Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_fold2

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_fold2

Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_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: Mar 18, 2025
Updated: Mar 18, 2025
SHA: fd638e49

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Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_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: 0.7971
Qwk: 0.5256
Mse: 0.7970
Rmse: 0.8928

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	3	6.9439	0.0	6.9442	2.6352
No log	2.0	6	4.3462	0.0007	4.3466	2.0849
No log	3.0	9	3.0081	0.0	3.0085	1.7345
No log	4.0	12	2.0269	0.0765	2.0274	1.4239
No log	5.0	15	1.4098	0.0	1.4102	1.1875
No log	6.0	18	1.0280	0.0	1.0284	1.0141
No log	7.0	21	0.8793	0.1083	0.8796	0.9379
No log	8.0	24	0.6778	0.4236	0.6779	0.8234
No log	9.0	27	0.6734	0.4697	0.6736	0.8207
No log	10.0	30	0.5593	0.4887	0.5595	0.7480
No log	11.0	33	0.5648	0.4821	0.5650	0.7517
No log	12.0	36	0.4898	0.5146	0.4899	0.6999
No log	13.0	39	0.5679	0.5052	0.5681	0.7537
No log	14.0	42	0.5705	0.3954	0.5705	0.7553
No log	15.0	45	0.6023	0.5005	0.6025	0.7762
No log	16.0	48	0.6470	0.5086	0.6471	0.8044
No log	17.0	51	0.6800	0.4779	0.6802	0.8247
No log	18.0	54	0.8414	0.4079	0.8416	0.9174
No log	19.0	57	0.6830	0.4886	0.6832	0.8265
No log	20.0	60	0.7336	0.4693	0.7337	0.8565
No log	21.0	63	0.6748	0.4994	0.6747	0.8214
No log	22.0	66	0.7185	0.5189	0.7185	0.8477
No log	23.0	69	0.8087	0.5009	0.8088	0.8993
No log	24.0	72	0.6864	0.5453	0.6864	0.8285
No log	25.0	75	0.8420	0.5258	0.8420	0.9176
No log	26.0	78	0.7099	0.5341	0.7099	0.8426
No log	27.0	81	0.7498	0.5448	0.7497	0.8659
No log	28.0	84	0.6262	0.5563	0.6262	0.7913
No log	29.0	87	0.8485	0.5173	0.8484	0.9211
No log	30.0	90	0.6342	0.5443	0.6341	0.7963
No log	31.0	93	0.6262	0.5718	0.6261	0.7912
No log	32.0	96	0.7352	0.5290	0.7351	0.8574
No log	33.0	99	0.8582	0.5143	0.8581	0.9264
No log	34.0	102	0.7711	0.5332	0.7711	0.8781
No log	35.0	105	0.7090	0.5422	0.7090	0.8420
No log	36.0	108	0.7742	0.5415	0.7742	0.8799
No log	37.0	111	0.7223	0.5549	0.7222	0.8498
No log	38.0	114	0.8899	0.5085	0.8899	0.9433
No log	39.0	117	0.6659	0.5097	0.6658	0.8159
No log	40.0	120	0.6144	0.5690	0.6144	0.7838
No log	41.0	123	1.4006	0.3727	1.4005	1.1834
No log	42.0	126	0.6180	0.5526	0.6180	0.7862
No log	43.0	129	0.5923	0.5457	0.5924	0.7696
No log	44.0	132	0.8872	0.5423	0.8873	0.9419
No log	45.0	135	0.6088	0.5755	0.6088	0.7803
No log	46.0	138	0.7768	0.5363	0.7769	0.8814
No log	47.0	141	0.5734	0.5502	0.5735	0.7573
No log	48.0	144	0.7156	0.5215	0.7157	0.8460
No log	49.0	147	0.6373	0.5608	0.6374	0.7984
No log	50.0	150	0.9541	0.5005	0.9541	0.9768
No log	51.0	153	0.7477	0.5621	0.7477	0.8647
No log	52.0	156	0.8342	0.5343	0.8343	0.9134
No log	53.0	159	0.6776	0.5726	0.6777	0.8232
No log	54.0	162	0.5734	0.5411	0.5735	0.7573
No log	55.0	165	0.7219	0.5321	0.7220	0.8497
No log	56.0	168	0.6266	0.5573	0.6266	0.7916
No log	57.0	171	0.6529	0.5677	0.6530	0.8081
No log	58.0	174	0.6775	0.5695	0.6776	0.8231
No log	59.0	177	0.6350	0.5784	0.6351	0.7969
No log	60.0	180	0.7682	0.5374	0.7682	0.8765
No log	61.0	183	0.7313	0.5514	0.7313	0.8552
No log	62.0	186	0.6427	0.5695	0.6428	0.8017
No log	63.0	189	0.7047	0.5637	0.7047	0.8394
No log	64.0	192	0.5801	0.5349	0.5801	0.7616
No log	65.0	195	0.7481	0.5397	0.7481	0.8649
No log	66.0	198	0.7291	0.5524	0.7291	0.8538
No log	67.0	201	0.5818	0.5377	0.5818	0.7628
No log	68.0	204	0.8010	0.5272	0.8009	0.8949
No log	69.0	207	0.5862	0.5304	0.5863	0.7657
No log	70.0	210	0.6164	0.5417	0.6164	0.7851
No log	71.0	213	0.8342	0.5378	0.8341	0.9133
No log	72.0	216	0.7637	0.5437	0.7637	0.8739
No log	73.0	219	0.9153	0.5213	0.9153	0.9567
No log	74.0	222	0.7301	0.5410	0.7301	0.8545
No log	75.0	225	0.7627	0.5375	0.7626	0.8733
No log	76.0	228	0.6142	0.5401	0.6142	0.7837
No log	77.0	231	0.6639	0.5396	0.6639	0.8148
No log	78.0	234	0.6605	0.5411	0.6604	0.8127
No log	79.0	237	0.6544	0.5721	0.6543	0.8089
No log	80.0	240	0.7128	0.5632	0.7127	0.8442
No log	81.0	243	0.7029	0.5461	0.7028	0.8383
No log	82.0	246	0.7256	0.5477	0.7255	0.8518
No log	83.0	249	0.7042	0.5662	0.7041	0.8391
No log	84.0	252	0.8015	0.5350	0.8014	0.8952
No log	85.0	255	0.6621	0.5608	0.6621	0.8137
No log	86.0	258	0.6997	0.5649	0.6996	0.8364
No log	87.0	261	0.6662	0.5728	0.6661	0.8162
No log	88.0	264	0.6335	0.5540	0.6334	0.7959
No log	89.0	267	0.7971	0.5256	0.7970	0.8928

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 18, 2025
Updated: Mar 18, 2025