Version22NewTestASAP_FineTuningBERT_AugV22_k3_task1_organization_k3_k3_fold0

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k3_task1_organization_k3_k3_fold0

Version22NewTestASAP_FineTuningBERT_AugV22_k3_task1_organization_k3_k3_fold0 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 15, 2025
Updated: Mar 15, 2025
SHA: 99e02e1e

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Base models

Model card

From the Hugging Face model README

Version22NewTestASAP_FineTuningBERT_AugV22_k3_task1_organization_k3_k3_fold0

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: 0.6926
Qwk: 0.5322
Mse: 0.6926
Rmse: 0.8322

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	8.8988	0.0	8.8988	2.9831
No log	2.0	4	7.6062	0.0	7.6062	2.7579
No log	3.0	6	6.9069	0.0	6.9069	2.6281
No log	4.0	8	5.9660	0.0087	5.9660	2.4425
No log	5.0	10	5.1461	0.0115	5.1461	2.2685
No log	6.0	12	4.3284	0.0039	4.3284	2.0805
No log	7.0	14	3.5950	0.0	3.5950	1.8961
No log	8.0	16	2.8265	0.0	2.8265	1.6812
No log	9.0	18	2.1906	0.0909	2.1906	1.4801
No log	10.0	20	1.7868	0.0575	1.7868	1.3367
No log	11.0	22	1.3720	0.0316	1.3720	1.1713
No log	12.0	24	1.1552	0.0316	1.1552	1.0748
No log	13.0	26	1.3068	0.0612	1.3068	1.1431
No log	14.0	28	2.2675	0.1734	2.2675	1.5058
No log	15.0	30	1.3884	0.1368	1.3884	1.1783
No log	16.0	32	0.9373	0.0548	0.9373	0.9681
No log	17.0	34	0.9454	0.0638	0.9454	0.9723
No log	18.0	36	1.4077	0.1707	1.4077	1.1865
No log	19.0	38	0.9498	0.2082	0.9498	0.9746
No log	20.0	40	0.7757	0.3487	0.7757	0.8807
No log	21.0	42	0.7862	0.4181	0.7862	0.8867
No log	22.0	44	0.7608	0.4340	0.7608	0.8723
No log	23.0	46	0.6571	0.4231	0.6571	0.8106
No log	24.0	48	0.6248	0.4514	0.6248	0.7904
No log	25.0	50	0.8817	0.3772	0.8817	0.9390
No log	26.0	52	0.7525	0.4435	0.7525	0.8675
No log	27.0	54	0.5874	0.4532	0.5874	0.7664
No log	28.0	56	0.6114	0.4801	0.6114	0.7819
No log	29.0	58	0.9142	0.4247	0.9142	0.9562
No log	30.0	60	0.6582	0.4739	0.6582	0.8113
No log	31.0	62	0.5836	0.4313	0.5836	0.7639
No log	32.0	64	0.6055	0.4696	0.6055	0.7781
No log	33.0	66	0.8976	0.4200	0.8976	0.9474
No log	34.0	68	0.6703	0.4550	0.6703	0.8187
No log	35.0	70	0.5973	0.4353	0.5973	0.7729
No log	36.0	72	0.6023	0.4829	0.6023	0.7761
No log	37.0	74	0.7127	0.4527	0.7127	0.8442
No log	38.0	76	0.6624	0.4959	0.6624	0.8139
No log	39.0	78	0.6730	0.4714	0.6730	0.8204
No log	40.0	80	0.7644	0.4403	0.7644	0.8743
No log	41.0	82	0.6592	0.4630	0.6592	0.8119
No log	42.0	84	0.6393	0.4716	0.6393	0.7996
No log	43.0	86	0.6794	0.4397	0.6794	0.8242
No log	44.0	88	0.6378	0.4947	0.6378	0.7986
No log	45.0	90	0.6664	0.5169	0.6664	0.8164
No log	46.0	92	0.6225	0.4979	0.6225	0.7890
No log	47.0	94	0.6038	0.5018	0.6038	0.7771
No log	48.0	96	0.6214	0.4893	0.6214	0.7883
No log	49.0	98	0.6239	0.5143	0.6239	0.7899
No log	50.0	100	0.6599	0.5244	0.6599	0.8124
No log	51.0	102	0.6997	0.5218	0.6997	0.8365
No log	52.0	104	0.7642	0.5161	0.7642	0.8742
No log	53.0	106	0.7241	0.5142	0.7241	0.8509
No log	54.0	108	0.7057	0.5279	0.7057	0.8401
No log	55.0	110	0.7549	0.5161	0.7549	0.8688
No log	56.0	112	0.8422	0.4953	0.8422	0.9177
No log	57.0	114	0.7157	0.5325	0.7157	0.8460
No log	58.0	116	0.7163	0.5297	0.7163	0.8463
No log	59.0	118	0.7514	0.5322	0.7514	0.8668
No log	60.0	120	0.6719	0.5543	0.6719	0.8197
No log	61.0	122	0.6905	0.5556	0.6905	0.8310
No log	62.0	124	0.7269	0.5478	0.7269	0.8526
No log	63.0	126	0.6450	0.5337	0.6450	0.8031
No log	64.0	128	0.6345	0.5037	0.6345	0.7965
No log	65.0	130	0.6598	0.5294	0.6598	0.8123
No log	66.0	132	0.7575	0.5142	0.7575	0.8703
No log	67.0	134	0.7190	0.5264	0.7190	0.8479
No log	68.0	136	0.7253	0.5200	0.7253	0.8517
No log	69.0	138	0.8009	0.4828	0.8009	0.8950
No log	70.0	140	0.7788	0.4949	0.7788	0.8825
No log	71.0	142	0.7212	0.5348	0.7212	0.8492
No log	72.0	144	0.7014	0.5234	0.7014	0.8375
No log	73.0	146	0.7136	0.5287	0.7136	0.8448
No log	74.0	148	0.6881	0.5136	0.6881	0.8295
No log	75.0	150	0.7037	0.5285	0.7037	0.8389
No log	76.0	152	0.7726	0.4892	0.7726	0.8789
No log	77.0	154	0.7907	0.4959	0.7907	0.8892
No log	78.0	156	0.7722	0.5012	0.7722	0.8788
No log	79.0	158	0.7513	0.5193	0.7513	0.8668
No log	80.0	160	0.7505	0.5202	0.7505	0.8663
No log	81.0	162	0.7141	0.5401	0.7141	0.8450
No log	82.0	164	0.6809	0.5286	0.6809	0.8252
No log	83.0	166	0.6927	0.5272	0.6927	0.8323
No log	84.0	168	0.7268	0.5506	0.7268	0.8525
No log	85.0	170	0.7101	0.5335	0.7101	0.8427
No log	86.0	172	0.7010	0.5314	0.7010	0.8373
No log	87.0	174	0.6957	0.5312	0.6957	0.8341
No log	88.0	176	0.7325	0.5382	0.7325	0.8558
No log	89.0	178	0.7516	0.5314	0.7516	0.8669
No log	90.0	180	0.7204	0.5457	0.7204	0.8488
No log	91.0	182	0.6926	0.5322	0.6926	0.8322

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 15, 2025
Updated: Mar 15, 2025