Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold4

genki10/Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold4

Version25NewTestASAP_FineTuningBERT_AugV25_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: 91e17cdf

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Base models

Model card

From the Hugging Face model README

Version25NewTestASAP_FineTuningBERT_AugV25_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: 0.9822
Qwk: 0.5341
Mse: 0.9822
Rmse: 0.9911

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	2	12.6776	0.0	12.6776	3.5606
No log	2.0	4	10.5434	0.0081	10.5434	3.2471
No log	3.0	6	9.8805	0.0018	9.8805	3.1433
No log	4.0	8	8.2826	0.0	8.2826	2.8779
No log	5.0	10	6.9317	0.0	6.9317	2.6328
No log	6.0	12	5.3266	0.0585	5.3266	2.3079
No log	7.0	14	4.1210	0.0079	4.1210	2.0300
No log	8.0	16	3.2459	0.0040	3.2459	1.8016
No log	9.0	18	2.4020	0.0704	2.4020	1.5498
No log	10.0	20	1.8381	0.0572	1.8381	1.3558
No log	11.0	22	1.4400	0.0420	1.4400	1.2000
No log	12.0	24	1.0887	0.0316	1.0887	1.0434
No log	13.0	26	0.8820	0.0802	0.8820	0.9391
No log	14.0	28	0.7891	0.4051	0.7891	0.8883
No log	15.0	30	0.7199	0.4391	0.7199	0.8484
No log	16.0	32	0.6312	0.3853	0.6312	0.7945
No log	17.0	34	0.9100	0.1259	0.9100	0.9540
No log	18.0	36	1.1092	0.1199	1.1092	1.0532
No log	19.0	38	1.1035	0.2301	1.1035	1.0505
No log	20.0	40	1.4685	0.2316	1.4685	1.2118
No log	21.0	42	1.4864	0.2626	1.4864	1.2192
No log	22.0	44	0.8437	0.5419	0.8437	0.9185
No log	23.0	46	0.7602	0.5084	0.7602	0.8719
No log	24.0	48	0.7248	0.4960	0.7248	0.8513
No log	25.0	50	0.6622	0.4963	0.6622	0.8138
No log	26.0	52	0.7386	0.5012	0.7386	0.8594
No log	27.0	54	0.8087	0.4777	0.8087	0.8993
No log	28.0	56	0.6620	0.5093	0.6620	0.8136
No log	29.0	58	0.6960	0.5019	0.6960	0.8343
No log	30.0	60	0.6769	0.5074	0.6769	0.8227
No log	31.0	62	0.6817	0.4659	0.6817	0.8257
No log	32.0	64	0.6334	0.5008	0.6334	0.7959
No log	33.0	66	0.6392	0.5227	0.6392	0.7995
No log	34.0	68	0.7298	0.4684	0.7298	0.8543
No log	35.0	70	0.7649	0.4627	0.7649	0.8746
No log	36.0	72	0.7443	0.5347	0.7443	0.8627
No log	37.0	74	0.7195	0.5276	0.7195	0.8482
No log	38.0	76	0.7384	0.5204	0.7384	0.8593
No log	39.0	78	0.8030	0.5006	0.8030	0.8961
No log	40.0	80	0.7848	0.5140	0.7848	0.8859
No log	41.0	82	0.7570	0.5665	0.7570	0.8700
No log	42.0	84	0.8789	0.5196	0.8789	0.9375
No log	43.0	86	0.8337	0.5255	0.8337	0.9131
No log	44.0	88	0.7227	0.5655	0.7227	0.8501
No log	45.0	90	0.7080	0.5419	0.7080	0.8414
No log	46.0	92	0.8754	0.5408	0.8754	0.9356
No log	47.0	94	1.0002	0.5124	1.0002	1.0001
No log	48.0	96	0.8493	0.5414	0.8493	0.9216
No log	49.0	98	0.7527	0.5462	0.7527	0.8676
No log	50.0	100	0.8078	0.4917	0.8078	0.8988
No log	51.0	102	0.7512	0.5417	0.7512	0.8667
No log	52.0	104	0.7522	0.5528	0.7522	0.8673
No log	53.0	106	1.1174	0.4913	1.1174	1.0571
No log	54.0	108	1.2485	0.4709	1.2485	1.1174
No log	55.0	110	1.0587	0.4417	1.0587	1.0290
No log	56.0	112	0.9393	0.4626	0.9393	0.9692
No log	57.0	114	0.8128	0.5108	0.8128	0.9016
No log	58.0	116	0.6924	0.5891	0.6924	0.8321
No log	59.0	118	0.7192	0.5895	0.7192	0.8480
No log	60.0	120	0.9285	0.5220	0.9285	0.9636
No log	61.0	122	0.9496	0.4802	0.9496	0.9745
No log	62.0	124	0.9340	0.4902	0.9340	0.9664
No log	63.0	126	0.8493	0.5087	0.8493	0.9216
No log	64.0	128	0.7759	0.5143	0.7759	0.8808
No log	65.0	130	0.7968	0.5270	0.7968	0.8926
No log	66.0	132	0.8388	0.5281	0.8388	0.9159
No log	67.0	134	0.8413	0.5160	0.8413	0.9172
No log	68.0	136	0.8207	0.5215	0.8207	0.9059
No log	69.0	138	0.7770	0.5408	0.7770	0.8815
No log	70.0	140	0.7956	0.5283	0.7956	0.8920
No log	71.0	142	0.8360	0.5309	0.8360	0.9143
No log	72.0	144	0.8592	0.5316	0.8592	0.9270
No log	73.0	146	0.8694	0.4858	0.8694	0.9324
No log	74.0	148	0.9161	0.4546	0.9161	0.9571
No log	75.0	150	0.8902	0.4835	0.8902	0.9435
No log	76.0	152	0.7974	0.5376	0.7974	0.8930
No log	77.0	154	0.8030	0.5428	0.8030	0.8961
No log	78.0	156	0.8166	0.5505	0.8166	0.9037
No log	79.0	158	0.8003	0.5470	0.8003	0.8946
No log	80.0	160	0.8708	0.5309	0.8708	0.9332
No log	81.0	162	0.9509	0.5152	0.9509	0.9752
No log	82.0	164	0.9862	0.5227	0.9862	0.9931
No log	83.0	166	0.8647	0.5503	0.8647	0.9299
No log	84.0	168	0.7853	0.5505	0.7853	0.8862
No log	85.0	170	0.7792	0.5411	0.7792	0.8827
No log	86.0	172	0.8007	0.5334	0.8007	0.8948
No log	87.0	174	0.8907	0.5239	0.8907	0.9438
No log	88.0	176	1.0313	0.5211	1.0313	1.0155
No log	89.0	178	0.9822	0.5341	0.9822	0.9911

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