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	10.2879	0.0040	10.2866	3.2073
No log	2.0	4	8.9911	0.0	8.9895	2.9982
No log	3.0	6	7.7165	0.0	7.7150	2.7776
No log	4.0	8	6.2935	0.0310	6.2922	2.5084
No log	5.0	10	5.3253	0.0191	5.3242	2.3074
No log	6.0	12	4.4261	0.0038	4.4251	2.1036
No log	7.0	14	3.6177	0.0	3.6166	1.9017
No log	8.0	16	2.8369	0.0	2.8360	1.6840
No log	9.0	18	2.1781	0.0961	2.1772	1.4755
No log	10.0	20	1.6869	0.0302	1.6862	1.2985
No log	11.0	22	1.5173	0.0302	1.5167	1.2315
No log	12.0	24	1.0916	0.0202	1.0910	1.0445
No log	13.0	26	1.0276	0.0202	1.0271	1.0134
No log	14.0	28	1.3183	0.0202	1.3174	1.1478
No log	15.0	30	1.3362	0.0301	1.3354	1.1556
No log	16.0	32	0.9508	0.1227	0.9503	0.9748
No log	17.0	34	0.8715	0.2882	0.8713	0.9334
No log	18.0	36	0.9710	0.2175	0.9708	0.9853
No log	19.0	38	0.7882	0.4270	0.7883	0.8879
No log	20.0	40	0.7186	0.4602	0.7190	0.8479
No log	21.0	42	0.6895	0.4686	0.6898	0.8306
No log	22.0	44	0.8613	0.4190	0.8612	0.9280
No log	23.0	46	0.6980	0.4794	0.6980	0.8355
No log	24.0	48	0.6258	0.4344	0.6263	0.7914
No log	25.0	50	0.6619	0.4326	0.6626	0.8140
No log	26.0	52	0.5979	0.4789	0.5982	0.7735
No log	27.0	54	0.8497	0.4692	0.8497	0.9218
No log	28.0	56	0.9666	0.4283	0.9667	0.9832
No log	29.0	58	0.6762	0.5189	0.6764	0.8225
No log	30.0	60	0.7211	0.5279	0.7219	0.8496
No log	31.0	62	0.7676	0.4351	0.7685	0.8767
No log	32.0	64	0.6714	0.5714	0.6719	0.8197
No log	33.0	66	0.6412	0.5992	0.6416	0.8010
No log	34.0	68	0.7869	0.5294	0.7872	0.8873
No log	35.0	70	0.7912	0.5519	0.7916	0.8897
No log	36.0	72	0.7445	0.5929	0.7449	0.8631
No log	37.0	74	0.7583	0.5803	0.7586	0.8710
No log	38.0	76	0.8033	0.5718	0.8033	0.8963
No log	39.0	78	0.8649	0.5557	0.8648	0.9299
No log	40.0	80	0.8051	0.5847	0.8049	0.8972
No log	41.0	82	0.7873	0.5758	0.7872	0.8872
No log	42.0	84	0.7710	0.5856	0.7709	0.8780
No log	43.0	86	0.8524	0.5683	0.8522	0.9232
No log	44.0	88	0.9751	0.5246	0.9751	0.9874
No log	45.0	90	0.9158	0.5463	0.9158	0.9570
No log	46.0	92	0.8249	0.5886	0.8249	0.9083
No log	47.0	94	0.8207	0.5914	0.8208	0.9060
No log	48.0	96	0.8205	0.5938	0.8205	0.9058
No log	49.0	98	0.8051	0.5937	0.8050	0.8972
No log	50.0	100	0.8151	0.5990	0.8150	0.9028
No log	51.0	102	0.8540	0.5840	0.8540	0.9241
No log	52.0	104	0.8550	0.5863	0.8549	0.9246
No log	53.0	106	0.8248	0.6051	0.8247	0.9081
No log	54.0	108	0.8244	0.5982	0.8242	0.9078
No log	55.0	110	0.8322	0.6003	0.8319	0.9121
No log	56.0	112	0.8335	0.5910	0.8332	0.9128
No log	57.0	114	0.8086	0.5726	0.8081	0.8990
No log	58.0	116	0.8249	0.5706	0.8243	0.9079
No log	59.0	118	0.8595	0.5559	0.8589	0.9268
No log	60.0	120	0.9812	0.5195	0.9807	0.9903
No log	61.0	122	1.0305	0.5085	1.0300	1.0149
No log	62.0	124	0.9485	0.5535	0.9479	0.9736
No log	63.0	126	0.9526	0.5464	0.9519	0.9757
No log	64.0	128	0.9731	0.5541	0.9724	0.9861
No log	65.0	130	1.0004	0.5401	0.9998	0.9999
No log	66.0	132	1.0297	0.5296	1.0292	1.0145
No log	67.0	134	1.0016	0.5395	1.0011	1.0005
No log	68.0	136	0.9439	0.5544	0.9433	0.9712
No log	69.0	138	0.9265	0.5553	0.9259	0.9622
No log	70.0	140	0.9394	0.5380	0.9388	0.9689
No log	71.0	142	0.9578	0.5315	0.9573	0.9784
No log	72.0	144	0.9399	0.5297	0.9394	0.9692
No log	73.0	146	0.8986	0.5492	0.8981	0.9477

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV17_k1_task1_organization_k1_k1_fold3

ASAP_FineTuningBERT_AugV17_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_AugV17_k1_task1_organization_k1_k1_fold3