Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold3

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold3

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold3 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 18, 2025
SHA: 23124db9

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Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold3

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: 0.6201
Qwk: 0.6317
Mse: 0.6197
Rmse: 0.7872

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	11.6515	0.0174	11.6498	3.4132
No log	2.0	2	10.0485	0.0041	10.0470	3.1697
No log	3.0	3	8.5012	0.0	8.4999	2.9155
No log	4.0	4	7.4518	0.0	7.4505	2.7296
No log	5.0	5	6.3721	0.0172	6.3710	2.5241
No log	6.0	6	5.4051	0.0504	5.4040	2.3247
No log	7.0	7	4.8191	0.0331	4.8181	2.1950
No log	8.0	8	4.5138	0.0124	4.5128	2.1243
No log	9.0	9	4.2322	0.0114	4.2312	2.0570
No log	10.0	10	3.7188	0.0114	3.7178	1.9282
No log	11.0	11	3.1621	0.0	3.1612	1.7780
No log	12.0	12	2.7678	0.0	2.7668	1.6634
No log	13.0	13	2.4713	0.1542	2.4704	1.5718
No log	14.0	14	2.2420	0.0986	2.2412	1.4971
No log	15.0	15	2.0857	0.0684	2.0849	1.4439
No log	16.0	16	1.8378	0.0488	1.8371	1.3554
No log	17.0	17	1.5821	0.0488	1.5814	1.2575
No log	18.0	18	1.3947	0.0365	1.3940	1.1807
No log	19.0	19	1.2149	0.0302	1.2143	1.1019
No log	20.0	20	1.1559	0.0302	1.1554	1.0749
No log	21.0	21	1.0623	0.0302	1.0618	1.0304
No log	22.0	22	0.9680	0.0356	0.9674	0.9836
No log	23.0	23	0.8992	0.2107	0.8987	0.9480
No log	24.0	24	0.8290	0.3770	0.8285	0.9102
No log	25.0	25	0.7838	0.3408	0.7834	0.8851
No log	26.0	26	0.7868	0.2815	0.7864	0.8868
No log	27.0	27	0.7169	0.3136	0.7166	0.8465
No log	28.0	28	0.6439	0.4048	0.6436	0.8022
No log	29.0	29	0.6179	0.4510	0.6176	0.7859
No log	30.0	30	0.5912	0.4943	0.5910	0.7688
No log	31.0	31	0.5810	0.4654	0.5809	0.7622
No log	32.0	32	0.6033	0.4562	0.6033	0.7767
No log	33.0	33	0.5850	0.5350	0.5850	0.7649
No log	34.0	34	0.6125	0.5618	0.6126	0.7827
No log	35.0	35	0.6816	0.5373	0.6817	0.8256
No log	36.0	36	0.6694	0.5590	0.6694	0.8182
No log	37.0	37	0.6159	0.5588	0.6160	0.7849
No log	38.0	38	0.5884	0.5720	0.5885	0.7672
No log	39.0	39	0.6046	0.5620	0.6046	0.7775
No log	40.0	40	0.5794	0.5726	0.5794	0.7612
No log	41.0	41	0.5597	0.5665	0.5597	0.7482
No log	42.0	42	0.5590	0.5939	0.5591	0.7477
No log	43.0	43	0.5411	0.5924	0.5410	0.7355
No log	44.0	44	0.5581	0.5658	0.5580	0.7470
No log	45.0	45	0.5815	0.5569	0.5813	0.7624
No log	46.0	46	0.5392	0.5857	0.5390	0.7342
No log	47.0	47	0.5246	0.6191	0.5244	0.7242
No log	48.0	48	0.5757	0.5769	0.5756	0.7587
No log	49.0	49	0.5949	0.5737	0.5948	0.7713
No log	50.0	50	0.5581	0.6237	0.5579	0.7469
No log	51.0	51	0.5634	0.6241	0.5632	0.7504
