ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

ASAP_nosemantic_FineTuningBERT_AugV12_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: Mar 6, 2025
Updated: Mar 7, 2025
SHA: 37674f1e

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Base models

Model card

From the Hugging Face model README

ASAP_nosemantic_FineTuningBERT_AugV12_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: 0.7688
Qwk: 0.5555
Mse: 0.7682
Rmse: 0.8765

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	1	12.1684	-0.0034	12.1656	3.4879
No log	2.0	2	10.9580	-0.0172	10.9554	3.3099
No log	3.0	3	9.5406	-0.0002	9.5381	3.0884
No log	4.0	4	8.4453	0.0	8.4429	2.9057
No log	5.0	5	7.5392	0.0	7.5370	2.7454
No log	6.0	6	6.6957	0.0	6.6935	2.5872
No log	7.0	7	6.0771	0.0	6.0748	2.4647
No log	8.0	8	5.5330	0.0119	5.5308	2.3518
No log	9.0	9	4.7982	0.0128	4.7960	2.1900
No log	10.0	10	4.2562	0.0040	4.2541	2.0625
No log	11.0	11	3.8194	0.0	3.8174	1.9538
No log	12.0	12	3.3880	0.0	3.3860	1.8401
No log	13.0	13	3.1485	0.0	3.1465	1.7738
No log	14.0	14	2.8747	0.0	2.8729	1.6950
No log	15.0	15	2.4423	-0.0003	2.4405	1.5622
No log	16.0	16	2.1406	0.1064	2.1389	1.4625
No log	17.0	17	1.9135	0.0645	1.9118	1.3827
No log	18.0	18	1.7298	0.0521	1.7282	1.3146
No log	19.0	19	1.5780	0.0106	1.5764	1.2555
No log	20.0	20	1.4495	0.0	1.4479	1.2033
No log	21.0	21	1.3155	0.0	1.3140	1.1463
No log	22.0	22	1.1895	0.0106	1.1881	1.0900
No log	23.0	23	1.1024	0.0106	1.1010	1.0493
No log	24.0	24	1.0402	0.0106	1.0389	1.0192
No log	25.0	25	0.9941	0.0106	0.9927	0.9963
No log	26.0	26	0.9133	0.0106	0.9120	0.9550
No log	27.0	27	0.8281	0.2445	0.8268	0.9093
No log	28.0	28	0.7723	0.4981	0.7711	0.8781
No log	29.0	29	0.7534	0.4705	0.7522	0.8673
No log	30.0	30	0.6829	0.5062	0.6817	0.8256
No log	31.0	31	0.6371	0.5438	0.6359	0.7975
No log	32.0	32	0.6101	0.5447	0.6089	0.7803
No log	33.0	33	0.5696	0.5614	0.5685	0.7540
No log	34.0	34	0.5802	0.5067	0.5791	0.7610
No log	35.0	35	0.5412	0.5541	0.5401	0.7349
No log	36.0	36	0.5475	0.5998	0.5465	0.7392
No log	37.0	37	0.5502	0.5966	0.5492	0.7411
No log	38.0	38	0.5021	0.5984	0.5011	0.7079
No log	39.0	39	0.5014	0.5487	0.5004	0.7074
No log	40.0	40	0.5017	0.5462	0.5007	0.7076
No log	41.0	41	0.5011	0.5832	0.5001	0.7072
No log	42.0	42	0.5025	0.5531	0.5015	0.7081
No log	43.0	43	0.5273	0.5225	0.5263	0.7255
No log	44.0	44	0.5493	0.5120	0.5483	0.7405
No log	45.0	45	0.5400	0.5102	0.5391	0.7342
No log	46.0	46	0.5769	0.4933	0.5759	0.7589
No log	47.0	47	0.5677	0.5083	0.5668	0.7529
No log	48.0	48	0.5378	0.5614	0.5369	0.7328
No log	49.0	49	0.5635	0.5626	0.5626	0.7501
No log	50.0	50	0.6589	0.5336	0.6580	0.8112
No log	51.0	51	0.6897	0.5188	0.6888	0.8299
No log	52.0	52	0.6166	0.5738	0.6158	0.7847
No log	53.0	53	0.5357	0.6033	0.5350	0.7315
No log	54.0	54	0.5292	0.6169	0.5285	0.7270
No log	55.0	55	0.5743	0.5995	0.5736	0.7574
No log	56.0	56	0.6570	0.5642	0.6563	0.8101
No log	57.0	57	0.6624	0.5702	0.6617	0.8135
No log	58.0	58	0.5949	0.5847	0.5942	0.7709
No log	59.0	59	0.5858	0.5978	0.5852	0.7650
No log	60.0	60	0.6258	0.5710	0.6252	0.7907
No log	61.0	61	0.6203	0.5861	0.6197	0.7872
No log	62.0	62	0.6774	0.5704	0.6768	0.8227
No log	63.0	63	0.7134	0.5734	0.7129	0.8443
No log	64.0	64	0.6726	0.5776	0.6721	0.8198
No log	65.0	65	0.6971	0.5782	0.6966	0.8346
No log	66.0	66	0.7950	0.5575	0.7945	0.8913
No log	67.0	67	0.7967	0.5578	0.7962	0.8923
No log	68.0	68	0.7086	0.5807	0.7081	0.8415
No log	69.0	69	0.7050	0.5738	0.7045	0.8393
No log	70.0	70	0.7273	0.5720	0.7268	0.8525
No log	71.0	71	0.8262	0.5535	0.8256	0.9086
No log	72.0	72	0.8359	0.5506	0.8353	0.9139
No log	73.0	73	0.7920	0.5574	0.7914	0.8896
No log	74.0	74	0.7688	0.5555	0.7682	0.8765

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 6, 2025
Updated: Mar 7, 2025