Version_weird_ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

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: 0.6884
• Qwk: 0.6378
• Mse: 0.6879
• Rmse: 0.8294

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	2	11.8411	-0.0280	11.8388	3.4408
No log	2.0	4	9.9786	-0.0008	9.9768	3.1586
No log	3.0	6	8.1487	0.0	8.1469	2.8543
No log	4.0	8	6.6969	0.0	6.6952	2.5875
No log	5.0	10	5.6451	0.0166	5.6438	2.3757
No log	6.0	12	4.6807	0.0038	4.6795	2.1632
No log	7.0	14	3.8214	0.0	3.8203	1.9546
No log	8.0	16	3.0570	0.0	3.0560	1.7481
No log	9.0	18	2.4563	0.1492	2.4554	1.5670
No log	10.0	20	1.9425	0.0202	1.9416	1.3934
No log	11.0	22	1.5149	0.0202	1.5141	1.2305
No log	12.0	24	1.2024	0.0102	1.2018	1.0963
No log	13.0	26	0.9718	0.0102	0.9713	0.9855
No log	14.0	28	0.8796	0.2722	0.8792	0.9376
No log	15.0	30	0.8422	0.1454	0.8418	0.9175
No log	16.0	32	0.8392	0.0694	0.8389	0.9159
No log	17.0	34	0.8166	0.1596	0.8164	0.9035
No log	18.0	36	0.8480	0.1957	0.8478	0.9208
No log	19.0	38	0.6988	0.3640	0.6987	0.8359
No log	20.0	40	0.6543	0.4189	0.6543	0.8089
No log	21.0	42	0.6727	0.5195	0.6728	0.8202
No log	22.0	44	0.6913	0.5580	0.6914	0.8315
No log	23.0	46	0.7161	0.5887	0.7160	0.8462
No log	24.0	48	0.7537	0.5982	0.7535	0.8680
No log	25.0	50	0.7787	0.5646	0.7785	0.8823
No log	26.0	52	0.8045	0.5589	0.8042	0.8968
No log	27.0	54	0.8682	0.5975	0.8677	0.9315
No log	28.0	56	0.7792	0.5610	0.7789	0.8825
No log	29.0	58	0.7150	0.6100	0.7146	0.8453
No log	30.0	60	0.9378	0.5606	0.9371	0.9680
No log	31.0	62	0.7212	0.6190	0.7207	0.8490
No log	32.0	64	0.7669	0.5611	0.7665	0.8755
No log	33.0	66	0.9732	0.5421	0.9723	0.9860
No log	34.0	68	1.2088	0.5054	1.2079	1.0990
No log	35.0	70	0.6971	0.6143	0.6966	0.8346
No log	36.0	72	0.6469	0.6414	0.6465	0.8040
No log	37.0	74	0.7697	0.6123	0.7690	0.8769
No log	38.0	76	0.7532	0.6151	0.7525	0.8675
No log	39.0	78	0.6917	0.6164	0.6912	0.8314
No log	40.0	80	0.6870	0.6368	0.6864	0.8285
No log	41.0	82	0.6382	0.6378	0.6377	0.7986
No log	42.0	84	0.6742	0.6273	0.6737	0.8208
No log	43.0	86	0.6300	0.6417	0.6296	0.7935
No log	44.0	88	0.6357	0.6435	0.6352	0.7970
No log	45.0	90	0.7985	0.6052	0.7979	0.8933
No log	46.0	92	0.7070	0.6111	0.7065	0.8405
No log	47.0	94	0.7545	0.6034	0.7540	0.8683
No log	48.0	96	0.7686	0.6061	0.7680	0.8764
No log	49.0	98	0.7335	0.6310	0.7330	0.8562
No log	50.0	100	0.7875	0.6226	0.7869	0.8871
No log	51.0	102	0.7215	0.6172	0.7210	0.8491
No log	52.0	104	0.7103	0.6159	0.7098	0.8425
No log	53.0	106	0.7645	0.6012	0.7639	0.8740
No log	54.0	108	0.6865	0.6244	0.6860	0.8282
No log	55.0	110	0.7578	0.5984	0.7572	0.8702
No log	56.0	112	0.6662	0.6460	0.6657	0.8159
No log	57.0	114	0.6616	0.6500	0.6612	0.8131
No log	58.0	116	0.6928	0.6375	0.6923	0.8321
No log	59.0	118	0.6599	0.6411	0.6594	0.8121
No log	60.0	120	0.6505	0.6582	0.6501	0.8063
No log	61.0	122	0.7237	0.5967	0.7232	0.8504
No log	62.0	124	0.6983	0.6121	0.6978	0.8353
No log	63.0	126	0.6479	0.6406	0.6475	0.8047
No log	64.0	128	0.6878	0.6342	0.6873	0.8291
No log	65.0	130	0.7415	0.6114	0.7409	0.8608
No log	66.0	132	0.7204	0.6251	0.7198	0.8484
No log	67.0	134	0.6598	0.6252	0.6593	0.8120
No log	68.0	136	0.6659	0.6027	0.6655	0.8158
No log	69.0	138	0.6622	0.6487	0.6617	0.8135
No log	70.0	140	0.7494	0.6068	0.7488	0.8653
No log	71.0	142	0.7290	0.6126	0.7285	0.8535
No log	72.0	144	0.6421	0.6600	0.6416	0.8010
No log	73.0	146	0.6170	0.6322	0.6166	0.7853
No log	74.0	148	0.6108	0.6510	0.6104	0.7813
No log	75.0	150	0.6383	0.6579	0.6378	0.7987
No log	76.0	152	0.7009	0.6266	0.7004	0.8369
No log	77.0	154	0.7044	0.6275	0.7038	0.8389
No log	78.0	156	0.6650	0.6468	0.6646	0.8152
No log	79.0	158	0.6701	0.6406	0.6696	0.8183
No log	80.0	160	0.6516	0.6534	0.6511	0.8069
No log	81.0	162	0.6612	0.6494	0.6608	0.8129
No log	82.0	164	0.6858	0.6296	0.6854	0.8279
No log	83.0	166	0.6991	0.6282	0.6986	0.8358
No log	84.0	168	0.6865	0.6367	0.6861	0.8283
No log	85.0	170	0.6643	0.6553	0.6639	0.8148
No log	86.0	172	0.6597	0.6549	0.6592	0.8119
No log	87.0	174	0.6722	0.6322	0.6717	0.8196
No log	88.0	176	0.6652	0.6408	0.6648	0.8153
No log	89.0	178	0.6726	0.6346	0.6721	0.8198
No log	90.0	180	0.6629	0.6513	0.6624	0.8139
No log	91.0	182	0.6664	0.6470	0.6660	0.8161
No log	92.0	184	0.6884	0.6378	0.6879	0.8294

Framework versions

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