BERT_AugV8_k3_task1_organization_sp060_lw040_fold4

genki10/BERT_AugV8_k3_task1_organization_sp060_lw040_fold4

BERT_AugV8_k3_task1_organization_sp060_lw040_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 27, 2025
Updated: Apr 3, 2025
SHA: 9c8ba3e8

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Base models

Model card

From the Hugging Face model README

BERT_AugV8_k3_task1_organization_sp060_lw040_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: 0.7788
Qwk: 0.4469
Mse: 0.7788
Rmse: 0.8825

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	9.5453	0.0037	9.5453	3.0896
No log	2.0	6	7.4540	0.0018	7.4540	2.7302
No log	3.0	9	6.3225	0.0018	6.3225	2.5145
No log	4.0	12	4.7421	0.0128	4.7421	2.1776
No log	5.0	15	3.2736	0.0040	3.2736	1.8093
No log	6.0	18	2.3479	0.0609	2.3479	1.5323
No log	7.0	21	1.7985	0.0634	1.7985	1.3411
No log	8.0	24	1.5161	0.0445	1.5161	1.2313
No log	9.0	27	1.1061	0.0445	1.1061	1.0517
No log	10.0	30	1.3145	0.0729	1.3145	1.1465
No log	11.0	33	0.9902	0.0809	0.9902	0.9951
No log	12.0	36	0.8842	0.3344	0.8842	0.9403
No log	13.0	39	0.9049	0.3566	0.9049	0.9512
No log	14.0	42	0.8682	0.3933	0.8682	0.9318
No log	15.0	45	0.8196	0.3779	0.8196	0.9053
No log	16.0	48	0.7780	0.3340	0.7780	0.8821
No log	17.0	51	0.7332	0.4039	0.7332	0.8563
No log	18.0	54	0.7182	0.4573	0.7182	0.8474
No log	19.0	57	0.6688	0.4714	0.6688	0.8178
No log	20.0	60	0.8472	0.5011	0.8472	0.9205
No log	21.0	63	0.7005	0.5669	0.7005	0.8369
No log	22.0	66	0.9321	0.4786	0.9321	0.9655
No log	23.0	69	0.7314	0.5504	0.7314	0.8552
No log	24.0	72	0.9747	0.4767	0.9747	0.9873
No log	25.0	75	0.8923	0.5012	0.8923	0.9446
No log	26.0	78	0.7738	0.5188	0.7738	0.8797
No log	27.0	81	0.8272	0.4501	0.8272	0.9095
No log	28.0	84	0.7613	0.5019	0.7613	0.8725
No log	29.0	87	0.9244	0.4417	0.9244	0.9615
No log	30.0	90	0.8334	0.4675	0.8334	0.9129
No log	31.0	93	0.9424	0.4335	0.9424	0.9708
No log	32.0	96	0.8225	0.4877	0.8225	0.9069
No log	33.0	99	1.0815	0.3825	1.0815	1.0400
No log	34.0	102	0.7937	0.4521	0.7937	0.8909
No log	35.0	105	0.9488	0.3887	0.9488	0.9741
No log	36.0	108	0.7788	0.4469	0.7788	0.8825

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 27, 2025
Updated: Apr 3, 2025