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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	2	8.5838	0.0	8.5816	2.9294
No log	2.0	4	6.3498	0.0	6.3475	2.5194
No log	3.0	6	4.9582	0.0079	4.9561	2.2262
No log	4.0	8	3.9674	0.0	3.9655	1.9913
No log	5.0	10	2.8327	0.0	2.8309	1.6825
No log	6.0	12	2.1075	0.1036	2.1058	1.4511
No log	7.0	14	1.6435	0.0106	1.6419	1.2814
No log	8.0	16	1.2708	0.0106	1.2692	1.1266
No log	9.0	18	1.0616	0.0	1.0602	1.0297
No log	10.0	20	0.9626	0.0211	0.9613	0.9805
No log	11.0	22	0.9258	0.0622	0.9245	0.9615
No log	12.0	24	1.0792	0.1351	1.0778	1.0382
No log	13.0	26	0.8998	0.0951	0.8985	0.9479
No log	14.0	28	0.8175	0.2918	0.8162	0.9034
No log	15.0	30	0.9516	0.3143	0.9503	0.9748
No log	16.0	32	0.7175	0.4572	0.7163	0.8464
No log	17.0	34	0.6467	0.5222	0.6456	0.8035
No log	18.0	36	0.6695	0.5310	0.6684	0.8176
No log	19.0	38	0.5917	0.4975	0.5906	0.7685
No log	20.0	40	0.6181	0.4592	0.6171	0.7855
No log	21.0	42	0.5564	0.5412	0.5555	0.7453
No log	22.0	44	0.5993	0.5015	0.5983	0.7735
No log	23.0	46	0.5595	0.5425	0.5587	0.7475
No log	24.0	48	0.6072	0.5031	0.6065	0.7788
No log	25.0	50	0.5554	0.5376	0.5547	0.7448
No log	26.0	52	0.5654	0.5866	0.5646	0.7514
No log	27.0	54	0.5926	0.5803	0.5919	0.7693
No log	28.0	56	0.6398	0.5807	0.6391	0.7994
No log	29.0	58	0.7080	0.5583	0.7073	0.8410
No log	30.0	60	0.7289	0.5591	0.7283	0.8534
No log	31.0	62	0.8958	0.4884	0.8951	0.9461
No log	32.0	64	0.6718	0.5911	0.6712	0.8192
No log	33.0	66	0.8731	0.5239	0.8725	0.9341
No log	34.0	68	0.8645	0.5230	0.8640	0.9295
No log	35.0	70	0.7760	0.5539	0.7756	0.8807
No log	36.0	72	0.9121	0.5122	0.9117	0.9548
No log	37.0	74	0.7995	0.5398	0.7992	0.8940
No log	38.0	76	0.9668	0.4962	0.9664	0.9831
No log	39.0	78	0.8621	0.5260	0.8617	0.9283
No log	40.0	80	0.8226	0.5406	0.8224	0.9068
No log	41.0	82	0.6695	0.6081	0.6693	0.8181
No log	42.0	84	0.9261	0.5131	0.9259	0.9622
No log	43.0	86	0.8254	0.5312	0.8252	0.9084
No log	44.0	88	1.1947	0.4461	1.1944	1.0929
No log	45.0	90	1.1738	0.4566	1.1736	1.0833
No log	46.0	92	0.8550	0.5515	0.8548	0.9246
No log	47.0	94	1.2566	0.4487	1.2564	1.1209
No log	48.0	96	1.2274	0.4646	1.2271	1.1078
No log	49.0	98	0.8246	0.5595	0.8244	0.9080
No log	50.0	100	1.0221	0.5060	1.0219	1.0109
No log	51.0	102	1.0078	0.5079	1.0076	1.0038
No log	52.0	104	0.8330	0.5715	0.8329	0.9127
No log	53.0	106	0.9485	0.5392	0.9484	0.9739
No log	54.0	108	0.9270	0.5294	0.9268	0.9627
No log	55.0	110	0.7510	0.5928	0.7509	0.8665
No log	56.0	112	0.8982	0.5361	0.8980	0.9476
No log	57.0	114	1.1284	0.5073	1.1282	1.0622
No log	58.0	116	0.7805	0.5832	0.7804	0.8834
No log	59.0	118	0.7539	0.5996	0.7537	0.8682
No log	60.0	120	1.0602	0.5435	1.0600	1.0296
No log	61.0	122	1.0387	0.5290	1.0386	1.0191
No log	62.0	124	0.7023	0.6062	0.7020	0.8379
No log	63.0	126	0.8680	0.5440	0.8678	0.9315
No log	64.0	128	0.8514	0.5476	0.8511	0.9225
No log	65.0	130	1.0120	0.5070	1.0117	1.0058
No log	66.0	132	0.9896	0.5156	0.9893	0.9947
No log	67.0	134	0.9721	0.5186	0.9718	0.9858
No log	68.0	136	0.9279	0.5373	0.9276	0.9631
No log	69.0	138	0.6934	0.5909	0.6930	0.8325
No log	70.0	140	0.7248	0.5785	0.7245	0.8512
No log	71.0	142	1.0149	0.5344	1.0147	1.0073

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version24NewTestASAP_FineTuningBERT_AugV24_k3_task1_organization_k3_k3_fold1

Version24NewTestASAP_FineTuningBERT_AugV24_k3_task1_organization_k3_k3_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version24NewTestASAP_FineTuningBERT_AugV24_k3_task1_organization_k3_k3_fold1