No log	52.0	52	0.5878	0.6027	0.5876	0.7666
No log	53.0	53	0.6581	0.5627	0.6580	0.8112
No log	54.0	54	0.6786	0.5634	0.6786	0.8238
No log	55.0	55	0.6390	0.6095	0.6389	0.7993
No log	56.0	56	0.6134	0.6083	0.6131	0.7830
No log	57.0	57	0.6574	0.5284	0.6571	0.8106
No log	58.0	58	0.6638	0.5235	0.6636	0.8146
No log	59.0	59	0.6092	0.5971	0.6090	0.7804
No log	60.0	60	0.5898	0.6401	0.5896	0.7678
No log	61.0	61	0.6947	0.5776	0.6945	0.8334
No log	62.0	62	0.7747	0.5511	0.7745	0.8801
No log	63.0	63	0.7728	0.5480	0.7726	0.8790
No log	64.0	64	0.6943	0.5758	0.6941	0.8331
No log	65.0	65	0.5927	0.6417	0.5924	0.7697
No log	66.0	66	0.5611	0.6503	0.5608	0.7489
No log	67.0	67	0.5613	0.6407	0.5610	0.7490
No log	68.0	68	0.5681	0.6578	0.5678	0.7535
No log	69.0	69	0.5992	0.6455	0.5990	0.7739
No log	70.0	70	0.6302	0.6319	0.6299	0.7937
No log	71.0	71	0.6337	0.6388	0.6334	0.7959
No log	72.0	72	0.6095	0.6502	0.6092	0.7805
No log	73.0	73	0.6071	0.6165	0.6067	0.7789
No log	74.0	74	0.6155	0.6089	0.6150	0.7842
No log	75.0	75	0.6112	0.6214	0.6108	0.7815
No log	76.0	76	0.6372	0.6500	0.6368	0.7980
No log	77.0	77	0.7645	0.5939	0.7642	0.8742
No log	78.0	78	0.8429	0.5681	0.8427	0.9180
No log	79.0	79	0.8405	0.5714	0.8402	0.9166
No log	80.0	80	0.7726	0.5985	0.7723	0.8788
No log	81.0	81	0.6611	0.6444	0.6607	0.8128
No log	82.0	82	0.6171	0.6590	0.6167	0.7853
No log	83.0	83	0.6098	0.6579	0.6094	0.7806
No log	84.0	84	0.6183	0.6442	0.6179	0.7861
No log	85.0	85	0.6601	0.6393	0.6597	0.8122
No log	86.0	86	0.6862	0.6242	0.6858	0.8281
No log	87.0	87	0.6875	0.6234	0.6871	0.8289
No log	88.0	88	0.6599	0.6369	0.6595	0.8121
No log	89.0	89	0.6395	0.6485	0.6391	0.7994
No log	90.0	90	0.6548	0.6411	0.6544	0.8090
No log	91.0	91	0.7254	0.5988	0.7249	0.8514
No log	92.0	92	0.7507	0.5947	0.7503	0.8662
No log	93.0	93	0.7165	0.5978	0.7161	0.8462
No log	94.0	94	0.6624	0.6374	0.6620	0.8136
No log	95.0	95	0.6364	0.6314	0.6360	0.7975
No log	96.0	96	0.6373	0.6067	0.6368	0.7980
No log	97.0	97	0.6322	0.6116	0.6318	0.7948
No log	98.0	98	0.6358	0.6266	0.6353	0.7971
No log	99.0	99	0.6761	0.6215	0.6757	0.8220
No log	100.0	100	0.7047	0.6104	0.7043	0.8392
No log	101.0	101	0.7113	0.6114	0.7108	0.8431
No log	102.0	102	0.6714	0.6298	0.6710	0.8191
No log	103.0	103	0.6262	0.6379	0.6258	0.7910
No log	104.0	104	0.6104	0.6381	0.6100	0.7810
No log	105.0	105	0.6093	0.6432	0.6089	0.7803
No log	106.0	106	0.6215	0.6392	0.6211	0.7881
No log	107.0	107	0.6540	0.6312	0.6536	0.8085
No log	108.0	108	0.6981	0.5989	0.6977	0.8353
No log	109.0	109	0.7053	0.5863	0.7049	0.8396
No log	110.0	110	0.6923	0.6042	0.6919	0.8318
No log	111.0	111	0.6593	0.6243	0.6589	0.8117
No log	112.0	112	0.6201	0.6317	0.6197	0.7872

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 18, 2025