ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_fold0

genki10/ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_fold0

ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_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: 9e5955e7

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_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: 0.7050
Qwk: 0.4668
Mse: 0.7050
Rmse: 0.8397

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	6.8768	0.0	6.8768	2.6224
No log	2.0	12	4.3248	0.0039	4.3248	2.0796
No log	3.0	18	2.2647	0.1169	2.2647	1.5049
No log	4.0	24	1.2303	0.0316	1.2303	1.1092
No log	5.0	30	2.0946	0.1530	2.0946	1.4473
No log	6.0	36	1.1405	0.1053	1.1405	1.0679
No log	7.0	42	1.0378	0.2354	1.0378	1.0187
No log	8.0	48	0.8130	0.2935	0.8130	0.9017
No log	9.0	54	0.8864	0.2875	0.8864	0.9415
No log	10.0	60	1.0700	0.2794	1.0700	1.0344
No log	11.0	66	0.8442	0.3828	0.8442	0.9188
No log	12.0	72	0.8385	0.4265	0.8385	0.9157
No log	13.0	78	0.7474	0.4589	0.7474	0.8645
No log	14.0	84	0.8692	0.4032	0.8692	0.9323
No log	15.0	90	0.7927	0.4467	0.7927	0.8903
No log	16.0	96	0.7402	0.4579	0.7402	0.8604
No log	17.0	102	0.7611	0.4546	0.7611	0.8724
No log	18.0	108	0.7791	0.4523	0.7791	0.8827
No log	19.0	114	0.7782	0.4357	0.7782	0.8822
No log	20.0	120	0.7764	0.4660	0.7764	0.8811
No log	21.0	126	0.7508	0.4256	0.7508	0.8665
No log	22.0	132	0.8363	0.4042	0.8363	0.9145
No log	23.0	138	0.7580	0.4567	0.7580	0.8706
No log	24.0	144	0.7852	0.4439	0.7852	0.8861
No log	25.0	150	0.6961	0.4727	0.6961	0.8343
No log	26.0	156	0.7616	0.5206	0.7616	0.8727
No log	27.0	162	0.6816	0.4832	0.6816	0.8256
No log	28.0	168	0.8672	0.3852	0.8672	0.9312
No log	29.0	174	0.6987	0.4513	0.6987	0.8359
No log	30.0	180	0.6978	0.4623	0.6978	0.8354
No log	31.0	186	0.7940	0.4044	0.7940	0.8911
No log	32.0	192	0.7596	0.4131	0.7596	0.8716
No log	33.0	198	0.7037	0.4769	0.7037	0.8389
No log	34.0	204	0.7625	0.4570	0.7625	0.8732
No log	35.0	210	0.7362	0.4633	0.7362	0.8580
No log	36.0	216	0.7539	0.4577	0.7539	0.8683
No log	37.0	222	0.7445	0.4309	0.7445	0.8629
No log	38.0	228	0.9144	0.3167	0.9144	0.9563
No log	39.0	234	0.7172	0.4697	0.7172	0.8469
No log	40.0	240	0.7408	0.4273	0.7408	0.8607
No log	41.0	246	0.8003	0.3947	0.8003	0.8946
No log	42.0	252	0.7132	0.4452	0.7132	0.8445
No log	43.0	258	0.8320	0.3824	0.8320	0.9121
No log	44.0	264	0.7173	0.4479	0.7173	0.8469
No log	45.0	270	0.7843	0.4401	0.7843	0.8856
No log	46.0	276	0.7767	0.4387	0.7767	0.8813
No log	47.0	282	0.7537	0.4570	0.7537	0.8682
No log	48.0	288	0.7236	0.4727	0.7236	0.8506
No log	49.0	294	0.7274	0.4734	0.7274	0.8529
No log	50.0	300	0.7554	0.4631	0.7554	0.8691
No log	51.0	306	0.7193	0.4831	0.7193	0.8481
No log	52.0	312	0.6894	0.4645	0.6894	0.8303
No log	53.0	318	0.7263	0.4489	0.7263	0.8523
No log	54.0	324	0.7395	0.4490	0.7395	0.8600
No log	55.0	330	0.7250	0.4490	0.7250	0.8515
No log	56.0	336	0.7050	0.4668	0.7050	0.8397

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