Version22NewTestASAP_FineTuningBERT_AugV22_k10_task1_organization_k10_k10_fold1

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k10_task1_organization_k10_k10_fold1

Version22NewTestASAP_FineTuningBERT_AugV22_k10_task1_organization_k10_k10_fold1 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 16, 2025
Updated: Mar 16, 2025
SHA: d8b1c961

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Base models

Model card

From the Hugging Face model README

Version22NewTestASAP_FineTuningBERT_AugV22_k10_task1_organization_k10_k10_fold1

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: 0.8350
Qwk: 0.4164
Mse: 0.8346
Rmse: 0.9135

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	6	6.3058	0.0	6.3036	2.5107
No log	2.0	12	2.6656	0.0	2.6637	1.6321
No log	3.0	18	1.3925	0.0	1.3910	1.1794
No log	4.0	24	1.5412	0.0	1.5396	1.2408
No log	5.0	30	1.7695	0.1079	1.7681	1.3297
No log	6.0	36	1.1423	0.0	1.1410	1.0682
No log	7.0	42	1.3631	0.1370	1.3617	1.1669
No log	8.0	48	1.2802	0.2438	1.2789	1.1309
No log	9.0	54	1.6033	0.2337	1.6023	1.2658
No log	10.0	60	1.1066	0.3616	1.1061	1.0517
No log	11.0	66	0.9497	0.3689	0.9495	0.9744
No log	12.0	72	0.9409	0.3678	0.9405	0.9698
No log	13.0	78	1.4958	0.2742	1.4950	1.2227
No log	14.0	84	1.4441	0.2201	1.4434	1.2014
No log	15.0	90	1.5283	0.2541	1.5275	1.2359
No log	16.0	96	1.3259	0.2638	1.3252	1.1512
No log	17.0	102	1.5041	0.2281	1.5035	1.2262
No log	18.0	108	1.1987	0.2799	1.1984	1.0947
No log	19.0	114	0.9438	0.3396	0.9437	0.9714
No log	20.0	120	0.9654	0.3285	0.9653	0.9825
No log	21.0	126	1.2404	0.2856	1.2402	1.1136
No log	22.0	132	0.9748	0.3439	0.9747	0.9873
No log	23.0	138	0.9417	0.3540	0.9417	0.9704
No log	24.0	144	0.9118	0.3571	0.9118	0.9549
No log	25.0	150	0.8756	0.3964	0.8754	0.9356
No log	26.0	156	0.8936	0.3901	0.8935	0.9453
No log	27.0	162	0.8136	0.4271	0.8136	0.9020
No log	28.0	168	0.8167	0.4232	0.8168	0.9037
No log	29.0	174	1.1144	0.3122	1.1143	1.0556
No log	30.0	180	0.8080	0.4547	0.8080	0.8989
No log	31.0	186	0.7267	0.4856	0.7267	0.8525
No log	32.0	192	0.9614	0.4246	0.9613	0.9804
No log	33.0	198	1.0680	0.3400	1.0680	1.0334
No log	34.0	204	0.8072	0.4565	0.8074	0.8985
No log	35.0	210	0.7555	0.4656	0.7555	0.8692
No log	36.0	216	1.1070	0.3098	1.1068	1.0521
No log	37.0	222	0.7438	0.4705	0.7439	0.8625
No log	38.0	228	0.7907	0.4656	0.7908	0.8892
No log	39.0	234	0.8834	0.4520	0.8834	0.9399
No log	40.0	240	0.7991	0.4245	0.7993	0.8941
No log	41.0	246	0.8160	0.4507	0.8162	0.9034
No log	42.0	252	1.0033	0.3634	1.0032	1.0016
No log	43.0	258	0.7829	0.4465	0.7829	0.8848
No log	44.0	264	0.7881	0.4602	0.7882	0.8878
No log	45.0	270	0.9336	0.3811	0.9336	0.9662
No log	46.0	276	0.7650	0.4892	0.7652	0.8748
No log	47.0	282	0.7073	0.5045	0.7075	0.8411
No log	48.0	288	0.7388	0.4808	0.7391	0.8597
No log	49.0	294	0.7579	0.4530	0.7581	0.8707
No log	50.0	300	0.7385	0.4855	0.7387	0.8595
No log	51.0	306	0.7535	0.4774	0.7535	0.8681
No log	52.0	312	0.7592	0.4675	0.7593	0.8714
No log	53.0	318	0.9116	0.4401	0.9113	0.9546
No log	54.0	324	0.8349	0.4457	0.8346	0.9136
No log	55.0	330	0.7907	0.4209	0.7905	0.8891
No log	56.0	336	0.7430	0.4433	0.7427	0.8618
No log	57.0	342	0.7591	0.4765	0.7588	0.8711
No log	58.0	348	0.6969	0.4972	0.6967	0.8347
No log	59.0	354	0.7096	0.4845	0.7095	0.8423
No log	60.0	360	0.8196	0.4619	0.8194	0.9052
No log	61.0	366	0.8457	0.4496	0.8453	0.9194
No log	62.0	372	0.7376	0.4676	0.7375	0.8588
No log	63.0	378	0.7484	0.4193	0.7481	0.8649
No log	64.0	384	0.7062	0.4770	0.7060	0.8402
No log	65.0	390	0.7065	0.4387	0.7062	0.8403
No log	66.0	396	0.7621	0.4724	0.7617	0.8727
No log	67.0	402	0.7928	0.4548	0.7923	0.8901
No log	68.0	408	0.7620	0.4561	0.7616	0.8727
No log	69.0	414	0.8103	0.4293	0.8099	0.8999
No log	70.0	420	0.7294	0.4544	0.7291	0.8538
No log	71.0	426	0.7591	0.4407	0.7587	0.8710
No log	72.0	432	0.7145	0.4292	0.7143	0.8452
No log	73.0	438	0.7924	0.4257	0.7920	0.8899
No log	74.0	444	0.6882	0.4643	0.6880	0.8295
No log	75.0	450	0.7600	0.4432	0.7597	0.8716
No log	76.0	456	0.6951	0.4622	0.6949	0.8336
No log	77.0	462	0.8350	0.4164	0.8346	0.9135

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 16, 2025
Updated: Mar 16, 2025