BERT_V8_sp10_lw40_ex50_lo50_k2_k2_fold2

genki10/BERT_V8_sp10_lw40_ex50_lo50_k2_k2_fold2

BERT_V8_sp10_lw40_ex50_lo50_k2_k2_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. --

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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: 86cd7c43

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Base models

Model card

From the Hugging Face model README

BERT_V8_sp10_lw40_ex50_lo50_k2_k2_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: 0.5162
Qwk: 0.6112
Mse: 0.5160
Rmse: 0.7183

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	2	8.6022	0.0	8.6026	2.9330
No log	2.0	4	7.7195	0.0	7.7198	2.7785
No log	3.0	6	6.2878	-0.0003	6.2881	2.5076
No log	4.0	8	5.0042	0.0078	5.0046	2.2371
No log	5.0	10	3.9093	0.0078	3.9096	1.9773
No log	6.0	12	3.4540	0.0	3.4545	1.8586
No log	7.0	14	2.6568	0.0339	2.6572	1.6301
No log	8.0	16	2.0705	0.0640	2.0710	1.4391
No log	9.0	18	1.6134	0.0411	1.6139	1.2704
No log	10.0	20	1.4137	0.0665	1.4140	1.1891
No log	11.0	22	1.0925	0.0213	1.0930	1.0455
No log	12.0	24	0.8952	0.0280	0.8956	0.9464
No log	13.0	26	0.7396	0.4640	0.7399	0.8602
No log	14.0	28	0.6826	0.3911	0.6827	0.8263
No log	15.0	30	0.6610	0.4978	0.6610	0.8130
No log	16.0	32	0.5472	0.5146	0.5471	0.7397
No log	17.0	34	0.6072	0.4628	0.6071	0.7792
No log	18.0	36	0.5641	0.5596	0.5638	0.7509
No log	19.0	38	0.5072	0.5455	0.5070	0.7121
No log	20.0	40	0.5884	0.4413	0.5882	0.7669
No log	21.0	42	0.5649	0.4199	0.5647	0.7515
No log	22.0	44	0.5352	0.4464	0.5352	0.7316
No log	23.0	46	0.5184	0.4777	0.5184	0.7200
No log	24.0	48	0.4647	0.5565	0.4645	0.6816
No log	25.0	50	0.5062	0.5317	0.5061	0.7114
No log	26.0	52	0.5038	0.5353	0.5037	0.7097
No log	27.0	54	0.4898	0.5282	0.4895	0.6997
No log	28.0	56	0.4683	0.5519	0.4683	0.6843
No log	29.0	58	0.4677	0.5403	0.4677	0.6839
No log	30.0	60	0.4652	0.5827	0.4653	0.6821
No log	31.0	62	0.5057	0.5738	0.5058	0.7112
No log	32.0	64	0.5796	0.5292	0.5797	0.7614
No log	33.0	66	0.5291	0.5782	0.5292	0.7274
No log	34.0	68	0.7362	0.4567	0.7364	0.8581
No log	35.0	70	0.5816	0.5286	0.5819	0.7628
No log	36.0	72	0.5847	0.5655	0.5847	0.7647
No log	37.0	74	0.5276	0.5890	0.5277	0.7264
No log	38.0	76	0.6564	0.5429	0.6565	0.8102
No log	39.0	78	0.5234	0.5678	0.5235	0.7236
No log	40.0	80	0.5369	0.5665	0.5370	0.7328
No log	41.0	82	0.6274	0.5238	0.6277	0.7923
No log	42.0	84	0.5549	0.5436	0.5549	0.7449
No log	43.0	86	0.5309	0.5583	0.5310	0.7287
No log	44.0	88	0.5590	0.5594	0.5592	0.7478
No log	45.0	90	0.5126	0.5972	0.5126	0.7160
