ASAP_FineTuningBERT_AugV15_k7_task1_organization_k7_k7_fold1

genki10/ASAP_FineTuningBERT_AugV15_k7_task1_organization_k7_k7_fold1

ASAP_FineTuningBERT_AugV15_k7_task1_organization_k7_k7_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: Mar 3, 2025
Updated: Mar 3, 2025
SHA: b8e4141d

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV15_k7_task1_organization_k7_k7_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: 0.9418
Qwk: 0.5699
Mse: 0.9415
Rmse: 0.9703

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	6	8.0983	0.0	8.0959	2.8453
No log	2.0	12	3.1777	0.0	3.1760	1.7821
No log	3.0	18	1.5834	0.0699	1.5817	1.2576
No log	4.0	24	1.3956	0.0545	1.3940	1.1807
No log	5.0	30	1.5312	0.1256	1.5295	1.2367
No log	6.0	36	1.3755	0.0947	1.3739	1.1721
No log	7.0	42	0.8972	0.1524	0.8959	0.9465
No log	8.0	48	1.0456	0.1001	1.0441	1.0218
No log	9.0	54	0.8185	0.2704	0.8173	0.9040
No log	10.0	60	1.0608	0.1060	1.0594	1.0293
No log	11.0	66	1.0204	0.0958	1.0191	1.0095
No log	12.0	72	0.7787	0.3042	0.7776	0.8818
No log	13.0	78	0.6744	0.4428	0.6734	0.8206
No log	14.0	84	0.5832	0.5120	0.5823	0.7631
No log	15.0	90	0.5695	0.5268	0.5686	0.7541
No log	16.0	96	0.5685	0.4960	0.5678	0.7535
No log	17.0	102	0.6241	0.4410	0.6233	0.7895
No log	18.0	108	0.5188	0.5357	0.5182	0.7198
No log	19.0	114	0.5129	0.5877	0.5123	0.7158
No log	20.0	120	0.6246	0.6043	0.6239	0.7899
No log	21.0	126	0.6022	0.6522	0.6016	0.7756
No log	22.0	132	0.5053	0.6030	0.5048	0.7105
No log	23.0	138	1.3326	0.3298	1.3319	1.1541
No log	24.0	144	0.5085	0.5797	0.5080	0.7127
No log	25.0	150	1.0496	0.4372	1.0490	1.0242
No log	26.0	156	0.9526	0.4874	0.9520	0.9757
No log	27.0	162	0.6017	0.6654	0.6013	0.7755
No log	28.0	168	0.9540	0.4560	0.9533	0.9764
No log	29.0	174	1.1409	0.4235	1.1401	1.0678
No log	30.0	180	0.9882	0.4914	0.9876	0.9938
No log	31.0	186	1.0002	0.5129	0.9998	0.9999
No log	32.0	192	0.9833	0.5281	0.9829	0.9914
No log	33.0	198	1.4066	0.4031	1.4061	1.1858
No log	34.0	204	1.6050	0.3530	1.6044	1.2667
No log	35.0	210	0.6740	0.6564	0.6740	0.8210
No log	36.0	216	1.3885	0.4371	1.3879	1.1781
No log	37.0	222	1.7609	0.2603	1.7607	1.3269
No log	38.0	228	0.6430	0.6259	0.6425	0.8016
No log	39.0	234	1.4401	0.3813	1.4395	1.1998
No log	40.0	240	1.5497	0.3344	1.5493	1.2447
No log	41.0	246	0.6181	0.6681	0.6181	0.7862
No log	42.0	252	0.6812	0.6455	0.6810	0.8252
No log	43.0	258	1.4836	0.2737	1.4838	1.2181
No log	44.0	264	1.3036	0.3827	1.3037	1.1418
No log	45.0	270	0.9508	0.5417	0.9505	0.9749
No log	46.0	276	0.9279	0.5377	0.9276	0.9631
No log	47.0	282	1.1031	0.4840	1.1028	1.0501
No log	48.0	288	1.0917	0.4904	1.0914	1.0447
No log	49.0	294	1.0172	0.5166	1.0171	1.0085
No log	50.0	300	0.8917	0.5414	0.8916	0.9442
No log	51.0	306	1.0754	0.4975	1.0753	1.0370
No log	52.0	312	1.3783	0.4474	1.3779	1.1738
No log	53.0	318	1.4098	0.4573	1.4093	1.1871
No log	54.0	324	1.2237	0.4905	1.2233	1.1060
No log	55.0	330	1.2193	0.4906	1.2192	1.1042
No log	56.0	336	1.0462	0.5207	1.0460	1.0227
No log	57.0	342	0.9620	0.5204	0.9618	0.9807
No log	58.0	348	1.6406	0.3820	1.6405	1.2808
No log	59.0	354	0.9291	0.5371	0.9289	0.9638
No log	60.0	360	0.6382	0.6559	0.6380	0.7988
No log	61.0	366	0.9418	0.5699	0.9415	0.9703

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Mar 3, 2025
Updated: Mar 3, 2025