BERT_V8_sp10_lw40_ex50_lo100_k2_k2_fold3

genki10/BERT_V8_sp10_lw40_ex50_lo100_k2_k2_fold3

BERT_V8_sp10_lw40_ex50_lo100_k2_k2_fold3 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: f987b6fa

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Base models

Model card

From the Hugging Face model README

BERT_V8_sp10_lw40_ex50_lo100_k2_k2_fold3

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: 0.6316
Qwk: 0.5390
Mse: 0.6315
Rmse: 0.7947

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	10.0642	0.0070	10.0628	3.1722
No log	2.0	4	7.7402	0.0	7.7390	2.7819
No log	3.0	6	5.7190	0.0623	5.7179	2.3912
No log	4.0	8	4.4655	0.0260	4.4645	2.1129
No log	5.0	10	3.6667	0.0114	3.6658	1.9146
No log	6.0	12	2.7481	0.0	2.7472	1.6575
No log	7.0	14	2.1096	0.0833	2.1088	1.4522
No log	8.0	16	1.6122	0.0463	1.6115	1.2694
No log	9.0	18	1.3155	0.0401	1.3149	1.1467
No log	10.0	20	1.0623	0.0302	1.0617	1.0304
No log	11.0	22	0.8795	0.3405	0.8790	0.9375
No log	12.0	24	0.8694	0.2688	0.8688	0.9321
No log	13.0	26	0.7253	0.4295	0.7249	0.8514
No log	14.0	28	0.6725	0.4057	0.6722	0.8199
No log	15.0	30	0.7272	0.4525	0.7270	0.8526
No log	16.0	32	0.6922	0.4728	0.6920	0.8319
No log	17.0	34	0.5430	0.5322	0.5430	0.7369
No log	18.0	36	0.7537	0.5087	0.7537	0.8681
No log	19.0	38	0.7497	0.5131	0.7498	0.8659
No log	20.0	40	0.5542	0.5669	0.5544	0.7446
No log	21.0	42	0.5729	0.5714	0.5732	0.7571
No log	22.0	44	0.6768	0.5197	0.6770	0.8228
No log	23.0	46	0.5805	0.5872	0.5809	0.7621
No log	24.0	48	0.5901	0.5919	0.5905	0.7684
No log	25.0	50	0.6259	0.5787	0.6260	0.7912
No log	26.0	52	0.6842	0.5443	0.6846	0.8274
No log	27.0	54	0.6459	0.5582	0.6461	0.8038
No log	28.0	56	0.8557	0.4955	0.8551	0.9247
No log	29.0	58	0.6223	0.5782	0.6224	0.7889
No log	30.0	60	0.7084	0.5343	0.7088	0.8419
No log	31.0	62	0.6123	0.5694	0.6127	0.7828
No log	32.0	64	0.9525	0.4148	0.9526	0.9760
No log	33.0	66	0.7980	0.4777	0.7982	0.8934
No log	34.0	68	0.6196	0.5586	0.6199	0.7873
No log	35.0	70	0.6990	0.5444	0.6993	0.8362
No log	36.0	72	0.5997	0.5856	0.5999	0.7745
No log	37.0	74	0.7856	0.4579	0.7856	0.8863
No log	38.0	76	0.6865	0.5047	0.6865	0.8286
No log	39.0	78	0.6322	0.5595	0.6324	0.7952
No log	40.0	80	0.6160	0.5934	0.6161	0.7849
No log	41.0	82	0.6981	0.5267	0.6980	0.8355
No log	42.0	84	0.6277	0.5702	0.6277	0.7923
No log	43.0	86	0.6149	0.5914	0.6151	0.7843
No log	44.0	88	0.6486	0.5535	0.6488	0.8055
No log	45.0	90	0.6268	0.5645	0.6269	0.7918
No log	46.0	92	0.6114	0.5670	0.6115	0.7820
