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	12.1984	-0.0003	12.1984	3.4926
No log	2.0	2	10.6381	0.0053	10.6385	3.2617
No log	3.0	3	9.6734	0.0	9.6738	3.1103
No log	4.0	4	8.6136	0.0	8.6139	2.9349
No log	5.0	5	7.7444	0.0	7.7447	2.7829
No log	6.0	6	7.0761	0.0	7.0764	2.6602
No log	7.0	7	6.3981	0.0	6.3985	2.5295
No log	8.0	8	5.8125	0.0532	5.8129	2.4110
No log	9.0	9	5.2938	0.0247	5.2942	2.3009
No log	10.0	10	4.6974	0.0078	4.6978	2.1674
No log	11.0	11	4.2288	0.0	4.2292	2.0565
No log	12.0	12	3.7717	0.0	3.7720	1.9422
No log	13.0	13	3.3738	0.0	3.3742	1.8369
No log	14.0	14	2.9742	0.0	2.9746	1.7247
No log	15.0	15	2.6645	0.0	2.6648	1.6324
No log	16.0	16	2.3613	0.1524	2.3616	1.5368
No log	17.0	17	2.1489	0.1083	2.1493	1.4661
No log	18.0	18	1.8609	0.0527	1.8613	1.3643
No log	19.0	19	1.7358	0.0590	1.7363	1.3177
No log	20.0	20	1.5261	0.0345	1.5265	1.2355
No log	21.0	21	1.5780	0.0449	1.5784	1.2563
No log	22.0	22	1.4568	0.0345	1.4572	1.2071
No log	23.0	23	1.1972	0.0241	1.1976	1.0943
No log	24.0	24	1.1510	0.0136	1.1515	1.0731
No log	25.0	25	1.0620	0.0136	1.0625	1.0308
No log	26.0	26	0.9451	0.0241	0.9454	0.9723
No log	27.0	27	1.0261	0.0835	1.0264	1.0131
No log	28.0	28	1.0006	0.2042	1.0009	1.0004
No log	29.0	29	0.8440	0.3962	0.8442	0.9188
No log	30.0	30	0.7282	0.4203	0.7284	0.8535
No log	31.0	31	0.7138	0.3764	0.7140	0.8450
No log	32.0	32	0.6874	0.3731	0.6876	0.8292
No log	33.0	33	0.6636	0.3884	0.6637	0.8147
No log	34.0	34	0.6764	0.4429	0.6763	0.8224
No log	35.0	35	0.7719	0.4375	0.7717	0.8784
No log	36.0	36	0.7800	0.4228	0.7797	0.8830
No log	37.0	37	0.7148	0.4054	0.7144	0.8452
No log	38.0	38	0.6643	0.3999	0.6640	0.8149
No log	39.0	39	0.6624	0.3761	0.6622	0.8137
No log	40.0	40	0.6688	0.3662	0.6684	0.8176
No log	41.0	41	0.6830	0.4045	0.6825	0.8262
No log	42.0	42	0.7649	0.4133	0.7642	0.8742
No log	43.0	43	0.8665	0.3919	0.8658	0.9305
No log	44.0	44	0.8434	0.4487	0.8427	0.9180
No log	45.0	45	0.7553	0.5190	0.7546	0.8687
No log	46.0	46	0.7259	0.4849	0.7253	0.8517
No log	47.0	47	0.7585	0.4584	0.7580	0.8706
No log	48.0	48	0.7686	0.4696	0.7679	0.8763
No log	49.0	49	0.7950	0.4981	0.7941	0.8911
No log	50.0	50	0.9105	0.5220	0.9094	0.9536
No log	51.0	51	0.9850	0.4945	0.9839	0.9919
No log	52.0	52	0.9695	0.4922	0.9684	0.9841
No log	53.0	53	0.8956	0.5252	0.8945	0.9458
No log	54.0	54	0.8458	0.5024	0.8448	0.9191
No log	55.0	55	0.8550	0.4478	0.8542	0.9242
No log	56.0	56	0.8704	0.4108	0.8698	0.9327
No log	57.0	57	0.8539	0.4052	0.8533	0.9238
No log	58.0	58	0.8052	0.4488	0.8045	0.8970
No log	59.0	59	0.7782	0.5104	0.7774	0.8817
No log	60.0	60	0.8107	0.5292	0.8098	0.8999
No log	61.0	61	0.8564	0.5262	0.8554	0.9249
No log	62.0	62	0.8641	0.5246	0.8630	0.9290
No log	63.0	63	0.8423	0.5303	0.8413	0.9172
No log	64.0	64	0.8176	0.5092	0.8166	0.9037
No log	65.0	65	0.8037	0.4913	0.8028	0.8960
No log	66.0	66	0.8077	0.4511	0.8070	0.8983
No log	67.0	67	0.7800	0.4648	0.7794	0.8829
No log	68.0	68	0.7639	0.4746	0.7634	0.8737
No log	69.0	69	0.7593	0.5019	0.7587	0.8710
No log	70.0	70	0.7878	0.4996	0.7870	0.8871
No log	71.0	71	0.8601	0.5094	0.8591	0.9269
No log	72.0	72	0.9008	0.5242	0.8996	0.9485
No log	73.0	73	0.9092	0.5183	0.9081	0.9529
No log	74.0	74	0.8956	0.5163	0.8945	0.9458
No log	75.0	75	0.8651	0.4996	0.8641	0.9296
No log	76.0	76	0.8391	0.4953	0.8383	0.9156
No log	77.0	77	0.8228	0.4908	0.8221	0.9067
No log	78.0	78	0.8333	0.4811	0.8327	0.9125
No log	79.0	79	0.8698	0.4797	0.8690	0.9322
No log	80.0	80	0.8763	0.4948	0.8755	0.9357
No log	81.0	81	0.8598	0.5079	0.8590	0.9268
No log	82.0	82	0.8366	0.4837	0.8357	0.9141
No log	83.0	83	0.8279	0.5253	0.8270	0.9094
No log	84.0	84	0.8332	0.5275	0.8323	0.9123
No log	85.0	85	0.8404	0.5356	0.8394	0.9162
No log	86.0	86	0.8427	0.5319	0.8418	0.9175
No log	87.0	87	0.8405	0.5333	0.8396	0.9163
No log	88.0	88	0.8380	0.5229	0.8370	0.9149
No log	89.0	89	0.8361	0.5031	0.8352	0.9139
No log	90.0	90	0.8405	0.5081	0.8396	0.9163
No log	91.0	91	0.8495	0.5092	0.8487	0.9213
No log	92.0	92	0.8586	0.5072	0.8579	0.9262
No log	93.0	93	0.8652	0.5007	0.8645	0.9298
No log	94.0	94	0.8638	0.4960	0.8631	0.9291
No log	95.0	95	0.8655	0.4995	0.8648	0.9299
No log	96.0	96	0.8654	0.5072	0.8647	0.9299
No log	97.0	97	0.8650	0.5132	0.8642	0.9296
No log	98.0	98	0.8665	0.5125	0.8657	0.9304
No log	99.0	99	0.8696	0.5125	0.8688	0.9321
No log	100.0	100	0.8706	0.5129	0.8698	0.9326

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_fold2

Version22NewTestASAP_FineTuningBERT_AugV22_k1_task1_organization_k1_k1_fold2

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_fold2