Version2ASAP_FineTuningBERT_AugV8_k3_task1_organization_k3_k3_fold2

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: 0.6891
• Qwk: 0.5109
• Mse: 0.6884
• Rmse: 0.8297

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	10.1394	0.0036	10.1397	3.1843
No log	2.0	6	6.8463	0.0	6.8466	2.6166
No log	3.0	9	4.4814	0.0074	4.4818	2.1170
No log	4.0	12	3.0081	0.0	3.0085	1.7345
No log	5.0	15	1.9023	0.0640	1.9028	1.3794
No log	6.0	18	1.3882	0.0213	1.3887	1.1784
No log	7.0	21	1.0474	0.0213	1.0478	1.0236
No log	8.0	24	1.1547	0.0345	1.1549	1.0747
No log	9.0	27	1.1051	0.0345	1.1054	1.0514
No log	10.0	30	1.2744	0.0765	1.2747	1.1290
No log	11.0	33	1.0249	0.1066	1.0252	1.0125
No log	12.0	36	1.0643	0.1910	1.0646	1.0318
No log	13.0	39	0.7428	0.4171	0.7430	0.8620
No log	14.0	42	0.8825	0.1368	0.8828	0.9396
No log	15.0	45	0.7802	0.3654	0.7805	0.8834
No log	16.0	48	0.6879	0.3226	0.6881	0.8295
No log	17.0	51	0.7548	0.4542	0.7550	0.8689
No log	18.0	54	0.6653	0.4383	0.6653	0.8157
No log	19.0	57	0.7026	0.3785	0.7026	0.8382
No log	20.0	60	0.7653	0.3928	0.7653	0.8748
No log	21.0	63	0.6897	0.4167	0.6896	0.8304
No log	22.0	66	0.6747	0.5163	0.6747	0.8214
No log	23.0	69	0.6816	0.5018	0.6814	0.8255
No log	24.0	72	0.6703	0.5146	0.6699	0.8185
No log	25.0	75	0.6793	0.5350	0.6789	0.8240
No log	26.0	78	0.7377	0.5234	0.7374	0.8587
No log	27.0	81	0.7165	0.5448	0.7162	0.8463
No log	28.0	84	0.7230	0.5424	0.7225	0.8500
No log	29.0	87	0.7511	0.5049	0.7503	0.8662
No log	30.0	90	0.7277	0.5150	0.7271	0.8527
No log	31.0	93	0.9566	0.4438	0.9556	0.9775
No log	32.0	96	0.7242	0.5438	0.7233	0.8505
No log	33.0	99	0.8182	0.5003	0.8173	0.9040
No log	34.0	102	0.7449	0.5162	0.7444	0.8628
No log	35.0	105	0.7986	0.5056	0.7979	0.8932
No log	36.0	108	0.7219	0.5484	0.7213	0.8493
No log	37.0	111	0.8078	0.5095	0.8072	0.8984
No log	38.0	114	0.7214	0.5401	0.7208	0.8490
No log	39.0	117	0.6936	0.5435	0.6930	0.8325
No log	40.0	120	0.6865	0.5396	0.6858	0.8281
No log	41.0	123	0.7113	0.5449	0.7106	0.8430
No log	42.0	126	0.7349	0.5146	0.7342	0.8569
No log	43.0	129	0.7184	0.5273	0.7177	0.8472
No log	44.0	132	0.6941	0.5332	0.6936	0.8328
No log	45.0	135	0.6881	0.5259	0.6874	0.8291
No log	46.0	138	0.6894	0.5516	0.6890	0.8300
No log	47.0	141	0.6444	0.5470	0.6439	0.8024
No log	48.0	144	0.6536	0.5506	0.6533	0.8082
No log	49.0	147	0.6894	0.4992	0.6889	0.8300
No log	50.0	150	0.6885	0.5063	0.6881	0.8295
No log	51.0	153	0.7050	0.4929	0.7045	0.8394
No log	52.0	156	0.6801	0.5286	0.6799	0.8245
No log	53.0	159	0.6716	0.5357	0.6711	0.8192
No log	54.0	162	0.6901	0.5452	0.6897	0.8305
No log	55.0	165	0.6673	0.5527	0.6667	0.8165
No log	56.0	168	0.6947	0.5293	0.6942	0.8332
No log	57.0	171	0.6801	0.5273	0.6795	0.8243
No log	58.0	174	0.6940	0.5258	0.6933	0.8326
No log	59.0	177	0.7052	0.5092	0.7045	0.8393
No log	60.0	180	0.6943	0.5125	0.6937	0.8329
No log	61.0	183	0.6813	0.5274	0.6806	0.8250
No log	62.0	186	0.6860	0.5363	0.6852	0.8278
No log	63.0	189	0.6905	0.5512	0.6897	0.8305
No log	64.0	192	0.6748	0.5511	0.6741	0.8210
No log	65.0	195	0.6785	0.5266	0.6779	0.8233
No log	66.0	198	0.7156	0.5219	0.7149	0.8455
No log	67.0	201	0.6964	0.4877	0.6958	0.8341
No log	68.0	204	0.7093	0.4884	0.7087	0.8418
No log	69.0	207	0.7017	0.5002	0.7010	0.8373
No log	70.0	210	0.6577	0.5151	0.6572	0.8107
No log	71.0	213	0.6722	0.5205	0.6715	0.8195
No log	72.0	216	0.6724	0.5045	0.6718	0.8197
No log	73.0	219	0.6988	0.5049	0.6981	0.8355
No log	74.0	222	0.7048	0.5074	0.7041	0.8391
No log	75.0	225	0.6891	0.5109	0.6884	0.8297

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.3.1
• Tokenizers 0.21.0