No log	46.0	92	0.5023	0.6152	0.5023	0.7087
No log	47.0	94	0.5187	0.6075	0.5187	0.7202
No log	48.0	96	0.5093	0.6072	0.5092	0.7136
No log	49.0	98	0.5153	0.5765	0.5153	0.7178
No log	50.0	100	0.5987	0.5487	0.5988	0.7738
No log	51.0	102	0.5307	0.5962	0.5306	0.7284
No log	52.0	104	0.6030	0.5574	0.6028	0.7764
No log	53.0	106	0.5160	0.6068	0.5158	0.7182
No log	54.0	108	0.6180	0.5592	0.6180	0.7861
No log	55.0	110	0.6284	0.5663	0.6283	0.7926
No log	56.0	112	0.4745	0.6036	0.4743	0.6887
No log	57.0	114	0.4964	0.5780	0.4963	0.7045
No log	58.0	116	0.5022	0.5707	0.5022	0.7086
No log	59.0	118	0.5241	0.5987	0.5240	0.7239
No log	60.0	120	0.5038	0.6099	0.5036	0.7097
No log	61.0	122	0.5130	0.6117	0.5129	0.7162
No log	62.0	124	0.5428	0.6149	0.5426	0.7366
No log	63.0	126	0.5809	0.6052	0.5809	0.7621
No log	64.0	128	0.5295	0.6083	0.5293	0.7275
No log	65.0	130	0.5384	0.5863	0.5383	0.7337
No log	66.0	132	0.5458	0.5926	0.5457	0.7387
No log	67.0	134	0.5469	0.5949	0.5469	0.7395
No log	68.0	136	0.5111	0.5975	0.5111	0.7149
No log	69.0	138	0.5359	0.5791	0.5358	0.7320
No log	70.0	140	0.5218	0.5757	0.5217	0.7223
No log	71.0	142	0.5249	0.5719	0.5248	0.7244
No log	72.0	144	0.5223	0.5751	0.5222	0.7226
No log	73.0	146	0.5009	0.6120	0.5008	0.7077
No log	74.0	148	0.5002	0.6267	0.5000	0.7071
No log	75.0	150	0.4939	0.6257	0.4937	0.7027
No log	76.0	152	0.5021	0.6250	0.5020	0.7085
No log	77.0	154	0.5144	0.6179	0.5143	0.7171
No log	78.0	156	0.5312	0.5930	0.5311	0.7288
No log	79.0	158	0.5279	0.6000	0.5278	0.7265
No log	80.0	160	0.5160	0.5999	0.5159	0.7183
No log	81.0	162	0.5233	0.6131	0.5232	0.7233
No log	82.0	164	0.5128	0.6154	0.5127	0.7161
No log	83.0	166	0.4973	0.6067	0.4972	0.7052
No log	84.0	168	0.5043	0.6182	0.5042	0.7101
No log	85.0	170	0.5332	0.6172	0.5331	0.7302
No log	86.0	172	0.5376	0.6173	0.5375	0.7332
No log	87.0	174	0.5175	0.6172	0.5174	0.7193
No log	88.0	176	0.5194	0.6088	0.5193	0.7206
No log	89.0	178	0.5240	0.6075	0.5238	0.7238
No log	90.0	180	0.5382	0.6015	0.5381	0.7335
No log	91.0	182	0.5456	0.5993	0.5455	0.7386
No log	92.0	184	0.5429	0.5970	0.5428	0.7367
No log	93.0	186	0.5341	0.5984	0.5339	0.7307
No log	94.0	188	0.5264	0.6118	0.5262	0.7254
No log	95.0	190	0.5245	0.5997	0.5242	0.7240
No log	96.0	192	0.5209	0.6102	0.5207	0.7216
No log	97.0	194	0.5174	0.6185	0.5172	0.7192
No log	98.0	196	0.5162	0.6186	0.5160	0.7183
No log	99.0	198	0.5164	0.6112	0.5162	0.7185
No log	100.0	200	0.5162	0.6112	0.5160	0.7183

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