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	11.2546	0.0297	11.2521	3.3544
No log	2.0	2	9.8276	0.0131	9.8251	3.1345
No log	3.0	3	8.3175	0.0	8.3151	2.8836
No log	4.0	4	6.9905	0.0	6.9882	2.6435
No log	5.0	5	6.1305	0.0114	6.1284	2.4756
No log	6.0	6	5.2940	0.0497	5.2919	2.3004
No log	7.0	7	4.6525	0.0143	4.6504	2.1565
No log	8.0	8	4.0644	0.0079	4.0624	2.0155
No log	9.0	9	3.4948	0.0	3.4929	1.8689
No log	10.0	10	3.0536	0.0	3.0518	1.7469
No log	11.0	11	2.6493	0.0	2.6475	1.6271
No log	12.0	12	2.2739	0.0742	2.2722	1.5074
No log	13.0	13	1.9845	0.0884	1.9828	1.4081
No log	14.0	14	1.7526	0.0453	1.7510	1.3232
No log	15.0	15	1.5415	0.0379	1.5400	1.2410
No log	16.0	16	1.3560	0.0315	1.3544	1.1638
No log	17.0	17	1.2443	0.0315	1.2428	1.1148
No log	18.0	18	1.0951	0.0315	1.0937	1.0458
No log	19.0	19	0.9982	0.0315	0.9968	0.9984
No log	20.0	20	0.9014	0.0545	0.9001	0.9487
No log	21.0	21	0.8521	0.1012	0.8507	0.9223
No log	22.0	22	0.7800	0.4270	0.7787	0.8824
No log	23.0	23	0.7134	0.5013	0.7121	0.8438
No log	24.0	24	0.7309	0.3800	0.7297	0.8542
No log	25.0	25	0.7069	0.4004	0.7057	0.8401
No log	26.0	26	0.5971	0.5161	0.5958	0.7719
No log	27.0	27	0.5744	0.5747	0.5731	0.7570
No log	28.0	28	0.5447	0.5295	0.5435	0.7372
No log	29.0	29	0.5959	0.4699	0.5948	0.7712
No log	30.0	30	0.5874	0.4750	0.5864	0.7657
No log	31.0	31	0.5150	0.5153	0.5139	0.7169
No log	32.0	32	0.5194	0.5086	0.5183	0.7200
No log	33.0	33	0.5897	0.4749	0.5887	0.7672
No log	34.0	34	0.5606	0.5036	0.5597	0.7481
No log	35.0	35	0.4924	0.5680	0.4914	0.7010
No log	36.0	36	0.4922	0.5813	0.4913	0.7009
No log	37.0	37	0.5905	0.5455	0.5896	0.7679
No log	38.0	38	0.6221	0.5279	0.6212	0.7881
No log	39.0	39	0.5732	0.5723	0.5723	0.7565
No log	40.0	40	0.5422	0.6588	0.5413	0.7357
No log	41.0	41	0.6005	0.6071	0.5997	0.7744
No log	42.0	42	0.7593	0.5359	0.7585	0.8709
No log	43.0	43	0.7528	0.5462	0.7521	0.8672
No log	44.0	44	0.6174	0.6085	0.6167	0.7853
No log	45.0	45	0.5403	0.6541	0.5395	0.7345
No log	46.0	46	0.5978	0.6050	0.5971	0.7727
No log	47.0	47	0.7310	0.5632	0.7303	0.8546
No log	48.0	48	0.6993	0.5620	0.6987	0.8359
No log	49.0	49	0.6188	0.6101	0.6181	0.7862
No log	50.0	50	0.6163	0.6326	0.6157	0.7847
No log	51.0	51	0.6465	0.6145	0.6459	0.8037
No log	52.0	52	0.7945	0.5607	0.7939	0.8910
No log	53.0	53	0.7296	0.5789	0.7291	0.8539
No log	54.0	54	0.6152	0.6153	0.6146	0.7839
No log	55.0	55	0.6417	0.5998	0.6410	0.8006
No log	56.0	56	0.8483	0.5448	0.8477	0.9207
No log	57.0	57	0.9592	0.5367	0.9587	0.9791
No log	58.0	58	0.8653	0.5461	0.8648	0.9299
No log	59.0	59	0.6301	0.6171	0.6295	0.7934
No log	60.0	60	0.5849	0.6454	0.5843	0.7644
No log	61.0	61	0.6676	0.6142	0.6671	0.8167
No log	62.0	62	0.9116	0.5496	0.9111	0.9545
No log	63.0	63	1.2143	0.4934	1.2139	1.1018
No log	64.0	64	1.2970	0.4854	1.2966	1.1387
No log	65.0	65	1.1684	0.5145	1.1681	1.0808
No log	66.0	66	0.9000	0.5708	0.8996	0.9485
No log	67.0	67	0.7964	0.5928	0.7959	0.8921
No log	68.0	68	0.8339	0.5821	0.8334	0.9129
No log	69.0	69	0.9940	0.5486	0.9935	0.9967
No log	70.0	70	0.9807	0.5487	0.9802	0.9900

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k1_task1_organization_k1_k1_fold1

Version22NewTestASAP_FineTuningBERT_AugV22_k1_task1_organization_k1_k1_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version22NewTestASAP_FineTuningBERT_AugV22_k1_task1_organization_k1_k1_fold1