ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_fold3

genki10/ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_fold3

ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_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 15, 2025
Updated: Mar 15, 2025
SHA: f0f04cff

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_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: 0.8263
Qwk: 0.4165
Mse: 0.8270
Rmse: 0.9094

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	6.2072	0.0079	6.2054	2.4911
No log	2.0	12	3.1406	0.0038	3.1396	1.7719
No log	3.0	18	1.6702	0.0355	1.6696	1.2921
No log	4.0	24	1.5365	0.0365	1.5359	1.2393
No log	5.0	30	1.5472	0.1345	1.5472	1.2438
No log	6.0	36	1.6930	0.1727	1.6934	1.3013
No log	7.0	42	1.2026	0.2858	1.2031	1.0969
No log	8.0	48	1.2922	0.3383	1.2934	1.1373
No log	9.0	54	1.1816	0.3409	1.1824	1.0874
No log	10.0	60	1.6324	0.2458	1.6332	1.2780
No log	11.0	66	1.9839	0.2143	1.9847	1.4088
No log	12.0	72	1.7215	0.2182	1.7224	1.3124
No log	13.0	78	1.4821	0.2414	1.4831	1.2178
No log	14.0	84	1.4440	0.2338	1.4449	1.2021
No log	15.0	90	1.1567	0.3277	1.1574	1.0758
No log	16.0	96	1.2789	0.2828	1.2798	1.1313
No log	17.0	102	1.2983	0.2792	1.2992	1.1398
No log	18.0	108	1.2213	0.2947	1.2223	1.1056
No log	19.0	114	0.9519	0.4210	0.9528	0.9761
No log	20.0	120	0.9440	0.3898	0.9445	0.9719
No log	21.0	126	1.5065	0.2451	1.5074	1.2278
No log	22.0	132	1.3674	0.2441	1.3682	1.1697
No log	23.0	138	1.1387	0.3257	1.1399	1.0677
No log	24.0	144	1.0356	0.3420	1.0364	1.0180
No log	25.0	150	1.1260	0.3348	1.1272	1.0617
No log	26.0	156	0.9338	0.3896	0.9347	0.9668
No log	27.0	162	1.0151	0.3928	1.0162	1.0081
No log	28.0	168	1.7424	0.2009	1.7432	1.3203
No log	29.0	174	1.2479	0.3075	1.2488	1.1175
No log	30.0	180	0.9065	0.4032	0.9073	0.9525
No log	31.0	186	0.9589	0.3893	0.9598	0.9797
No log	32.0	192	1.0411	0.3681	1.0419	1.0208
No log	33.0	198	1.0144	0.3588	1.0153	1.0076
No log	34.0	204	1.0890	0.3556	1.0899	1.0440
No log	35.0	210	0.9929	0.4016	0.9938	0.9969
No log	36.0	216	1.0865	0.3850	1.0875	1.0428
No log	37.0	222	1.0283	0.3483	1.0291	1.0145
No log	38.0	228	1.0971	0.3496	1.0982	1.0480
No log	39.0	234	1.0204	0.3850	1.0214	1.0106
No log	40.0	240	0.8444	0.4004	0.8451	0.9193
No log	41.0	246	1.2986	0.3021	1.2996	1.1400
No log	42.0	252	0.8088	0.4135	0.8095	0.8997
No log	43.0	258	0.8539	0.4286	0.8547	0.9245
No log	44.0	264	1.0242	0.3827	1.0251	1.0125
No log	45.0	270	1.3376	0.2707	1.3385	1.1569
No log	46.0	276	0.8738	0.4108	0.8747	0.9352
No log	47.0	282	0.9792	0.3921	0.9801	0.9900
No log	48.0	288	1.0376	0.3597	1.0384	1.0190
No log	49.0	294	0.9524	0.4005	0.9532	0.9763
No log	50.0	300	0.8188	0.4114	0.8196	0.9053
No log	51.0	306	0.9517	0.4025	0.9528	0.9761
No log	52.0	312	0.9450	0.4151	0.9458	0.9725
No log	53.0	318	1.0513	0.3557	1.0521	1.0257
No log	54.0	324	0.9436	0.3990	0.9445	0.9718
No log	55.0	330	0.8291	0.4431	0.8298	0.9110
No log	56.0	336	0.8676	0.4512	0.8684	0.9319
No log	57.0	342	0.8779	0.4342	0.8786	0.9373
No log	58.0	348	1.1028	0.3441	1.1037	1.0506
No log	59.0	354	0.9789	0.4120	0.9798	0.9898
No log	60.0	360	0.8634	0.4141	0.8642	0.9296
No log	61.0	366	0.8854	0.4225	0.8862	0.9414
No log	62.0	372	1.0242	0.3813	1.0251	1.0125
No log	63.0	378	0.8606	0.4188	0.8614	0.9281
No log	64.0	384	0.9286	0.4134	0.9293	0.9640
No log	65.0	390	0.9467	0.4104	0.9474	0.9733
No log	66.0	396	0.9080	0.4078	0.9088	0.9533
No log	67.0	402	0.8453	0.4352	0.8461	0.9199
No log	68.0	408	0.8882	0.4031	0.8890	0.9429
No log	69.0	414	0.9010	0.4202	0.9018	0.9496
No log	70.0	420	0.9055	0.4019	0.9064	0.9521
No log	71.0	426	0.8601	0.4047	0.8609	0.9279
No log	72.0	432	0.9061	0.3953	0.9069	0.9523
No log	73.0	438	0.8779	0.4010	0.8787	0.9374
No log	74.0	444	0.8873	0.4047	0.8881	0.9424
No log	75.0	450	0.9947	0.3856	0.9956	0.9978
No log	76.0	456	0.8277	0.4311	0.8284	0.9102
No log	77.0	462	1.0289	0.3817	1.0297	1.0147
No log	78.0	468	0.8200	0.4389	0.8208	0.9060
No log	79.0	474	0.9514	0.3916	0.9522	0.9758
No log	80.0	480	0.8407	0.4270	0.8414	0.9173
No log	81.0	486	0.8631	0.4204	0.8638	0.9294
No log	82.0	492	0.9296	0.3854	0.9303	0.9645
No log	83.0	498	0.8407	0.4209	0.8414	0.9173
0.3359	84.0	504	0.8875	0.4154	0.8882	0.9424
0.3359	85.0	510	0.8572	0.3939	0.8579	0.9262
0.3359	86.0	516	0.8263	0.4165	0.8270	0.9094

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