BERT_V8_sp10_lw40_ex100_lo100_k5_k5_fold1

genki10/BERT_V8_sp10_lw40_ex100_lo100_k5_k5_fold1

BERT_V8_sp10_lw40_ex100_lo100_k5_k5_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 26, 2025
Updated: Apr 26, 2025
SHA: 066d903f

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Base models

Model card

From the Hugging Face model README

BERT_V8_sp10_lw40_ex100_lo100_k5_k5_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: 1.6353
Qwk: 0.1140
Mse: 1.6326
Rmse: 1.2777

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 OptimizerNames.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	9.0299	0.0	9.0274	3.0046
No log	2.0	8	5.3444	0.0433	5.3423	2.3113
No log	3.0	12	3.2180	0.0	3.2166	1.7935
No log	4.0	16	1.7202	0.0506	1.7186	1.3109
No log	5.0	20	1.1436	0.0	1.1421	1.0687
No log	6.0	24	0.9181	0.1020	0.9168	0.9575
No log	7.0	28	1.2768	0.0106	1.2750	1.1292
No log	8.0	32	0.9237	0.0281	0.9223	0.9604
No log	9.0	36	1.2520	0.0477	1.2501	1.1181
No log	10.0	40	0.9408	0.1032	0.9393	0.9692
No log	11.0	44	1.3241	0.0759	1.3219	1.1497
No log	12.0	48	1.1377	0.0685	1.1358	1.0657
No log	13.0	52	1.1281	0.0798	1.1263	1.0613
No log	14.0	56	1.2087	0.1320	1.2066	1.0985
No log	15.0	60	1.1542	0.2049	1.1523	1.0735
No log	16.0	64	1.0781	0.2410	1.0763	1.0374
No log	17.0	68	1.5877	0.1953	1.5851	1.2590
No log	18.0	72	1.4312	0.2144	1.4288	1.1953
No log	19.0	76	1.5225	0.1989	1.5199	1.2328
No log	20.0	80	1.4931	0.1813	1.4905	1.2209
No log	21.0	84	1.3639	0.1714	1.3615	1.1668
No log	22.0	88	1.3926	0.1355	1.3901	1.1790
No log	23.0	92	1.2948	0.1480	1.2926	1.1369
No log	24.0	96	1.9045	0.0879	1.9016	1.3790
No log	25.0	100	1.3207	0.1375	1.3187	1.1483
No log	26.0	104	1.4813	0.1235	1.4788	1.2160
No log	27.0	108	1.1697	0.1625	1.1677	1.0806
No log	28.0	112	1.5915	0.1017	1.5888	1.2605
No log	29.0	116	1.4440	0.1083	1.4414	1.2006
No log	30.0	120	1.3808	0.1171	1.3783	1.1740
No log	31.0	124	1.6899	0.0952	1.6871	1.2989
No log	32.0	128	1.2630	0.1673	1.2608	1.1228
No log	33.0	132	1.6055	0.0951	1.6027	1.2660
No log	34.0	136	1.7053	0.0880	1.7025	1.3048
No log	35.0	140	1.3067	0.1374	1.3045	1.1421
No log	36.0	144	1.3122	0.1539	1.3099	1.1445
No log	37.0	148	1.1826	0.1855	1.1806	1.0866
No log	38.0	152	1.5995	0.1339	1.5968	1.2637
No log	39.0	156	1.2464	0.1510	1.2443	1.1155
No log	40.0	160	1.5593	0.1048	1.5567	1.2477
No log	41.0	164	1.5133	0.1191	1.5108	1.2291
No log	42.0	168	1.2265	0.1376	1.2244	1.1065
No log	43.0	172	1.5164	0.1125	1.5138	1.2304
No log	44.0	176	1.4855	0.1236	1.4829	1.2177
No log	45.0	180	1.3312	0.1272	1.3288	1.1528
No log	46.0	184	1.6353	0.1140	1.6326	1.2777

Framework versions

Transformers 4.51.1
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Apr 26, 2025
Updated: Apr 26, 2025