Version_weird_ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_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: 0.8226
• Qwk: 0.5863
• Mse: 0.8223
• Rmse: 0.9068

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	3	9.6176	0.0	9.6157	3.1009
No log	2.0	6	9.0342	0.0	9.0324	3.0054
No log	3.0	9	7.6983	0.0	7.6968	2.7743
No log	4.0	12	6.2388	0.0050	6.2372	2.4974
No log	5.0	15	4.8576	0.0038	4.8565	2.2037
No log	6.0	18	3.6702	0.0	3.6692	1.9155
No log	7.0	21	3.0799	0.0	3.0788	1.7546
No log	8.0	24	2.4312	0.0864	2.4304	1.5590
No log	9.0	27	1.9404	0.0102	1.9397	1.3927
No log	10.0	30	1.5533	0.0	1.5526	1.2460
No log	11.0	33	1.2229	0.0	1.2224	1.1056
No log	12.0	36	1.0382	0.0	1.0378	1.0187
No log	13.0	39	0.9154	0.0641	0.9150	0.9566
No log	14.0	42	0.9088	0.0157	0.9086	0.9532
No log	15.0	45	0.9578	0.0157	0.9576	0.9786
No log	16.0	48	1.1753	0.0157	1.1753	1.0841
No log	17.0	51	1.5135	0.0999	1.5136	1.2303
No log	18.0	54	1.8952	0.0625	1.8953	1.3767
No log	19.0	57	2.3602	0.0732	2.3603	1.5363
No log	20.0	60	1.7138	0.2313	1.7135	1.3090
No log	21.0	63	0.7374	0.4815	0.7369	0.8584
No log	22.0	66	0.9549	0.5395	0.9539	0.9767
No log	23.0	69	1.5020	0.2868	1.5015	1.2253
No log	24.0	72	0.9024	0.5656	0.9020	0.9498
No log	25.0	75	0.8631	0.5901	0.8630	0.9290
No log	26.0	78	0.8089	0.5093	0.8093	0.8996
No log	27.0	81	1.0124	0.5700	1.0122	1.0061
No log	28.0	84	0.7005	0.5818	0.7008	0.8372
No log	29.0	87	1.4149	0.4832	1.4143	1.1892
No log	30.0	90	1.2705	0.5018	1.2701	1.1270
No log	31.0	93	0.6857	0.5888	0.6859	0.8282
No log	32.0	96	0.8176	0.5889	0.8175	0.9041
No log	33.0	99	0.7382	0.5364	0.7384	0.8593
No log	34.0	102	0.7541	0.5390	0.7543	0.8685
No log	35.0	105	0.8878	0.5836	0.8875	0.9421
No log	36.0	108	0.7505	0.6056	0.7505	0.8663
No log	37.0	111	0.8580	0.5944	0.8577	0.9261
No log	38.0	114	0.7840	0.5979	0.7839	0.8854
No log	39.0	117	1.0089	0.5661	1.0085	1.0042
No log	40.0	120	0.8573	0.5952	0.8572	0.9258
No log	41.0	123	0.9075	0.5916	0.9073	0.9525
No log	42.0	126	0.8167	0.5919	0.8166	0.9037
No log	43.0	129	0.9390	0.5661	0.9387	0.9688
No log	44.0	132	0.7460	0.5844	0.7460	0.8637
No log	45.0	135	0.8169	0.5906	0.8167	0.9037
No log	46.0	138	1.1277	0.5522	1.1271	1.0617
No log	47.0	141	1.0491	0.5633	1.0487	1.0240
No log	48.0	144	0.7677	0.6099	0.7677	0.8762
No log	49.0	147	0.8054	0.5937	0.8052	0.8974
No log	50.0	150	0.8206	0.5889	0.8204	0.9057
No log	51.0	153	1.0684	0.5532	1.0680	1.0334
No log	52.0	156	0.9359	0.5771	0.9355	0.9672
No log	53.0	159	0.8065	0.5328	0.8067	0.8982
No log	54.0	162	0.9316	0.5104	0.9319	0.9653
No log	55.0	165	0.8563	0.5599	0.8563	0.9254
No log	56.0	168	0.8735	0.5963	0.8732	0.9345
No log	57.0	171	0.7610	0.6016	0.7609	0.8723
No log	58.0	174	0.8378	0.6028	0.8376	0.9152
No log	59.0	177	0.7624	0.6259	0.7622	0.8731
No log	60.0	180	0.7651	0.6197	0.7649	0.8746
No log	61.0	183	0.9582	0.5623	0.9578	0.9787
No log	62.0	186	0.8779	0.5882	0.8777	0.9369
No log	63.0	189	0.7897	0.5961	0.7897	0.8886
No log	64.0	192	0.8434	0.5899	0.8433	0.9183
No log	65.0	195	0.9232	0.5976	0.9229	0.9607
No log	66.0	198	0.8557	0.6068	0.8554	0.9249
No log	67.0	201	0.7793	0.5991	0.7791	0.8827
No log	68.0	204	0.7721	0.6002	0.7720	0.8786
No log	69.0	207	0.8492	0.5981	0.8489	0.9214
No log	70.0	210	0.7710	0.6096	0.7708	0.8779
No log	71.0	213	0.7424	0.6124	0.7423	0.8616
No log	72.0	216	0.7876	0.6079	0.7874	0.8874
No log	73.0	219	0.8770	0.5981	0.8766	0.9363
No log	74.0	222	0.8134	0.5940	0.8131	0.9017
No log	75.0	225	0.7618	0.6071	0.7616	0.8727
No log	76.0	228	0.7517	0.6116	0.7515	0.8669
No log	77.0	231	0.7969	0.6057	0.7966	0.8925
No log	78.0	234	0.8091	0.5913	0.8088	0.8993
No log	79.0	237	0.8226	0.5863	0.8223	0.9068

Framework versions

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