No log	48.0	144	0.9315	0.4386	0.9317	0.9653
No log	49.0	147	0.8266	0.4505	0.8268	0.9093
No log	50.0	150	0.7916	0.4531	0.7916	0.8897
No log	51.0	153	0.9235	0.4153	0.9237	0.9611
No log	52.0	156	0.7708	0.4578	0.7708	0.8780
No log	53.0	159	0.7773	0.4712	0.7773	0.8816
No log	54.0	162	0.7574	0.4751	0.7574	0.8703
No log	55.0	165	0.7611	0.4561	0.7611	0.8724
No log	56.0	168	0.7183	0.4774	0.7183	0.8475
No log	57.0	171	0.7965	0.4476	0.7966	0.8925
No log	58.0	174	0.7632	0.4837	0.7633	0.8737
No log	59.0	177	0.7699	0.4570	0.7699	0.8775
No log	60.0	180	0.8349	0.4403	0.8350	0.9138
No log	61.0	183	0.7758	0.4244	0.7758	0.8808
No log	62.0	186	0.7535	0.4300	0.7536	0.8681
No log	63.0	189	0.8341	0.4395	0.8343	0.9134
No log	64.0	192	0.7715	0.4466	0.7715	0.8784
No log	65.0	195	0.7883	0.4574	0.7883	0.8878
No log	66.0	198	0.7989	0.4750	0.7990	0.8938
No log	67.0	201	0.7623	0.4646	0.7622	0.8731
No log	68.0	204	0.7211	0.4595	0.7211	0.8492
No log	69.0	207	0.7983	0.4474	0.7984	0.8936
No log	70.0	210	0.7206	0.4577	0.7207	0.8489
No log	71.0	213	0.7281	0.4680	0.7281	0.8533
No log	72.0	216	0.7827	0.4667	0.7828	0.8848
No log	73.0	219	0.7518	0.4573	0.7518	0.8671
No log	74.0	222	0.7757	0.4614	0.7758	0.8808
No log	75.0	225	0.7838	0.4662	0.7839	0.8854
No log	76.0	228	0.7481	0.4507	0.7481	0.8649
No log	77.0	231	0.7862	0.4916	0.7862	0.8867
No log	78.0	234	0.7488	0.4548	0.7488	0.8653
No log	79.0	237	0.7373	0.4700	0.7372	0.8586
No log	80.0	240	0.7275	0.4970	0.7275	0.8529
No log	81.0	243	0.7210	0.5018	0.7210	0.8491
No log	82.0	246	0.7150	0.4988	0.7150	0.8456
No log	83.0	249	0.7205	0.4961	0.7205	0.8488
No log	84.0	252	0.7380	0.4981	0.7380	0.8591
No log	85.0	255	0.7201	0.4794	0.7201	0.8486
No log	86.0	258	0.7417	0.4916	0.7418	0.8613
No log	87.0	261	0.7619	0.4917	0.7620	0.8729
No log	88.0	264	0.7619	0.4894	0.7619	0.8729
No log	89.0	267	0.7448	0.4802	0.7448	0.8630
No log	90.0	270	0.7276	0.4619	0.7276	0.8530
No log	91.0	273	0.7251	0.4822	0.7251	0.8515
No log	92.0	276	0.7232	0.4774	0.7232	0.8504
No log	93.0	279	0.7197	0.4756	0.7197	0.8484
No log	94.0	282	0.7161	0.4737	0.7161	0.8462
No log	95.0	285	0.7159	0.4710	0.7158	0.8461
No log	96.0	288	0.7209	0.4788	0.7209	0.8491
No log	97.0	291	0.7220	0.4797	0.7220	0.8497
No log	98.0	294	0.7161	0.4778	0.7161	0.8462
No log	99.0	297	0.7118	0.4788	0.7118	0.8437
No log	100.0	300	0.7101	0.4788	0.7101	0.8427

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV10_k3_task1_organization_k3_k3_fold2

ASAP_FineTuningBERT_AugV10_k3_task1_organization_k3_k3_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV10_k3_task1_organization_k3_k3_fold2