Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold0

Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold0 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: be6b94e8

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Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold0

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: 0.5111
Qwk: 0.5724
Mse: 0.5111
Rmse: 0.7149

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	9.0978	0.0	9.0978	3.0163
No log	2.0	4	7.6464	0.0	7.6464	2.7652
No log	3.0	6	6.8538	0.0	6.8538	2.6180
No log	4.0	8	5.9710	0.0077	5.9710	2.4436
No log	5.0	10	5.1529	0.0115	5.1529	2.2700
No log	6.0	12	4.3112	0.0039	4.3112	2.0763
No log	7.0	14	3.5502	0.0	3.5502	1.8842
No log	8.0	16	2.8166	0.0	2.8166	1.6783
No log	9.0	18	2.1379	0.0689	2.1379	1.4622
No log	10.0	20	1.6280	0.0316	1.6280	1.2759
No log	11.0	22	1.2950	0.0316	1.2950	1.1380
No log	12.0	24	1.0457	0.0316	1.0457	1.0226
No log	13.0	26	0.8458	0.2120	0.8458	0.9197
No log	14.0	28	0.7155	0.4564	0.7155	0.8459
No log	15.0	30	0.6717	0.4756	0.6717	0.8195
No log	16.0	32	0.6747	0.4666	0.6747	0.8214
No log	17.0	34	0.7902	0.4505	0.7902	0.8889
No log	18.0	36	0.6376	0.5314	0.6376	0.7985
No log	19.0	38	0.6446	0.5368	0.6446	0.8029
No log	20.0	40	0.4853	0.5498	0.4853	0.6966
No log	21.0	42	0.6914	0.4992	0.6914	0.8315
No log	22.0	44	0.6922	0.4122	0.6922	0.8320
No log	23.0	46	0.6597	0.4342	0.6597	0.8122
No log	24.0	48	0.7140	0.4703	0.7140	0.8450
No log	25.0	50	0.6447	0.5186	0.6447	0.8030
No log	26.0	52	0.6133	0.5468	0.6133	0.7831
No log	27.0	54	0.5730	0.5887	0.5730	0.7570
No log	28.0	56	0.5659	0.5808	0.5659	0.7522
No log	29.0	58	0.5524	0.6264	0.5524	0.7432
No log	30.0	60	0.5736	0.5485	0.5736	0.7574
No log	31.0	62	0.5155	0.5898	0.5155	0.7180
No log	32.0	64	0.5369	0.5745	0.5369	0.7328
No log	33.0	66	0.6155	0.5906	0.6155	0.7845
No log	34.0	68	0.5593	0.5717	0.5593	0.7479
No log	35.0	70	0.5347	0.5869	0.5347	0.7312
No log	36.0	72	0.5053	0.5677	0.5053	0.7108
No log	37.0	74	0.5184	0.5951	0.5184	0.7200
No log	38.0	76	0.4983	0.5736	0.4983	0.7059
No log	39.0	78	0.5442	0.6163	0.5442	0.7377
No log	40.0	80	0.5326	0.6145	0.5326	0.7298
No log	41.0	82	0.5938	0.6204	0.5938	0.7706
No log	42.0	84	0.5005	0.6206	0.5005	0.7074
No log	43.0	86	0.4915	0.6186	0.4915	0.7011
No log	44.0	88	0.7338	0.5657	0.7338	0.8566
No log	45.0	90	0.4974	0.6156	0.4974	0.7053
No log	46.0	92	0.4870	0.5851	0.4870	0.6979
No log	47.0	94	0.5614	0.6046	0.5614	0.7493
No log	48.0	96	0.5689	0.6097	0.5689	0.7542
No log	49.0	98	0.4942	0.5862	0.4942	0.7030
No log	50.0	100	0.5885	0.6055	0.5885	0.7671
No log	51.0	102	0.5477	0.6386	0.5477	0.7401
No log	52.0	104	0.6121	0.5868	0.6121	0.7824
No log	53.0	106	0.4958	0.6094	0.4958	0.7041
No log	54.0	108	0.5003	0.6069	0.5003	0.7073
No log	55.0	110	0.5284	0.6372	0.5284	0.7269
No log	56.0	112	0.5261	0.6183	0.5261	0.7254
No log	57.0	114	0.5281	0.6169	0.5281	0.7267
No log	58.0	116	0.5180	0.6073	0.5180	0.7197
No log	59.0	118	0.4784	0.5941	0.4784	0.6917
No log	60.0	120	0.5140	0.6198	0.5140	0.7169
No log	61.0	122	0.4902	0.5770	0.4902	0.7002
No log	62.0	124	0.5271	0.6144	0.5271	0.7260
No log	63.0	126	0.5197	0.5872	0.5197	0.7209
No log	64.0	128	0.5144	0.6140	0.5144	0.7172
No log	65.0	130	0.5632	0.5969	0.5632	0.7505
No log	66.0	132	0.4763	0.6083	0.4763	0.6901
No log	67.0	134	0.4788	0.6003	0.4788	0.6919
No log	68.0	136	0.5189	0.6090	0.5189	0.7204
No log	69.0	138	0.4946	0.5889	0.4946	0.7033
No log	70.0	140	0.5042	0.6029	0.5042	0.7101
No log	71.0	142	0.5998	0.5972	0.5998	0.7744
No log	72.0	144	0.5327	0.6210	0.5327	0.7299
No log	73.0	146	0.5071	0.5712	0.5071	0.7121
No log	74.0	148	0.4953	0.5975	0.4953	0.7038
No log	75.0	150	0.6276	0.5859	0.6276	0.7922
No log	76.0	152	0.5511	0.6059	0.5511	0.7424
No log	77.0	154	0.4990	0.6015	0.4990	0.7064
No log	78.0	156	0.5112	0.6107	0.5112	0.7150
No log	79.0	158	0.5918	0.5978	0.5918	0.7693
No log	80.0	160	0.5203	0.6061	0.5203	0.7213
No log	81.0	162	0.5111	0.5724	0.5111	0.7149

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