Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold2

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold2

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold2 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 17, 2025
Updated: Mar 17, 2025
SHA: 35827ddc

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Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_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: 0.7885
Qwk: 0.5906
Mse: 0.7881
Rmse: 0.8877

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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	1	9.8144	0.0045	9.8147	3.1328
No log	2.0	2	8.6796	0.0	8.6800	2.9462
No log	3.0	3	8.3640	0.0	8.3644	2.8921
No log	4.0	4	8.0220	0.0	8.0223	2.8324
No log	5.0	5	7.5133	0.0	7.5136	2.7411
No log	6.0	6	6.7814	0.0	6.7817	2.6042
No log	7.0	7	5.9970	0.0050	5.9973	2.4489
No log	8.0	8	5.4144	0.0151	5.4148	2.3270
No log	9.0	9	4.8305	0.0078	4.8310	2.1979
No log	10.0	10	4.1849	0.0039	4.1854	2.0458
No log	11.0	11	3.8014	0.0	3.8017	1.9498
No log	12.0	12	3.4551	0.0	3.4555	1.8589
No log	13.0	13	2.9463	0.0	2.9468	1.7166
No log	14.0	14	2.5393	0.0043	2.5397	1.5937
No log	15.0	15	2.2696	0.0974	2.2701	1.5067
No log	16.0	16	2.0567	0.1292	2.0572	1.4343
No log	17.0	17	1.7715	0.0615	1.7720	1.3312
No log	18.0	18	1.6083	0.0280	1.6088	1.2684
No log	19.0	19	1.4129	0.0280	1.4134	1.1889
No log	20.0	20	1.3582	0.0615	1.3587	1.1656
No log	21.0	21	1.1430	0.0551	1.1434	1.0693
No log	22.0	22	1.1053	0.0280	1.1058	1.0516
No log	23.0	23	1.0310	0.0280	1.0315	1.0156
No log	24.0	24	0.8484	0.0384	0.8488	0.9213
No log	25.0	25	0.9567	0.1177	0.9570	0.9783
No log	26.0	26	0.9409	0.2101	0.9412	0.9701
No log	27.0	27	0.6976	0.4678	0.6978	0.8354
No log	28.0	28	0.6936	0.3828	0.6938	0.8329
No log	29.0	29	0.6792	0.3707	0.6793	0.8242
No log	30.0	30	0.5854	0.4901	0.5854	0.7651
No log	31.0	31	0.6624	0.5265	0.6624	0.8139
No log	32.0	32	0.6754	0.5266	0.6754	0.8218
No log	33.0	33	0.5382	0.5359	0.5381	0.7336
No log	34.0	34	0.5464	0.5561	0.5463	0.7391
No log	35.0	35	0.6971	0.5267	0.6971	0.8349
No log	36.0	36	0.6888	0.5367	0.6888	0.8299
No log	37.0	37	0.5015	0.5564	0.5014	0.7081
No log	38.0	38	0.4812	0.5553	0.4811	0.6936
No log	39.0	39	0.4742	0.5621	0.4742	0.6886
No log	40.0	40	0.4582	0.5417	0.4581	0.6769
No log	41.0	41	0.4928	0.5466	0.4928	0.7020
No log	42.0	42	0.4748	0.5132	0.4748	0.6891
No log	43.0	43	0.5892	0.4912	0.5893	0.7677
No log	44.0	44	0.5989	0.5269	0.5990	0.7739
No log	45.0	45	0.5458	0.5561	0.5458	0.7388
No log	46.0	46	0.5582	0.5791	0.5582	0.7471
No log	47.0	47	0.6046	0.5316	0.6046	0.7776
No log	48.0	48	0.5898	0.5675	0.5899	0.7680
No log	49.0	49	0.5744	0.5823	0.5744	0.7579
No log	50.0	50	0.5687	0.5947	0.5686	0.7541
No log	51.0	51	0.5581	0.6077	0.5580	0.7470
No log	52.0	52	0.5585	0.6016	0.5584	0.7472
No log	53.0	53	0.5730	0.5946	0.5730	0.7569
No log	54.0	54	0.5447	0.6040	0.5445	0.7379
No log	55.0	55	0.5548	0.5874	0.5547	0.7448
No log	56.0	56	0.6306	0.5788	0.6305	0.7941
No log	57.0	57	0.6123	0.5853	0.6122	0.7824
No log	58.0	58	0.5634	0.5969	0.5633	0.7505
No log	59.0	59	0.5717	0.5961	0.5716	0.7561
No log	60.0	60	0.6047	0.5628	0.6046	0.7776
No log	61.0	61	0.7929	0.5252	0.7929	0.8905
No log	62.0	62	0.8349	0.5210	0.8349	0.9137
No log	63.0	63	0.7018	0.5549	0.7017	0.8377
No log	64.0	64	0.6458	0.5577	0.6457	0.8035
No log	65.0	65	0.6528	0.5520	0.6526	0.8078
No log	66.0	66	0.7290	0.5598	0.7288	0.8537
No log	67.0	67	0.9089	0.5265	0.9087	0.9533
No log	68.0	68	0.9081	0.5285	0.9078	0.9528
No log	69.0	69	0.7517	0.5705	0.7514	0.8668
No log	70.0	70	0.6557	0.5901	0.6554	0.8096
No log	71.0	71	0.6648	0.5832	0.6645	0.8152
No log	72.0	72	0.6254	0.5968	0.6251	0.7906
No log	73.0	73	0.6355	0.5699	0.6352	0.7970
No log	74.0	74	0.6638	0.5676	0.6634	0.8145
No log	75.0	75	0.6175	0.5696	0.6172	0.7856
No log	76.0	76	0.5997	0.6005	0.5994	0.7742
No log	77.0	77	0.6273	0.5921	0.6269	0.7918
No log	78.0	78	0.6409	0.5902	0.6406	0.8004
No log	79.0	79	0.7067	0.5852	0.7062	0.8404
No log	80.0	80	0.7081	0.5908	0.7076	0.8412
No log	81.0	81	0.7885	0.5906	0.7881	0.8877

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 17, 2025
Updated: Mar 17, 2025