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	13.1885	0.0	13.1864	3.6313
No log	2.0	4	10.7669	0.0059	10.7650	3.2810
No log	3.0	6	8.4436	0.0	8.4421	2.9055
No log	4.0	8	5.7971	0.0226	5.7960	2.4075
No log	5.0	10	5.4633	0.0115	5.4618	2.3371
No log	6.0	12	3.7241	0.0038	3.7230	1.9295
No log	7.0	14	2.9570	0.0	2.9559	1.7193
No log	8.0	16	2.6017	0.0514	2.6007	1.6127
No log	9.0	18	1.8165	0.0926	1.8158	1.3475
No log	10.0	20	1.6824	0.0365	1.6815	1.2967
No log	11.0	22	1.2919	0.0202	1.2912	1.1363
No log	12.0	24	1.0328	0.0202	1.0325	1.0161
No log	13.0	26	0.9925	0.0827	0.9920	0.9960
No log	14.0	28	0.8873	0.1957	0.8869	0.9418
No log	15.0	30	0.7231	0.4079	0.7229	0.8502
No log	16.0	32	0.6911	0.4409	0.6910	0.8313
No log	17.0	34	0.8630	0.2515	0.8630	0.9290
No log	18.0	36	0.8525	0.3122	0.8528	0.9234
No log	19.0	38	0.7414	0.4028	0.7414	0.8611
No log	20.0	40	0.7628	0.3885	0.7628	0.8734
No log	21.0	42	0.6255	0.4080	0.6259	0.7911
No log	22.0	44	0.5801	0.4335	0.5804	0.7618
No log	23.0	46	0.5268	0.5199	0.5268	0.7258
No log	24.0	48	0.5607	0.5699	0.5607	0.7488
No log	25.0	50	0.6374	0.5089	0.6377	0.7986
No log	26.0	52	0.6070	0.5640	0.6072	0.7792
No log	27.0	54	0.5393	0.6282	0.5392	0.7343
No log	28.0	56	0.5472	0.6281	0.5471	0.7397
No log	29.0	58	0.5434	0.6418	0.5434	0.7371
No log	30.0	60	0.6155	0.6021	0.6151	0.7843
No log	31.0	62	0.6120	0.6073	0.6116	0.7821
No log	32.0	64	0.5577	0.6517	0.5575	0.7467
No log	33.0	66	0.5688	0.6556	0.5687	0.7541
No log	34.0	68	0.6636	0.6213	0.6635	0.8145
No log	35.0	70	0.6408	0.6469	0.6407	0.8004
No log	36.0	72	0.6177	0.6059	0.6180	0.7861
No log	37.0	74	0.5936	0.6315	0.5938	0.7706
No log	38.0	76	0.6525	0.6419	0.6523	0.8076
No log	39.0	78	0.8084	0.5911	0.8082	0.8990
No log	40.0	80	0.7355	0.6092	0.7352	0.8574
No log	41.0	82	0.6112	0.6394	0.6112	0.7818
No log	42.0	84	0.6035	0.6520	0.6034	0.7768
No log	43.0	86	0.6248	0.6529	0.6247	0.7904
No log	44.0	88	0.7172	0.6226	0.7169	0.8467
No log	45.0	90	0.6201	0.6587	0.6199	0.7873
No log	46.0	92	0.5721	0.6567	0.5720	0.7563
No log	47.0	94	0.6060	0.6650	0.6058	0.7783
No log	48.0	96	0.7224	0.6170	0.7220	0.8497
No log	49.0	98	0.6754	0.6534	0.6751	0.8216
No log	50.0	100	0.5976	0.6601	0.5975	0.7730
No log	51.0	102	0.6092	0.6513	0.6090	0.7804
No log	52.0	104	0.6515	0.6494	0.6513	0.8070
No log	53.0	106	0.7190	0.6350	0.7186	0.8477
No log	54.0	108	0.7285	0.6238	0.7282	0.8534
No log	55.0	110	0.5705	0.6629	0.5704	0.7553
No log	56.0	112	0.5664	0.6648	0.5662	0.7525
No log	57.0	114	0.6605	0.6402	0.6602	0.8125
No log	58.0	116	0.6619	0.6351	0.6617	0.8134
No log	59.0	118	0.6049	0.6557	0.6047	0.7776
No log	60.0	120	0.5882	0.6774	0.5881	0.7669
No log	61.0	122	0.6409	0.6561	0.6406	0.8004
No log	62.0	124	0.7150	0.6289	0.7148	0.8454
No log	63.0	126	0.7410	0.6189	0.7408	0.8607
No log	64.0	128	0.6526	0.6414	0.6522	0.8076
No log	65.0	130	0.5856	0.6799	0.5853	0.7650
No log	66.0	132	0.5780	0.6747	0.5778	0.7601
No log	67.0	134	0.6432	0.6439	0.6431	0.8019
No log	68.0	136	0.7710	0.5973	0.7705	0.8778
No log	69.0	138	0.7820	0.5962	0.7815	0.8840
No log	70.0	140	0.6752	0.6395	0.6749	0.8215
No log	71.0	142	0.6078	0.6500	0.6075	0.7794
No log	72.0	144	0.6222	0.6516	0.6219	0.7886
No log	73.0	146	0.6850	0.6371	0.6848	0.8275
No log	74.0	148	0.6811	0.6366	0.6809	0.8252
No log	75.0	150	0.6254	0.6498	0.6252	0.7907
No log	76.0	152	0.5823	0.6824	0.5821	0.7629
No log	77.0	154	0.5776	0.6693	0.5775	0.7599
No log	78.0	156	0.6420	0.6496	0.6418	0.8011
No log	79.0	158	0.7030	0.6245	0.7028	0.8383
No log	80.0	160	0.7196	0.6178	0.7194	0.8482
No log	81.0	162	0.6836	0.6312	0.6834	0.8267
No log	82.0	164	0.6118	0.6531	0.6116	0.7820
No log	83.0	166	0.5902	0.6628	0.5900	0.7681
No log	84.0	168	0.6012	0.6558	0.6010	0.7752
No log	85.0	170	0.6377	0.6515	0.6375	0.7984
No log	86.0	172	0.6741	0.6481	0.6738	0.8208
No log	87.0	174	0.6880	0.6414	0.6877	0.8293
No log	88.0	176	0.6659	0.6492	0.6657	0.8159
No log	89.0	178	0.6485	0.6400	0.6483	0.8051
No log	90.0	180	0.6517	0.6439	0.6514	0.8071
No log	91.0	182	0.6487	0.6439	0.6485	0.8053
No log	92.0	184	0.6560	0.6490	0.6557	0.8098
No log	93.0	186	0.6679	0.6502	0.6676	0.8171
No log	94.0	188	0.6672	0.6502	0.6669	0.8166
No log	95.0	190	0.6607	0.6465	0.6605	0.8127
No log	96.0	192	0.6494	0.6510	0.6492	0.8057
No log	97.0	194	0.6375	0.6546	0.6373	0.7983
No log	98.0	196	0.6324	0.6531	0.6322	0.7951
No log	99.0	198	0.6312	0.6517	0.6310	0.7944
No log	100.0	200	0.6308	0.6460	0.6306	0.7941

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold3

ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold3