ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_fold2

genki10/ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_fold2

ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_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 15, 2025
Updated: Mar 15, 2025
SHA: 0970db71

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_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: 0.6082
Qwk: 0.4640
Mse: 0.6080
Rmse: 0.7798

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	7.7968	0.0	7.7969	2.7923
No log	2.0	12	4.6143	0.0	4.6146	2.1482
No log	3.0	18	2.6932	0.0	2.6937	1.6412
No log	4.0	24	1.5383	0.0213	1.5387	1.2405
No log	5.0	30	1.1288	0.0449	1.1292	1.0626
No log	6.0	36	1.3578	0.2149	1.3581	1.1654
No log	7.0	42	1.1869	0.1752	1.1872	1.0896
No log	8.0	48	0.9358	0.2987	0.9359	0.9674
No log	9.0	54	0.8995	0.1706	0.8996	0.9485
No log	10.0	60	0.6903	0.3449	0.6905	0.8310
No log	11.0	66	0.9326	0.3998	0.9326	0.9657
No log	12.0	72	0.8354	0.4174	0.8354	0.9140
No log	13.0	78	0.5745	0.4759	0.5745	0.7580
No log	14.0	84	0.6136	0.4577	0.6137	0.7834
No log	15.0	90	0.8358	0.4195	0.8360	0.9143
No log	16.0	96	0.7545	0.4670	0.7547	0.8687
No log	17.0	102	0.6683	0.4496	0.6683	0.8175
No log	18.0	108	0.6613	0.4773	0.6613	0.8132
No log	19.0	114	0.7494	0.4836	0.7494	0.8657
No log	20.0	120	0.6709	0.5070	0.6710	0.8191
No log	21.0	126	0.6658	0.4765	0.6659	0.8160
No log	22.0	132	0.6477	0.4749	0.6477	0.8048
No log	23.0	138	0.6861	0.4822	0.6861	0.8283
No log	24.0	144	0.6687	0.4697	0.6687	0.8177
No log	25.0	150	0.7266	0.4535	0.7266	0.8524
No log	26.0	156	0.7331	0.5101	0.7331	0.8562
No log	27.0	162	0.6423	0.4881	0.6421	0.8013
No log	28.0	168	0.6462	0.4896	0.6462	0.8038
No log	29.0	174	0.6308	0.4678	0.6306	0.7941
No log	30.0	180	0.6520	0.5078	0.6518	0.8074
No log	31.0	186	0.6144	0.4687	0.6143	0.7838
No log	32.0	192	0.6863	0.4834	0.6862	0.8284
No log	33.0	198	0.7756	0.4679	0.7755	0.8806
No log	34.0	204	0.6676	0.4848	0.6675	0.8170
No log	35.0	210	0.6219	0.4750	0.6218	0.7886
No log	36.0	216	0.6093	0.4795	0.6093	0.7806
No log	37.0	222	0.6474	0.5068	0.6474	0.8046
No log	38.0	228	0.6056	0.4618	0.6056	0.7782
No log	39.0	234	0.6179	0.4714	0.6180	0.7861
No log	40.0	240	0.6623	0.4922	0.6623	0.8138
No log	41.0	246	0.6292	0.4927	0.6292	0.7932
No log	42.0	252	0.6282	0.5191	0.6281	0.7925
No log	43.0	258	0.6357	0.4866	0.6356	0.7973
No log	44.0	264	0.7314	0.4506	0.7314	0.8552
No log	45.0	270	0.6456	0.4803	0.6455	0.8034
No log	46.0	276	0.6266	0.4768	0.6265	0.7915
No log	47.0	282	0.6120	0.5194	0.6118	0.7822
No log	48.0	288	0.6262	0.4735	0.6261	0.7913
No log	49.0	294	0.6199	0.5109	0.6197	0.7872
No log	50.0	300	0.6314	0.4665	0.6313	0.7945
No log	51.0	306	0.6355	0.4737	0.6354	0.7971
No log	52.0	312	0.6120	0.4803	0.6118	0.7822
No log	53.0	318	0.6411	0.4709	0.6409	0.8006
No log	54.0	324	0.7147	0.4630	0.7146	0.8454
No log	55.0	330	0.6350	0.4713	0.6347	0.7967
No log	56.0	336	0.6116	0.4671	0.6113	0.7819
No log	57.0	342	0.6603	0.4714	0.6602	0.8125
No log	58.0	348	0.6746	0.4500	0.6745	0.8213
No log	59.0	354	0.6573	0.4600	0.6572	0.8107
No log	60.0	360	0.6344	0.4426	0.6342	0.7964
No log	61.0	366	0.6381	0.4685	0.6379	0.7987
No log	62.0	372	0.6319	0.4738	0.6317	0.7948
No log	63.0	378	0.6216	0.4846	0.6214	0.7883
No log	64.0	384	0.6324	0.4522	0.6323	0.7951
No log	65.0	390	0.6392	0.4512	0.6390	0.7994
No log	66.0	396	0.6393	0.4474	0.6391	0.7994
No log	67.0	402	0.6281	0.4481	0.6279	0.7924
No log	68.0	408	0.6415	0.4342	0.6414	0.8009
No log	69.0	414	0.6204	0.4394	0.6202	0.7875
No log	70.0	420	0.6235	0.4659	0.6233	0.7895
No log	71.0	426	0.6276	0.4652	0.6273	0.7920
No log	72.0	432	0.6134	0.4682	0.6132	0.7831
No log	73.0	438	0.6075	0.4737	0.6073	0.7793
No log	74.0	444	0.6100	0.4715	0.6098	0.7809
No log	75.0	450	0.6245	0.4724	0.6243	0.7901
No log	76.0	456	0.6097	0.4580	0.6095	0.7807
No log	77.0	462	0.6082	0.4640	0.6080	0.7798

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