No log	47.0	94	0.6167	0.5622	0.6167	0.7853
No log	48.0	96	0.6254	0.5472	0.6253	0.7908
No log	49.0	98	0.6108	0.5626	0.6109	0.7816
No log	50.0	100	0.6008	0.5650	0.6008	0.7751
No log	51.0	102	0.6337	0.5389	0.6336	0.7960
No log	52.0	104	0.6448	0.5514	0.6447	0.8029
No log	53.0	106	0.5994	0.5946	0.5994	0.7742
No log	54.0	108	0.6038	0.5905	0.6039	0.7771
No log	55.0	110	0.6945	0.5204	0.6945	0.8333
No log	56.0	112	0.7058	0.4943	0.7057	0.8400
No log	57.0	114	0.5988	0.5833	0.5988	0.7738
No log	58.0	116	0.5884	0.5834	0.5883	0.7670
No log	59.0	118	0.6311	0.5273	0.6309	0.7943
No log	60.0	120	0.6371	0.5198	0.6369	0.7980
No log	61.0	122	0.5920	0.5885	0.5919	0.7693
No log	62.0	124	0.6019	0.5748	0.6019	0.7758
No log	63.0	126	0.6976	0.5088	0.6975	0.8352
No log	64.0	128	0.7296	0.4961	0.7296	0.8542
No log	65.0	130	0.6359	0.5286	0.6359	0.7975
No log	66.0	132	0.6116	0.5747	0.6116	0.7820
No log	67.0	134	0.6416	0.5479	0.6415	0.8009
No log	68.0	136	0.6348	0.5435	0.6347	0.7967
No log	69.0	138	0.5840	0.5858	0.5840	0.7642
No log	70.0	140	0.5895	0.6019	0.5895	0.7678
No log	71.0	142	0.6354	0.5607	0.6355	0.7972
No log	72.0	144	0.6269	0.5576	0.6270	0.7918
No log	73.0	146	0.6232	0.5799	0.6232	0.7894
No log	74.0	148	0.6143	0.5848	0.6143	0.7838
No log	75.0	150	0.6393	0.5529	0.6393	0.7996
No log	76.0	152	0.6538	0.5430	0.6538	0.8086
No log	77.0	154	0.6299	0.5415	0.6300	0.7937
No log	78.0	156	0.6236	0.5523	0.6237	0.7897
No log	79.0	158	0.6045	0.5679	0.6045	0.7775
No log	80.0	160	0.6007	0.5609	0.6007	0.7750
No log	81.0	162	0.6132	0.5525	0.6132	0.7831
No log	82.0	164	0.6080	0.5509	0.6080	0.7797
No log	83.0	166	0.6131	0.5479	0.6131	0.7830
No log	84.0	168	0.6203	0.5326	0.6203	0.7876
No log	85.0	170	0.6104	0.5580	0.6103	0.7812
No log	86.0	172	0.6090	0.5594	0.6089	0.7803
No log	87.0	174	0.6031	0.5576	0.6031	0.7766
No log	88.0	176	0.6137	0.5431	0.6137	0.7834
No log	89.0	178	0.6337	0.5338	0.6336	0.7960
No log	90.0	180	0.6273	0.5360	0.6273	0.7920
No log	91.0	182	0.6273	0.5313	0.6273	0.7920
No log	92.0	184	0.6380	0.5387	0.6380	0.7987
No log	93.0	186	0.6320	0.5332	0.6320	0.7950
No log	94.0	188	0.6233	0.5321	0.6232	0.7895
No log	95.0	190	0.6243	0.5397	0.6242	0.7901
No log	96.0	192	0.6304	0.5398	0.6303	0.7939
No log	97.0	194	0.6348	0.5362	0.6347	0.7967
No log	98.0	196	0.6344	0.5413	0.6343	0.7964
No log	99.0	198	0.6330	0.5390	0.6329	0.7956
No log	100.0	200	0.6316	0.5390	0.6315	0.7947

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