Version_concise_ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6314
• Qwk: 0.5673
• Mse: 0.6310
• Rmse: 0.7944

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• 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	4	9.0759	0.0	9.0732	3.0122
No log	2.0	8	5.1924	0.0380	5.1903	2.2782
No log	3.0	12	3.0938	0.0	3.0920	1.7584
No log	4.0	16	2.0498	0.0379	2.0481	1.4311
No log	5.0	20	1.5627	0.0	1.5611	1.2494
No log	6.0	24	1.2314	0.0	1.2299	1.1090
No log	7.0	28	1.5684	0.0211	1.5669	1.2517
No log	8.0	32	1.1449	0.0315	1.1435	1.0693
No log	9.0	36	1.2510	0.1027	1.2495	1.1178
No log	10.0	40	1.2520	0.0972	1.2506	1.1183
No log	11.0	44	0.7563	0.4286	0.7550	0.8689
No log	12.0	48	0.9158	0.2918	0.9143	0.9562
No log	13.0	52	1.2156	0.2427	1.2139	1.1018
No log	14.0	56	0.7052	0.4126	0.7041	0.8391
No log	15.0	60	0.9944	0.3434	0.9930	0.9965
No log	16.0	64	0.6705	0.4787	0.6693	0.8181
No log	17.0	68	0.6490	0.4527	0.6479	0.8049
No log	18.0	72	0.6466	0.4722	0.6456	0.8035
No log	19.0	76	0.6846	0.4632	0.6838	0.8269
No log	20.0	80	0.9050	0.4045	0.9037	0.9507
No log	21.0	84	0.6879	0.5133	0.6869	0.8288
No log	22.0	88	0.9509	0.3829	0.9494	0.9744
No log	23.0	92	0.7990	0.4621	0.7982	0.8934
No log	24.0	96	1.0442	0.3851	1.0427	1.0211
No log	25.0	100	0.9479	0.4245	0.9475	0.9734
No log	26.0	104	0.7584	0.4891	0.7577	0.8705
No log	27.0	108	0.7376	0.4773	0.7367	0.8583
No log	28.0	112	1.3238	0.3392	1.3237	1.1505
No log	29.0	116	0.7661	0.4673	0.7652	0.8748
No log	30.0	120	0.8605	0.4837	0.8600	0.9274
No log	31.0	124	0.9785	0.4690	0.9783	0.9891
No log	32.0	128	0.8292	0.4989	0.8287	0.9103
No log	33.0	132	0.7663	0.5244	0.7656	0.8750
No log	34.0	136	1.1897	0.4035	1.1897	1.0907
No log	35.0	140	0.7333	0.4626	0.7323	0.8557
No log	36.0	144	0.6917	0.5176	0.6910	0.8312
No log	37.0	148	0.9150	0.4324	0.9148	0.9564
No log	38.0	152	0.7155	0.4968	0.7145	0.8453
No log	39.0	156	1.0108	0.4593	1.0104	1.0052
No log	40.0	160	0.7658	0.5399	0.7652	0.8747
No log	41.0	164	0.7358	0.5375	0.7352	0.8575
No log	42.0	168	0.9652	0.4605	0.9650	0.9824
No log	43.0	172	0.6609	0.5254	0.6603	0.8126
No log	44.0	176	0.9928	0.4636	0.9927	0.9964
No log	45.0	180	0.6912	0.5395	0.6907	0.8311
No log	46.0	184	0.7946	0.5292	0.7943	0.8913
No log	47.0	188	0.6701	0.5598	0.6697	0.8183
No log	48.0	192	0.8717	0.5226	0.8715	0.9335
No log	49.0	196	0.6677	0.5466	0.6670	0.8167
No log	50.0	200	0.8499	0.5176	0.8497	0.9218
No log	51.0	204	0.8897	0.4987	0.8896	0.9432
No log	52.0	208	0.6282	0.5419	0.6275	0.7921
No log	53.0	212	1.2012	0.4310	1.2013	1.0960
No log	54.0	216	1.2204	0.4150	1.2204	1.1047
No log	55.0	220	0.6971	0.5080	0.6963	0.8344
No log	56.0	224	0.7239	0.5409	0.7233	0.8505
No log	57.0	228	0.8197	0.5103	0.8195	0.9053
No log	58.0	232	0.5932	0.5743	0.5926	0.7698
No log	59.0	236	0.7753	0.5168	0.7751	0.8804
No log	60.0	240	0.6967	0.5337	0.6964	0.8345
No log	61.0	244	0.6374	0.5700	0.6370	0.7981
No log	62.0	248	0.6016	0.5701	0.6010	0.7753
No log	63.0	252	0.8185	0.5142	0.8184	0.9047
No log	64.0	256	0.7784	0.5255	0.7782	0.8822
No log	65.0	260	0.6558	0.5505	0.6553	0.8095
No log	66.0	264	0.8957	0.4917	0.8957	0.9464
No log	67.0	268	0.7644	0.5035	0.7642	0.8742
No log	68.0	272	0.6337	0.5294	0.6330	0.7956
No log	69.0	276	0.7035	0.5508	0.7033	0.8386
No log	70.0	280	0.7065	0.5483	0.7062	0.8404
No log	71.0	284	0.6405	0.5193	0.6397	0.7998
No log	72.0	288	0.6470	0.5306	0.6466	0.8041
No log	73.0	292	0.8013	0.5023	0.8011	0.8950
No log	74.0	296	0.6322	0.5392	0.6315	0.7947
No log	75.0	300	0.6380	0.5361	0.6374	0.7983
No log	76.0	304	0.7026	0.5561	0.7023	0.8380
No log	77.0	308	0.6170	0.5577	0.6165	0.7852
No log	78.0	312	0.6314	0.5673	0.6310	0.7944

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0