ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold0

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold0

ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_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 7, 2025
Updated: Mar 7, 2025
SHA: b7eef83a

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Base models

Model card

From the Hugging Face model README

ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_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: 0.6034
Qwk: 0.5330
Mse: 0.6034
Rmse: 0.7768

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	3	8.0788	0.0	8.0788	2.8423
No log	2.0	6	6.8015	0.0	6.8015	2.6080
No log	3.0	9	5.4078	0.0115	5.4078	2.3255
No log	4.0	12	4.1456	0.0039	4.1456	2.0361
No log	5.0	15	2.9603	0.0	2.9603	1.7205
No log	6.0	18	2.0063	0.0689	2.0063	1.4164
No log	7.0	21	1.3868	0.0316	1.3868	1.1776
No log	8.0	24	1.0330	0.0316	1.0330	1.0163
No log	9.0	27	0.8975	0.0764	0.8975	0.9473
No log	10.0	30	0.6970	0.4730	0.6970	0.8348
No log	11.0	33	0.9062	0.2446	0.9062	0.9520
No log	12.0	36	0.5412	0.4344	0.5412	0.7356
No log	13.0	39	1.2649	0.1739	1.2649	1.1247
No log	14.0	42	0.7757	0.3767	0.7757	0.8808
No log	15.0	45	0.5158	0.4191	0.5158	0.7182
No log	16.0	48	0.9711	0.3300	0.9711	0.9855
No log	17.0	51	0.5638	0.4294	0.5638	0.7509
No log	18.0	54	0.6415	0.3964	0.6415	0.8009
No log	19.0	57	0.6264	0.4385	0.6264	0.7914
No log	20.0	60	0.5957	0.5050	0.5957	0.7718
No log	21.0	63	0.6178	0.5090	0.6178	0.7860
No log	22.0	66	0.5804	0.5655	0.5804	0.7618
No log	23.0	69	1.3562	0.3041	1.3562	1.1646
No log	24.0	72	0.6788	0.5369	0.6788	0.8239
No log	25.0	75	0.7405	0.4953	0.7405	0.8605
No log	26.0	78	0.7800	0.4754	0.7800	0.8832
No log	27.0	81	0.6159	0.5475	0.6159	0.7848
No log	28.0	84	0.7405	0.4669	0.7405	0.8605
No log	29.0	87	0.6421	0.5325	0.6421	0.8013
No log	30.0	90	0.6623	0.4883	0.6623	0.8138
No log	31.0	93	0.5782	0.5313	0.5782	0.7604
No log	32.0	96	0.6284	0.5327	0.6284	0.7927
No log	33.0	99	0.6148	0.5440	0.6148	0.7841
No log	34.0	102	0.5969	0.5482	0.5969	0.7726
No log	35.0	105	0.5880	0.5680	0.5880	0.7668
No log	36.0	108	0.5928	0.5490	0.5928	0.7700
No log	37.0	111	0.6022	0.5507	0.6022	0.7760
No log	38.0	114	0.5946	0.5314	0.5946	0.7711
No log	39.0	117	0.6247	0.5263	0.6247	0.7904
No log	40.0	120	0.5996	0.5521	0.5996	0.7744
No log	41.0	123	0.6038	0.5416	0.6038	0.7770
No log	42.0	126	0.6066	0.5224	0.6066	0.7788
No log	43.0	129	0.5696	0.5209	0.5696	0.7547
No log	44.0	132	0.6343	0.5258	0.6343	0.7964
No log	45.0	135	0.5774	0.5465	0.5774	0.7599
No log	46.0	138	0.5843	0.5401	0.5843	0.7644
No log	47.0	141	0.5801	0.5272	0.5801	0.7616
No log	48.0	144	0.5877	0.5275	0.5877	0.7666
No log	49.0	147	0.6055	0.5224	0.6055	0.7781
No log	50.0	150	0.5756	0.5543	0.5756	0.7587
No log	51.0	153	0.5704	0.5294	0.5704	0.7553
No log	52.0	156	0.6006	0.5297	0.6006	0.7750
No log	53.0	159	0.6223	0.5267	0.6223	0.7889
No log	54.0	162	0.5764	0.5475	0.5764	0.7592
No log	55.0	165	0.6034	0.5330	0.6034	0.7768

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 7, 2025
Updated: Mar 7, 2025