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	1	11.5368	0.0	11.5368	3.3966
No log	2.0	2	10.2552	0.0031	10.2552	3.2024
No log	3.0	3	9.0998	0.0	9.0998	3.0166
No log	4.0	4	7.7654	0.0	7.7654	2.7866
No log	5.0	5	7.1344	0.0	7.1344	2.6710
No log	6.0	6	6.5444	0.0	6.5444	2.5582
No log	7.0	7	5.5546	0.0100	5.5546	2.3568
No log	8.0	8	5.1105	0.0383	5.1105	2.2606
No log	9.0	9	4.5000	0.0	4.5000	2.1213
No log	10.0	10	4.2878	0.0	4.2878	2.0707
No log	11.0	11	4.0839	0.0	4.0839	2.0209
No log	12.0	12	3.5268	0.0	3.5268	1.8780
No log	13.0	13	3.0634	0.0	3.0634	1.7503
No log	14.0	14	2.7882	0.0	2.7882	1.6698
No log	15.0	15	2.5637	0.0	2.5637	1.6011
No log	16.0	16	2.3308	-0.0080	2.3308	1.5267
No log	17.0	17	2.1137	-0.0284	2.1137	1.4538
No log	18.0	18	1.8409	0.0472	1.8409	1.3568
No log	19.0	19	1.6327	0.0	1.6327	1.2778
No log	20.0	20	1.5010	0.0	1.5010	1.2251
No log	21.0	21	1.3384	0.0	1.3384	1.1569
No log	22.0	22	1.2374	0.0	1.2374	1.1124
No log	23.0	23	1.1248	0.0	1.1248	1.0605
No log	24.0	24	1.0697	0.0	1.0697	1.0343
No log	25.0	25	0.9827	0.0	0.9827	0.9913
No log	26.0	26	1.0218	-0.0777	1.0218	1.0108
No log	27.0	27	0.9719	-0.0256	0.9719	0.9859
No log	28.0	28	0.8576	0.3125	0.8576	0.9261
No log	29.0	29	0.8861	0.1720	0.8861	0.9413
No log	30.0	30	1.0279	0.0564	1.0279	1.0138
No log	31.0	31	1.0476	0.0564	1.0476	1.0235
No log	32.0	32	0.9603	0.1379	0.9603	0.9799
No log	33.0	33	0.8509	0.1720	0.8509	0.9224
No log	34.0	34	0.8381	0.1720	0.8381	0.9155
No log	35.0	35	0.8979	0.1720	0.8979	0.9476
No log	36.0	36	0.8449	0.2398	0.8449	0.9192
No log	37.0	37	0.6735	0.3277	0.6735	0.8207
No log	38.0	38	0.6576	0.3606	0.6576	0.8109
No log	39.0	39	0.8206	0.3191	0.8206	0.9059
No log	40.0	40	0.8669	0.3312	0.8669	0.9311
No log	41.0	41	0.7051	0.3816	0.7051	0.8397
No log	42.0	42	0.5015	0.4444	0.5015	0.7082
No log	43.0	43	0.4719	0.4554	0.4719	0.6870
No log	44.0	44	0.5704	0.4503	0.5704	0.7552
No log	45.0	45	0.8419	0.3740	0.8419	0.9176
No log	46.0	46	0.8708	0.3438	0.8708	0.9332
No log	47.0	47	0.7147	0.4799	0.7147	0.8454
No log	48.0	48	0.4549	0.6186	0.4549	0.6745
No log	49.0	49	0.3905	0.7316	0.3905	0.6249
No log	50.0	50	0.4094	0.6986	0.4094	0.6398
No log	51.0	51	0.5159	0.6599	0.5159	0.7183
No log	52.0	52	0.5328	0.6486	0.5328	0.7299
No log	53.0	53	0.4267	0.6781	0.4267	0.6533
No log	54.0	54	0.4161	0.7006	0.4161	0.6451
No log	55.0	55	0.5293	0.6732	0.5293	0.7275
No log	56.0	56	0.5484	0.6732	0.5484	0.7406
No log	57.0	57	0.5628	0.6790	0.5628	0.7502
No log	58.0	58	0.4910	0.6918	0.4910	0.7007
No log	59.0	59	0.5706	0.6951	0.5706	0.7554
No log	60.0	60	0.8546	0.6145	0.8546	0.9245
No log	61.0	61	0.9303	0.6069	0.9303	0.9645
No log	62.0	62	0.7840	0.6313	0.7840	0.8855
No log	63.0	63	0.7477	0.6477	0.7477	0.8647
No log	64.0	64	0.6892	0.6659	0.6892	0.8302
No log	65.0	65	0.4887	0.7009	0.4887	0.6991
No log	66.0	66	0.4819	0.7073	0.4819	0.6942
No log	67.0	67	0.6158	0.6922	0.6158	0.7847
No log	68.0	68	0.9186	0.6162	0.9186	0.9585
No log	69.0	69	1.0581	0.5860	1.0581	1.0286

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12LowDevASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

Version12LowDevASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12LowDevASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1