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	13.7245	0.0	13.7245	3.7047
No log	2.0	4	10.5904	0.0093	10.5904	3.2543
No log	3.0	6	8.5278	0.0054	8.5278	2.9202
No log	4.0	8	6.5902	0.0034	6.5902	2.5671
No log	5.0	10	5.5007	0.0200	5.5007	2.3453
No log	6.0	12	4.0479	0.0115	4.0479	2.0119
No log	7.0	14	3.2893	0.0115	3.2893	1.8136
No log	8.0	16	2.7327	0.0143	2.7327	1.6531
No log	9.0	18	2.3421	0.0152	2.3421	1.5304
No log	10.0	20	1.4161	0.0316	1.4161	1.1900
No log	11.0	22	1.2735	0.0316	1.2735	1.1285
No log	12.0	24	0.8693	0.1408	0.8693	0.9324
No log	13.0	26	0.8039	0.1723	0.8039	0.8966
No log	14.0	28	0.7018	0.2760	0.7018	0.8378
No log	15.0	30	0.8384	0.2115	0.8384	0.9156
No log	16.0	32	0.9149	0.1102	0.9149	0.9565
No log	17.0	34	0.9314	0.1654	0.9314	0.9651
No log	18.0	36	0.7689	0.3253	0.7689	0.8769
No log	19.0	38	0.6002	0.4108	0.6002	0.7747
No log	20.0	40	0.5798	0.4626	0.5798	0.7615
No log	21.0	42	0.6228	0.5078	0.6228	0.7892
No log	22.0	44	0.5185	0.5113	0.5185	0.7201
No log	23.0	46	0.5945	0.5239	0.5945	0.7710
No log	24.0	48	0.7620	0.4890	0.7620	0.8729
No log	25.0	50	0.8757	0.4558	0.8757	0.9358
No log	26.0	52	0.9210	0.4435	0.9210	0.9597
No log	27.0	54	0.6634	0.5333	0.6634	0.8145
No log	28.0	56	0.4754	0.5615	0.4754	0.6895
No log	29.0	58	0.6989	0.5254	0.6989	0.8360
No log	30.0	60	0.5399	0.5770	0.5399	0.7348
No log	31.0	62	0.4769	0.5166	0.4769	0.6905
No log	32.0	64	0.4944	0.5528	0.4944	0.7032
No log	33.0	66	0.9061	0.4532	0.9061	0.9519
No log	34.0	68	0.6861	0.5205	0.6861	0.8283
No log	35.0	70	0.4894	0.5931	0.4894	0.6996
No log	36.0	72	0.4859	0.5960	0.4859	0.6970
No log	37.0	74	0.5051	0.5774	0.5051	0.7107
No log	38.0	76	0.5103	0.5822	0.5103	0.7144
No log	39.0	78	0.5452	0.5930	0.5452	0.7384
No log	40.0	80	0.5368	0.5942	0.5368	0.7327
No log	41.0	82	0.5721	0.5771	0.5721	0.7564
No log	42.0	84	0.5958	0.5938	0.5958	0.7719
No log	43.0	86	0.5962	0.5986	0.5962	0.7722
No log	44.0	88	0.5859	0.5969	0.5859	0.7655
No log	45.0	90	0.5732	0.6380	0.5732	0.7571
No log	46.0	92	0.5612	0.6353	0.5612	0.7491
No log	47.0	94	0.5415	0.6307	0.5415	0.7359
No log	48.0	96	0.5333	0.5891	0.5333	0.7303
No log	49.0	98	0.5193	0.5930	0.5193	0.7206
No log	50.0	100	0.6841	0.5995	0.6841	0.8271
No log	51.0	102	0.7195	0.5833	0.7195	0.8483
No log	52.0	104	0.5601	0.6112	0.5601	0.7484
No log	53.0	106	0.5078	0.6030	0.5078	0.7126
No log	54.0	108	0.5684	0.6287	0.5684	0.7539
No log	55.0	110	0.5517	0.6260	0.5517	0.7428
No log	56.0	112	0.6479	0.6090	0.6479	0.8049
No log	57.0	114	0.6138	0.6238	0.6138	0.7834
No log	58.0	116	0.5821	0.5665	0.5821	0.7629
No log	59.0	118	0.5614	0.5893	0.5614	0.7493
No log	60.0	120	0.6100	0.6135	0.6100	0.7810
No log	61.0	122	0.5899	0.6134	0.5899	0.7680
No log	62.0	124	0.5728	0.6034	0.5728	0.7568
No log	63.0	126	0.5756	0.5637	0.5756	0.7587
No log	64.0	128	0.5765	0.5542	0.5765	0.7593
No log	65.0	130	0.5944	0.5946	0.5944	0.7710

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV16_k1_task1_organization_k1_k1_fold0

ASAP_FineTuningBERT_AugV16_k1_task1_organization_k1_k1_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV16_k1_task1_organization_k1_k1_fold0