BERT_AugV8_k5_task1_organization_sp020_lw030_fold2

genki10/BERT_AugV8_k5_task1_organization_sp020_lw030_fold2

BERT_AugV8_k5_task1_organization_sp020_lw030_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. --

Downloads · 30 days

11% of all-time downloads

All-time downloads

Public

Repo size

18 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin876 MB · 100%

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: 56d0f875

Try a prompt

Base models

Model card

From the Hugging Face model README

BERT_AugV8_k5_task1_organization_sp020_lw030_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: 0.7593
Qwk: 0.4105
Mse: 0.7594
Rmse: 0.8714

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	4	10.1767	0.0	10.1766	3.1901
No log	2.0	8	7.4764	0.0	7.4765	2.7343
No log	3.0	12	4.7960	0.0039	4.7965	2.1901
No log	4.0	16	3.1121	0.0	3.1126	1.7642
No log	5.0	20	1.8475	0.0422	1.8480	1.3594
No log	6.0	24	1.1576	0.0107	1.1580	1.0761
No log	7.0	28	0.9815	-0.0078	0.9821	0.9910
No log	8.0	32	0.9150	0.0581	0.9153	0.9567
No log	9.0	36	1.0052	0.0147	1.0057	1.0028
No log	10.0	40	0.7933	0.2903	0.7935	0.8908
No log	11.0	44	0.7385	0.3174	0.7387	0.8595
No log	12.0	48	0.6100	0.4126	0.6101	0.7811
No log	13.0	52	0.5769	0.3456	0.5769	0.7596
No log	14.0	56	0.6613	0.3002	0.6614	0.8133
No log	15.0	60	0.5818	0.3645	0.5817	0.7627
No log	16.0	64	0.5251	0.4883	0.5249	0.7245
No log	17.0	68	0.5773	0.4410	0.5772	0.7597
No log	18.0	72	0.5945	0.4260	0.5943	0.7709
No log	19.0	76	0.5458	0.5357	0.5455	0.7386
No log	20.0	80	0.7119	0.3742	0.7118	0.8437
No log	21.0	84	0.6306	0.4742	0.6303	0.7939
No log	22.0	88	0.5904	0.5093	0.5902	0.7682
No log	23.0	92	0.5962	0.5329	0.5960	0.7720
No log	24.0	96	0.6463	0.4684	0.6460	0.8038
No log	25.0	100	0.7380	0.4259	0.7378	0.8590
No log	26.0	104	0.6452	0.5008	0.6448	0.8030
No log	27.0	108	0.8298	0.3330	0.8299	0.9110
No log	28.0	112	0.8041	0.3615	0.8042	0.8968
No log	29.0	116	0.6268	0.5306	0.6265	0.7915
No log	30.0	120	0.7023	0.4729	0.7023	0.8380
No log	31.0	124	0.7306	0.4086	0.7308	0.8548
No log	32.0	128	0.6840	0.5034	0.6839	0.8270
No log	33.0	132	0.7725	0.3866	0.7726	0.8790
No log	34.0	136	0.7593	0.4105	0.7594	0.8714

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