ASAP_FineTuningBERT_AugV8_k2_task1_organization_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: 0.7766
• Qwk: 0.4185
• Mse: 0.7768
• Rmse: 0.8814

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	11.9591	0.0025	11.9586	3.4581
No log	2.0	4	9.7474	0.0	9.7471	3.1220
No log	3.0	6	7.7343	0.0	7.7341	2.7810
No log	4.0	8	5.9549	0.0389	5.9546	2.4402
7.4702	5.0	10	4.7473	0.0078	4.7470	2.1788
7.4702	6.0	12	3.7200	0.0039	3.7199	1.9287
7.4702	7.0	14	2.9676	0.0	2.9675	1.7226
7.4702	8.0	16	2.3986	0.0934	2.3986	1.5487
7.4702	9.0	18	1.9058	0.0280	1.9059	1.3805
2.8489	10.0	20	1.5325	0.0107	1.5326	1.2380
2.8489	11.0	22	1.3021	0.0213	1.3023	1.1412
2.8489	12.0	24	1.0725	0.0280	1.0726	1.0357
2.8489	13.0	26	0.9227	0.0345	0.9229	0.9607
2.8489	14.0	28	1.1453	0.1479	1.1455	1.0703
1.9261	15.0	30	0.9498	0.2467	0.9501	0.9747
1.9261	16.0	32	0.7541	0.4768	0.7544	0.8686
1.9261	17.0	34	1.5407	0.2110	1.5409	1.2413
1.9261	18.0	36	1.8660	0.2053	1.8662	1.3661
1.9261	19.0	38	0.7939	0.4493	0.7941	0.8911
1.7031	20.0	40	0.6643	0.4938	0.6645	0.8152
1.7031	21.0	42	0.8474	0.4193	0.8477	0.9207
1.7031	22.0	44	0.6632	0.4971	0.6635	0.8146
1.7031	23.0	46	0.6176	0.4525	0.6179	0.7861
1.7031	24.0	48	0.8576	0.4078	0.8579	0.9262
1.0944	25.0	50	0.8269	0.4352	0.8272	0.9095
1.0944	26.0	52	0.6772	0.4104	0.6776	0.8232
1.0944	27.0	54	0.7124	0.4313	0.7129	0.8443
1.0944	28.0	56	0.7635	0.3970	0.7638	0.8740
1.0944	29.0	58	0.7226	0.3928	0.7229	0.8502
0.5972	30.0	60	0.7645	0.4076	0.7647	0.8745
0.5972	31.0	62	0.8804	0.4273	0.8807	0.9384
0.5972	32.0	64	0.7766	0.4185	0.7768	0.8814

Framework versions

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