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	11.2786	0.0367	11.2760	3.3580
No log	2.0	2	9.7158	0.0091	9.7134	3.1166
No log	3.0	3	8.0799	0.0	8.0775	2.8421
No log	4.0	4	6.9054	0.0	6.9031	2.6274
No log	5.0	5	5.9736	0.0175	5.9715	2.4437
No log	6.0	6	5.2223	0.0480	5.2202	2.2848
No log	7.0	7	4.5482	0.0143	4.5461	2.1322
No log	8.0	8	3.9522	0.0079	3.9502	1.9875
No log	9.0	9	3.3853	0.0	3.3833	1.8394
No log	10.0	10	2.8856	0.0	2.8837	1.6982
No log	11.0	11	2.4961	0.0029	2.4943	1.5794
No log	12.0	12	2.1807	0.1727	2.1789	1.4761
No log	13.0	13	1.8793	0.1031	1.8776	1.3703
No log	14.0	14	1.6618	0.0390	1.6602	1.2885
No log	15.0	15	1.4745	0.0211	1.4729	1.2136
No log	16.0	16	1.3215	0.0211	1.3200	1.1489
No log	17.0	17	1.1611	0.0211	1.1597	1.0769
No log	18.0	18	1.0546	0.0211	1.0532	1.0263
No log	19.0	19	0.9660	0.0211	0.9646	0.9821
No log	20.0	20	0.9085	0.0275	0.9072	0.9525
No log	21.0	21	0.8250	0.4064	0.8237	0.9076
No log	22.0	22	0.7885	0.3633	0.7873	0.8873
No log	23.0	23	0.7434	0.3603	0.7422	0.8615
No log	24.0	24	0.7022	0.3796	0.7010	0.8373
No log	25.0	25	0.6505	0.4047	0.6493	0.8058
No log	26.0	26	0.6234	0.4104	0.6223	0.7889
No log	27.0	27	0.6737	0.3685	0.6727	0.8202
No log	28.0	28	0.6154	0.4044	0.6143	0.7838
No log	29.0	29	0.5106	0.5106	0.5095	0.7138
No log	30.0	30	0.5025	0.5221	0.5015	0.7082
No log	31.0	31	0.5345	0.5181	0.5335	0.7304
No log	32.0	32	0.5875	0.4986	0.5865	0.7658
No log	33.0	33	0.5615	0.5291	0.5606	0.7487
No log	34.0	34	0.4577	0.5961	0.4568	0.6758
No log	35.0	35	0.4714	0.6232	0.4705	0.6859
No log	36.0	36	0.6293	0.6055	0.6285	0.7928
No log	37.0	37	0.8903	0.4385	0.8896	0.9432
No log	38.0	38	0.9078	0.4506	0.9071	0.9524
No log	39.0	39	0.7150	0.5779	0.7143	0.8452
No log	40.0	40	0.5972	0.6456	0.5965	0.7723
No log	41.0	41	0.6267	0.6279	0.6259	0.7912
No log	42.0	42	0.8267	0.5443	0.8260	0.9088
No log	43.0	43	0.8956	0.5371	0.8949	0.9460
No log	44.0	44	0.8102	0.5771	0.8096	0.8998
No log	45.0	45	0.7023	0.6192	0.7017	0.8377
No log	46.0	46	0.6463	0.6301	0.6457	0.8035
No log	47.0	47	0.7513	0.5973	0.7507	0.8664
No log	48.0	48	1.0223	0.5094	1.0217	1.0108
No log	49.0	49	1.2236	0.4629	1.2231	1.1059
No log	50.0	50	1.1251	0.5038	1.1246	1.0605
No log	51.0	51	0.7854	0.5991	0.7849	0.8860
No log	52.0	52	0.5880	0.6781	0.5875	0.7665
No log	53.0	53	0.5942	0.6795	0.5936	0.7705
No log	54.0	54	0.7767	0.5970	0.7761	0.8810
No log	55.0	55	1.1880	0.4852	1.1875	1.0897
No log	56.0	56	1.3395	0.4538	1.3389	1.1571
No log	57.0	57	1.2321	0.4850	1.2315	1.1097
No log	58.0	58	0.9197	0.5496	0.9192	0.9587
No log	59.0	59	0.6676	0.6379	0.6670	0.8167
No log	60.0	60	0.6282	0.6576	0.6276	0.7922
No log	61.0	61	0.7441	0.6266	0.7436	0.8623
No log	62.0	62	1.0640	0.5408	1.0635	1.0313
No log	63.0	63	1.3732	0.4770	1.3727	1.1716
No log	64.0	64	1.4195	0.4808	1.4190	1.1912
No log	65.0	65	1.2336	0.5205	1.2331	1.1104
No log	66.0	66	0.9115	0.5741	0.9110	0.9544
No log	67.0	67	0.7395	0.6291	0.7390	0.8596
No log	68.0	68	0.7275	0.6289	0.7270	0.8526
No log	69.0	69	0.8472	0.5805	0.8466	0.9201
No log	70.0	70	1.1183	0.5324	1.1177	1.0572
No log	71.0	71	1.2154	0.5139	1.2149	1.1022
No log	72.0	72	1.1329	0.5311	1.1323	1.0641
No log	73.0	73	0.9281	0.5568	0.9276	0.9631

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

Version12AGAINNNASAP_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/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1