BERT_V8_sp10_lw20_ex200_lo100_k7_k7_fold1

genki10/BERT_V8_sp10_lw20_ex200_lo100_k7_k7_fold1

BERT_V8_sp10_lw20_ex200_lo100_k7_k7_fold1 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: Apr 19, 2025
Updated: Apr 19, 2025
SHA: c23ec5a2

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Base models

Model card

From the Hugging Face model README

BERT_V8_sp10_lw20_ex200_lo100_k7_k7_fold1

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: 0.6835
Qwk: 0.4323
Mse: 0.6820
Rmse: 0.8258

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	4	7.0540	0.0	7.0517	2.6555
No log	2.0	8	5.3072	0.0045	5.3051	2.3033
No log	3.0	12	3.6718	0.0	3.6699	1.9157
No log	4.0	16	2.2097	0.1660	2.2080	1.4859
No log	5.0	20	1.3926	0.0	1.3911	1.1794
No log	6.0	24	0.9812	0.0	0.9799	0.9899
No log	7.0	28	0.8365	0.3998	0.8353	0.9140
No log	8.0	32	0.7960	0.3828	0.7948	0.8915
No log	9.0	36	0.9017	0.1553	0.9004	0.9489
No log	10.0	40	0.8191	0.3219	0.8179	0.9044
No log	11.0	44	0.7865	0.3738	0.7852	0.8861
No log	12.0	48	0.6297	0.4864	0.6285	0.7928
No log	13.0	52	0.6191	0.5206	0.6180	0.7861
No log	14.0	56	0.6859	0.4596	0.6850	0.8277
No log	15.0	60	0.5969	0.5525	0.5961	0.7721
No log	16.0	64	0.6194	0.5486	0.6184	0.7864
No log	17.0	68	1.0312	0.3324	1.0301	1.0149
No log	18.0	72	0.6024	0.5264	0.6013	0.7754
No log	19.0	76	0.7547	0.4233	0.7535	0.8680
No log	20.0	80	0.8085	0.3971	0.8072	0.8984
No log	21.0	84	0.7757	0.4125	0.7743	0.8800
No log	22.0	88	0.7169	0.4229	0.7155	0.8459
No log	23.0	92	0.6662	0.4705	0.6648	0.8153
No log	24.0	96	0.8145	0.4211	0.8131	0.9017
No log	25.0	100	0.9754	0.3481	0.9739	0.9868
No log	26.0	104	0.7127	0.4175	0.7112	0.8433
No log	27.0	108	0.7265	0.4252	0.7250	0.8515
No log	28.0	112	0.8696	0.3866	0.8681	0.9317
No log	29.0	116	0.7805	0.4036	0.7791	0.8827
No log	30.0	120	0.8488	0.3315	0.8472	0.9205
No log	31.0	124	0.8379	0.3439	0.8364	0.9145
No log	32.0	128	0.7261	0.3976	0.7247	0.8513
No log	33.0	132	0.8214	0.3771	0.8199	0.9055
No log	34.0	136	0.8021	0.3610	0.8005	0.8947
No log	35.0	140	0.7789	0.3914	0.7774	0.8817
No log	36.0	144	1.0122	0.3342	1.0107	1.0053
No log	37.0	148	0.7004	0.4182	0.6990	0.8360
No log	38.0	152	0.9180	0.3401	0.9165	0.9573
No log	39.0	156	0.6444	0.4992	0.6430	0.8019
No log	40.0	160	1.1477	0.2717	1.1460	1.0705
No log	41.0	164	0.7341	0.3937	0.7326	0.8559
No log	42.0	168	0.8095	0.3339	0.8079	0.8988
No log	43.0	172	0.7735	0.3703	0.7720	0.8786
No log	44.0	176	0.8972	0.3675	0.8957	0.9464
No log	45.0	180	0.6835	0.4323	0.6820	0.8258

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: Apr 19, 2025
Updated: Apr 19, 2025