BERT_AugV8_k3_task1_organization_sp040_lw010_fold4

genki10/BERT_AugV8_k3_task1_organization_sp040_lw010_fold4

BERT_AugV8_k3_task1_organization_sp040_lw010_fold4 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 25, 2025
Updated: Mar 25, 2025
SHA: 3469c980

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Base models

Model card

From the Hugging Face model README

BERT_AugV8_k3_task1_organization_sp040_lw010_fold4

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: 0.6399
Qwk: 0.5515
Mse: 0.6399
Rmse: 0.8000

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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	3	8.9254	0.0037	8.9254	2.9875
No log	2.0	6	5.5983	0.0307	5.5983	2.3661
No log	3.0	9	3.5231	0.0118	3.5231	1.8770
No log	4.0	12	2.3272	0.1256	2.3272	1.5255
No log	5.0	15	1.5973	0.1040	1.5973	1.2638
No log	6.0	18	1.1536	0.0420	1.1536	1.0741
No log	7.0	21	0.8922	0.1007	0.8922	0.9445
No log	8.0	24	1.2160	0.1288	1.2160	1.1027
No log	9.0	27	0.7126	0.4413	0.7126	0.8442
No log	10.0	30	0.8963	0.1202	0.8963	0.9467
No log	11.0	33	0.7986	0.4151	0.7986	0.8937
No log	12.0	36	0.7288	0.4888	0.7288	0.8537
No log	13.0	39	0.8729	0.4080	0.8729	0.9343
No log	14.0	42	0.5607	0.5390	0.5607	0.7488
No log	15.0	45	0.6447	0.5264	0.6447	0.8029
No log	16.0	48	1.5836	0.2657	1.5836	1.2584
No log	17.0	51	0.5512	0.5013	0.5512	0.7424
No log	18.0	54	0.5376	0.5318	0.5376	0.7332
No log	19.0	57	0.8111	0.4555	0.8111	0.9006
No log	20.0	60	0.5759	0.4807	0.5759	0.7589
No log	21.0	63	0.5636	0.5490	0.5636	0.7508
No log	22.0	66	0.7564	0.5103	0.7564	0.8697
No log	23.0	69	0.7853	0.5203	0.7853	0.8861
No log	24.0	72	0.6224	0.5764	0.6224	0.7889
No log	25.0	75	0.5993	0.5883	0.5993	0.7741
No log	26.0	78	0.5316	0.6074	0.5316	0.7291
No log	27.0	81	0.6448	0.5550	0.6448	0.8030
No log	28.0	84	0.5465	0.5818	0.5465	0.7392
No log	29.0	87	1.1056	0.4250	1.1056	1.0515
No log	30.0	90	0.7205	0.5681	0.7205	0.8488
No log	31.0	93	0.6239	0.5980	0.6239	0.7899
No log	32.0	96	0.8062	0.5131	0.8062	0.8979
No log	33.0	99	0.5171	0.6010	0.5171	0.7191
No log	34.0	102	0.7767	0.5193	0.7767	0.8813
No log	35.0	105	0.5521	0.5850	0.5521	0.7430
No log	36.0	108	0.8590	0.4894	0.8590	0.9268
No log	37.0	111	0.5665	0.5513	0.5665	0.7527
No log	38.0	114	0.5551	0.5589	0.5551	0.7451
No log	39.0	117	0.7141	0.5139	0.7141	0.8450
No log	40.0	120	0.7559	0.5171	0.7559	0.8694
No log	41.0	123	0.6399	0.5515	0.6399	0.8000

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 25, 2025
Updated: Mar 25, 2025