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	6	7.9598	0.0	7.9585	2.8211
No log	2.0	12	3.5019	0.0	3.5009	1.8711
No log	3.0	18	1.8598	0.0401	1.8590	1.3635
No log	4.0	24	1.3817	0.0302	1.3810	1.1752
No log	5.0	30	1.5790	0.0463	1.5782	1.2563
No log	6.0	36	1.3562	0.0643	1.3555	1.1643
No log	7.0	42	1.5149	0.1918	1.5147	1.2307
No log	8.0	48	0.8436	0.3930	0.8436	0.9185
No log	9.0	54	0.9092	0.3795	0.9095	0.9537
No log	10.0	60	0.7587	0.4670	0.7595	0.8715
No log	11.0	66	0.6841	0.5011	0.6848	0.8275
No log	12.0	72	0.6996	0.5205	0.6999	0.8366
No log	13.0	78	1.1581	0.4031	1.1588	1.0765
No log	14.0	84	0.9602	0.4804	0.9609	0.9802
No log	15.0	90	0.7011	0.5559	0.7014	0.8375
No log	16.0	96	0.7377	0.5291	0.7380	0.8591
No log	17.0	102	0.7798	0.5311	0.7801	0.8833
No log	18.0	108	0.7133	0.5256	0.7136	0.8447
No log	19.0	114	0.7779	0.5351	0.7783	0.8822
No log	20.0	120	0.7791	0.4977	0.7795	0.8829
No log	21.0	126	0.7090	0.5204	0.7094	0.8423
No log	22.0	132	0.7173	0.5449	0.7177	0.8472
No log	23.0	138	0.8564	0.5121	0.8567	0.9256
No log	24.0	144	0.7250	0.5426	0.7254	0.8517
No log	25.0	150	0.7726	0.5183	0.7729	0.8792
No log	26.0	156	0.7910	0.5325	0.7913	0.8896
No log	27.0	162	0.7180	0.5481	0.7184	0.8476
No log	28.0	168	0.7870	0.5554	0.7875	0.8874
No log	29.0	174	0.7548	0.5423	0.7552	0.8690
No log	30.0	180	0.7003	0.5600	0.7006	0.8370
No log	31.0	186	0.7087	0.5323	0.7090	0.8420
No log	32.0	192	0.7224	0.5590	0.7227	0.8501
No log	33.0	198	0.7005	0.5472	0.7008	0.8371
No log	34.0	204	0.8262	0.5386	0.8267	0.9092
No log	35.0	210	0.7077	0.5325	0.7079	0.8414
No log	36.0	216	0.7618	0.5403	0.7620	0.8729
No log	37.0	222	0.7079	0.5663	0.7082	0.8416
No log	38.0	228	0.6985	0.5505	0.6989	0.8360
No log	39.0	234	0.7329	0.5337	0.7332	0.8563
No log	40.0	240	0.7445	0.5277	0.7448	0.8630
No log	41.0	246	0.7439	0.5489	0.7442	0.8627
No log	42.0	252	0.7423	0.5511	0.7426	0.8617
No log	43.0	258	0.6818	0.5680	0.6820	0.8258
No log	44.0	264	0.6939	0.5736	0.6942	0.8332
No log	45.0	270	0.7291	0.5152	0.7294	0.8540
No log	46.0	276	0.7077	0.5383	0.7081	0.8415
No log	47.0	282	0.7111	0.5592	0.7114	0.8435
No log	48.0	288	0.6443	0.5823	0.6446	0.8028
No log	49.0	294	0.6560	0.5748	0.6563	0.8101
No log	50.0	300	0.6784	0.5592	0.6786	0.8238
No log	51.0	306	0.6603	0.5568	0.6605	0.8127
No log	52.0	312	0.7401	0.5544	0.7404	0.8605
No log	53.0	318	0.6888	0.5381	0.6891	0.8301
No log	54.0	324	0.7094	0.5397	0.7096	0.8424
No log	55.0	330	0.6650	0.5654	0.6652	0.8156
No log	56.0	336	0.7401	0.5030	0.7404	0.8605
No log	57.0	342	0.7201	0.5211	0.7205	0.8488
No log	58.0	348	0.7134	0.5509	0.7137	0.8448
No log	59.0	354	0.6792	0.5718	0.6795	0.8243
No log	60.0	360	0.6818	0.5584	0.6822	0.8259
No log	61.0	366	0.6843	0.5477	0.6847	0.8275
No log	62.0	372	0.6626	0.5705	0.6629	0.8142
No log	63.0	378	0.6863	0.5799	0.6866	0.8286
No log	64.0	384	0.6569	0.5847	0.6572	0.8107
No log	65.0	390	0.6892	0.5513	0.6894	0.8303
No log	66.0	396	0.7029	0.5394	0.7032	0.8385
No log	67.0	402	0.6663	0.5556	0.6665	0.8164
No log	68.0	408	0.6578	0.5669	0.6581	0.8112
No log	69.0	414	0.6683	0.5673	0.6686	0.8177
No log	70.0	420	0.6722	0.5725	0.6725	0.8201
No log	71.0	426	0.6449	0.5804	0.6452	0.8033
No log	72.0	432	0.6582	0.5763	0.6585	0.8115
No log	73.0	438	0.6601	0.5687	0.6604	0.8126
No log	74.0	444	0.6487	0.5745	0.6489	0.8056
No log	75.0	450	0.6654	0.5706	0.6657	0.8159
No log	76.0	456	0.6637	0.5686	0.6639	0.8148
No log	77.0	462	0.6582	0.5806	0.6585	0.8115
No log	78.0	468	0.6624	0.5714	0.6627	0.8141
No log	79.0	474	0.6536	0.5703	0.6539	0.8086
No log	80.0	480	0.6593	0.5595	0.6596	0.8121
No log	81.0	486	0.6642	0.5660	0.6645	0.8152
No log	82.0	492	0.6634	0.5603	0.6637	0.8147
No log	83.0	498	0.6628	0.5604	0.6631	0.8143
0.4048	84.0	504	0.6696	0.5625	0.6699	0.8185

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold3

ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold3