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	11.1881	0.0105	11.1841	3.3443
No log	2.0	2	8.9318	0.0	8.9291	2.9882
No log	3.0	3	7.4541	0.0	7.4516	2.7298
No log	4.0	4	6.8148	0.0	6.8123	2.6100
No log	5.0	5	6.3409	-0.0038	6.3385	2.5176
No log	6.0	6	5.7511	-0.0060	5.7488	2.3977
No log	7.0	7	5.1010	0.0156	5.0987	2.2580
No log	8.0	8	4.4480	0.0	4.4458	2.1085
No log	9.0	9	3.8642	0.0	3.8622	1.9652
No log	10.0	10	3.4814	0.0	3.4795	1.8653
No log	11.0	11	3.2470	0.0	3.2451	1.8014
No log	12.0	12	2.8997	0.0	2.8978	1.7023
No log	13.0	13	2.5490	0.0074	2.5471	1.5960
No log	14.0	14	2.4076	0.0380	2.4062	1.5512
No log	15.0	15	2.1177	0.0489	2.1161	1.4547
No log	16.0	16	1.9771	0.1008	1.9753	1.4054
No log	17.0	17	1.9128	0.0915	1.9110	1.3824
No log	18.0	18	1.7077	0.0554	1.7059	1.3061
No log	19.0	19	1.4652	0.0211	1.4636	1.2098
No log	20.0	20	1.3876	-0.0052	1.3863	1.1774
No log	21.0	21	1.2345	0.0	1.2331	1.1105
No log	22.0	22	1.1430	0.0	1.1416	1.0684
No log	23.0	23	1.0876	0.0	1.0862	1.0422
No log	24.0	24	0.9840	0.0	0.9826	0.9913
No log	25.0	25	0.9280	0.0500	0.9268	0.9627
No log	26.0	26	0.8950	0.3047	0.8939	0.9455
No log	27.0	27	0.8290	0.4273	0.8278	0.9099
No log	28.0	28	0.8138	0.3915	0.8126	0.9014
No log	29.0	29	0.7845	0.4305	0.7834	0.8851
No log	30.0	30	0.7375	0.4141	0.7364	0.8582
No log	31.0	31	0.7155	0.3264	0.7145	0.8453
No log	32.0	32	0.6712	0.3803	0.6702	0.8187
No log	33.0	33	0.6240	0.4235	0.6230	0.7893
No log	34.0	34	0.5962	0.4577	0.5952	0.7715
No log	35.0	35	0.5749	0.4511	0.5740	0.7576
No log	36.0	36	0.5851	0.4082	0.5842	0.7643
No log	37.0	37	0.5562	0.4507	0.5553	0.7452
No log	38.0	38	0.5360	0.4516	0.5352	0.7316
No log	39.0	39	0.4955	0.4754	0.4947	0.7034
No log	40.0	40	0.4667	0.5256	0.4659	0.6826
No log	41.0	41	0.4525	0.5432	0.4518	0.6722
No log	42.0	42	0.4764	0.5315	0.4757	0.6897
No log	43.0	43	0.4879	0.5768	0.4873	0.6980
No log	44.0	44	0.4699	0.6243	0.4693	0.6851
No log	45.0	45	0.4831	0.6678	0.4825	0.6946
No log	46.0	46	0.4907	0.6618	0.4902	0.7001
No log	47.0	47	0.4476	0.6865	0.4470	0.6686
No log	48.0	48	0.4653	0.6738	0.4648	0.6817
No log	49.0	49	0.5660	0.6364	0.5655	0.7520
No log	50.0	50	0.5598	0.6446	0.5593	0.7479
No log	51.0	51	0.4911	0.6851	0.4906	0.7004
No log	52.0	52	0.5011	0.6877	0.5006	0.7075
No log	53.0	53	0.5213	0.6790	0.5208	0.7217
No log	54.0	54	0.6209	0.6260	0.6205	0.7877
No log	55.0	55	0.6416	0.6334	0.6412	0.8007
No log	56.0	56	0.5417	0.6770	0.5413	0.7357
No log	57.0	57	0.5391	0.6822	0.5387	0.7339
No log	58.0	58	0.5694	0.6668	0.5690	0.7543
No log	59.0	59	0.6070	0.6515	0.6066	0.7788
No log	60.0	60	0.6766	0.6187	0.6761	0.8222
No log	61.0	61	0.6221	0.6461	0.6216	0.7884
No log	62.0	62	0.5969	0.6613	0.5964	0.7723
No log	63.0	63	0.6471	0.6418	0.6466	0.8041
No log	64.0	64	0.6912	0.6281	0.6907	0.8311
No log	65.0	65	0.6127	0.6623	0.6123	0.7825
No log	66.0	66	0.5231	0.6928	0.5227	0.7230
No log	67.0	67	0.4704	0.6761	0.4700	0.6856
No log	68.0	68	0.4777	0.6765	0.4773	0.6909
No log	69.0	69	0.5393	0.6789	0.5388	0.7340
No log	70.0	70	0.6980	0.6262	0.6975	0.8352
No log	71.0	71	0.7638	0.5972	0.7633	0.8737
No log	72.0	72	0.7313	0.6128	0.7309	0.8549
No log	73.0	73	0.6239	0.6673	0.6235	0.7896
No log	74.0	74	0.5334	0.6807	0.5329	0.7300
No log	75.0	75	0.5197	0.6858	0.5192	0.7206
No log	76.0	76	0.5509	0.6879	0.5505	0.7420
No log	77.0	77	0.6400	0.6563	0.6396	0.7997
No log	78.0	78	0.7342	0.6246	0.7338	0.8566
No log	79.0	79	0.7455	0.6329	0.7450	0.8632
No log	80.0	80	0.6813	0.6520	0.6809	0.8252
No log	81.0	81	0.5961	0.6847	0.5957	0.7718
No log	82.0	82	0.5443	0.6995	0.5438	0.7375
No log	83.0	83	0.5398	0.7019	0.5394	0.7344
No log	84.0	84	0.5719	0.6956	0.5715	0.7560
No log	85.0	85	0.6003	0.6864	0.5999	0.7746
No log	86.0	86	0.6143	0.6773	0.6139	0.7835
No log	87.0	87	0.6308	0.6631	0.6304	0.7940
No log	88.0	88	0.6146	0.6676	0.6142	0.7837
No log	89.0	89	0.6181	0.6666	0.6176	0.7859
No log	90.0	90	0.6429	0.6540	0.6424	0.8015
No log	91.0	91	0.6777	0.6369	0.6773	0.8230
No log	92.0	92	0.6821	0.6413	0.6816	0.8256
No log	93.0	93	0.6759	0.6369	0.6754	0.8218
No log	94.0	94	0.6786	0.6400	0.6782	0.8235
No log	95.0	95	0.6788	0.6475	0.6784	0.8236
No log	96.0	96	0.6715	0.6500	0.6711	0.8192
No log	97.0	97	0.6625	0.6559	0.6621	0.8137
No log	98.0	98	0.6582	0.6596	0.6578	0.8111
No log	99.0	99	0.6601	0.6590	0.6597	0.8122
No log	100.0	100	0.6619	0.6566	0.6615	0.8133

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version5ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

Version5ASAP_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/
Version5ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1