ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold1

genki10/ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold1

ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold1 is a text classification model from genki10. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Feb 12, 2025
Updated: Feb 12, 2025
SHA: 2d4d0a55

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_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: 0.7237
Qwk: 0.4891
Mse: 0.7228
Rmse: 0.8502

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	11.8126	-0.0012	11.8098	3.4365
No log	2.0	4	8.8194	0.0018	8.8168	2.9693
No log	3.0	6	7.1565	0.0	7.1544	2.6748
No log	4.0	8	5.6503	0.0293	5.6478	2.3765
No log	5.0	10	4.1698	0.0113	4.1677	2.0415
No log	6.0	12	3.0803	0.0	3.0784	1.7545
No log	7.0	14	2.2671	0.0897	2.2656	1.5052
No log	8.0	16	2.1328	0.1153	2.1308	1.4597
No log	9.0	18	1.3536	0.0530	1.3521	1.1628
No log	10.0	20	1.1401	0.0276	1.1389	1.0672
No log	11.0	22	1.3780	0.0812	1.3764	1.1732
No log	12.0	24	1.4768	0.0952	1.4753	1.2146
No log	13.0	26	1.1228	0.0545	1.1214	1.0589
No log	14.0	28	0.7256	0.3948	0.7245	0.8512
No log	15.0	30	0.7811	0.3232	0.7799	0.8831
No log	16.0	32	1.2622	0.1436	1.2607	1.1228
No log	17.0	34	1.2646	0.1810	1.2632	1.1239
No log	18.0	36	0.6563	0.4899	0.6552	0.8095
No log	19.0	38	0.6532	0.4552	0.6522	0.8076
No log	20.0	40	0.7979	0.3931	0.7969	0.8927
No log	21.0	42	1.1143	0.3244	1.1130	1.0550
No log	22.0	44	0.8656	0.3941	0.8645	0.9298
No log	23.0	46	0.8016	0.3268	0.8004	0.8947
No log	24.0	48	0.8369	0.2796	0.8359	0.9142
No log	25.0	50	0.8279	0.3699	0.8268	0.9093
No log	26.0	52	1.0949	0.2851	1.0935	1.0457
No log	27.0	54	0.7944	0.4119	0.7933	0.8907
No log	28.0	56	0.6565	0.4668	0.6555	0.8096
No log	29.0	58	0.6489	0.5001	0.6480	0.8050
No log	30.0	60	1.0923	0.3500	1.0909	1.0445
No log	31.0	62	0.9487	0.3575	0.9474	0.9733
No log	32.0	64	0.7333	0.4160	0.7321	0.8556
No log	33.0	66	0.7510	0.4554	0.7500	0.8660
No log	34.0	68	0.7744	0.4322	0.7733	0.8793
No log	35.0	70	0.7899	0.4452	0.7887	0.8881
No log	36.0	72	0.7386	0.4732	0.7375	0.8588
No log	37.0	74	0.7347	0.4737	0.7337	0.8566
No log	38.0	76	0.6960	0.4760	0.6948	0.8335
No log	39.0	78	0.6836	0.4850	0.6826	0.8262
No log	40.0	80	0.6990	0.5238	0.6979	0.8354
No log	41.0	82	0.8802	0.4463	0.8789	0.9375
No log	42.0	84	0.7950	0.5027	0.7938	0.8909
No log	43.0	86	0.7362	0.5243	0.7353	0.8575
No log	44.0	88	0.6640	0.5501	0.6631	0.8143
No log	45.0	90	0.6441	0.5246	0.6431	0.8019
No log	46.0	92	0.6272	0.5272	0.6262	0.7914
No log	47.0	94	0.6510	0.5273	0.6502	0.8064
No log	48.0	96	0.6522	0.5494	0.6513	0.8070
No log	49.0	98	0.7022	0.5059	0.7013	0.8374
No log	50.0	100	0.7579	0.4811	0.7570	0.8701
No log	51.0	102	0.8038	0.4587	0.8031	0.8962
No log	52.0	104	0.7196	0.4735	0.7188	0.8478
No log	53.0	106	0.7266	0.4595	0.7255	0.8517
No log	54.0	108	0.6815	0.4797	0.6804	0.8249
No log	55.0	110	0.6235	0.5540	0.6226	0.7891
No log	56.0	112	0.6304	0.5521	0.6295	0.7934
No log	57.0	114	0.7442	0.4946	0.7432	0.8621
No log	58.0	116	0.7706	0.4792	0.7695	0.8772
No log	59.0	118	0.6790	0.5286	0.6782	0.8235
No log	60.0	120	0.7019	0.5094	0.7011	0.8373
No log	61.0	122	0.7175	0.4544	0.7166	0.8465
No log	62.0	124	0.7315	0.4712	0.7306	0.8547
No log	63.0	126	0.7546	0.4544	0.7537	0.8682
No log	64.0	128	0.7791	0.4586	0.7784	0.8823
No log	65.0	130	0.7555	0.4653	0.7548	0.8688
No log	66.0	132	0.7038	0.4853	0.7032	0.8386
No log	67.0	134	0.6463	0.5238	0.6456	0.8035
No log	68.0	136	0.6159	0.5478	0.6152	0.7843
No log	69.0	138	0.6024	0.5489	0.6017	0.7757
No log	70.0	140	0.6262	0.5157	0.6255	0.7909
No log	71.0	142	0.6246	0.5449	0.6239	0.7899
No log	72.0	144	0.6546	0.5254	0.6539	0.8087
No log	73.0	146	0.6787	0.5081	0.6780	0.8234
No log	74.0	148	0.6986	0.5024	0.6978	0.8353
No log	75.0	150	0.7237	0.4891	0.7228	0.8502

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Feb 12, 2025
Updated: Feb 12, 2025