Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold4

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold4

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold4 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: Mar 17, 2025
Updated: Mar 17, 2025
SHA: 6ad5f940

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Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold4

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: 0.6005
Qwk: 0.6396
Mse: 0.6005
Rmse: 0.7749

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	1	10.6901	0.0061	10.6901	3.2696
No log	2.0	2	9.6989	0.0018	9.6989	3.1143
No log	3.0	3	8.1737	0.0018	8.1737	2.8590
No log	4.0	4	7.3989	0.0018	7.3989	2.7201
No log	5.0	5	6.4687	0.0014	6.4687	2.5434
No log	6.0	6	5.6651	0.0399	5.6651	2.3801
No log	7.0	7	5.0032	0.0245	5.0032	2.2368
No log	8.0	8	4.2361	0.0128	4.2361	2.0582
No log	9.0	9	3.6636	0.0118	3.6636	1.9140
No log	10.0	10	3.2327	0.0079	3.2327	1.7980
No log	11.0	11	2.7865	0.0072	2.7865	1.6693
No log	12.0	12	2.2889	0.1017	2.2889	1.5129
No log	13.0	13	2.0584	0.1320	2.0584	1.4347
No log	14.0	14	1.9204	0.1336	1.9204	1.3858
No log	15.0	15	1.5413	0.0617	1.5413	1.2415
No log	16.0	16	1.3460	0.0445	1.3460	1.1602
No log	17.0	17	1.2068	0.0445	1.2068	1.0985
No log	18.0	18	1.0639	0.0445	1.0639	1.0315
No log	19.0	19	0.9634	0.0420	0.9634	0.9815
No log	20.0	20	0.8812	0.1596	0.8812	0.9387
No log	21.0	21	0.9696	0.1381	0.9696	0.9847
No log	22.0	22	0.8941	0.2206	0.8941	0.9456
No log	23.0	23	0.7289	0.4425	0.7289	0.8537
No log	24.0	24	0.6841	0.4457	0.6841	0.8271
No log	25.0	25	0.6494	0.4655	0.6494	0.8058
No log	26.0	26	0.7191	0.3985	0.7191	0.8480
No log	27.0	27	0.7216	0.4220	0.7216	0.8495
No log	28.0	28	0.6076	0.4781	0.6076	0.7795
No log	29.0	29	0.5610	0.5117	0.5610	0.7490
No log	30.0	30	0.5767	0.5141	0.5767	0.7594
No log	31.0	31	0.7095	0.5157	0.7095	0.8423
No log	32.0	32	0.6998	0.5252	0.6998	0.8365
No log	33.0	33	0.5544	0.5595	0.5544	0.7446
No log	34.0	34	0.5315	0.5499	0.5315	0.7291
No log	35.0	35	0.5712	0.5048	0.5712	0.7558
No log	36.0	36	0.5354	0.5354	0.5354	0.7317
No log	37.0	37	0.5230	0.5708	0.5230	0.7232
No log	38.0	38	0.5951	0.5328	0.5951	0.7715
No log	39.0	39	0.5766	0.5463	0.5766	0.7593
No log	40.0	40	0.5018	0.6239	0.5018	0.7083
No log	41.0	41	0.5232	0.6110	0.5232	0.7233
No log	42.0	42	0.5397	0.6272	0.5397	0.7346
No log	43.0	43	0.4993	0.6610	0.4993	0.7066
No log	44.0	44	0.5405	0.6196	0.5405	0.7352
No log	45.0	45	0.5930	0.6095	0.5930	0.7700
No log	46.0	46	0.5467	0.6369	0.5467	0.7394
No log	47.0	47	0.4994	0.6640	0.4994	0.7067
No log	48.0	48	0.5130	0.6583	0.5130	0.7162
No log	49.0	49	0.5071	0.6604	0.5071	0.7121
No log	50.0	50	0.5095	0.6558	0.5095	0.7138
No log	51.0	51	0.5275	0.6709	0.5275	0.7263
No log	52.0	52	0.5181	0.6728	0.5181	0.7198
No log	53.0	53	0.5195	0.6665	0.5195	0.7208
No log	54.0	54	0.5180	0.6761	0.5180	0.7197
No log	55.0	55	0.5100	0.6789	0.5100	0.7141
No log	56.0	56	0.4894	0.6776	0.4894	0.6996
No log	57.0	57	0.4910	0.6583	0.4910	0.7007
No log	58.0	58	0.5741	0.6324	0.5741	0.7577
No log	59.0	59	0.5985	0.6239	0.5985	0.7737
No log	60.0	60	0.5451	0.6473	0.5451	0.7383
No log	61.0	61	0.5365	0.6544	0.5365	0.7325
No log	62.0	62	0.5889	0.6296	0.5889	0.7674
No log	63.0	63	0.5666	0.6397	0.5666	0.7527
No log	64.0	64	0.5236	0.6506	0.5236	0.7236
No log	65.0	65	0.5333	0.6519	0.5333	0.7303
No log	66.0	66	0.5963	0.6364	0.5963	0.7722
No log	67.0	67	0.6234	0.6405	0.6234	0.7896
No log	68.0	68	0.5996	0.6446	0.5996	0.7744
No log	69.0	69	0.6164	0.6343	0.6164	0.7851
No log	70.0	70	0.6918	0.6084	0.6918	0.8317
No log	71.0	71	0.6923	0.6053	0.6923	0.8321
No log	72.0	72	0.6178	0.6252	0.6178	0.7860
No log	73.0	73	0.5809	0.6340	0.5809	0.7622
No log	74.0	74	0.5932	0.6222	0.5932	0.7702
No log	75.0	75	0.6179	0.6185	0.6179	0.7860
No log	76.0	76	0.5812	0.6398	0.5812	0.7623
No log	77.0	77	0.5358	0.6516	0.5358	0.7320
No log	78.0	78	0.5356	0.6475	0.5356	0.7319
No log	79.0	79	0.5545	0.6379	0.5545	0.7446
No log	80.0	80	0.6328	0.6385	0.6328	0.7955
No log	81.0	81	0.6716	0.6285	0.6716	0.8195
No log	82.0	82	0.6412	0.6359	0.6412	0.8008
No log	83.0	83	0.5883	0.6449	0.5883	0.7670
No log	84.0	84	0.5853	0.6358	0.5853	0.7650
No log	85.0	85	0.6005	0.6396	0.6005	0.7749

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 17, 2025
Updated: Mar 17, 2025