ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold0

genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold0

ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_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 8, 2025
Updated: Mar 8, 2025
SHA: e9e43d59

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Base models

Model card

From the Hugging Face model README

ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_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: 0.7375
Qwk: 0.5133
Mse: 0.7375
Rmse: 0.8588

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	6.7808	0.0	6.7808	2.6040
No log	2.0	12	4.2243	0.0039	4.2243	2.0553
No log	3.0	18	2.0865	0.0944	2.0865	1.4445
No log	4.0	24	1.2136	0.0316	1.2136	1.1016
No log	5.0	30	1.2239	0.0548	1.2239	1.1063
No log	6.0	36	1.0788	0.1002	1.0788	1.0386
No log	7.0	42	1.0530	0.2204	1.0530	1.0262
No log	8.0	48	0.7780	0.3963	0.7780	0.8821
No log	9.0	54	0.7862	0.4230	0.7862	0.8867
No log	10.0	60	0.8138	0.4185	0.8138	0.9021
No log	11.0	66	0.6654	0.4144	0.6654	0.8157
No log	12.0	72	0.7386	0.5279	0.7386	0.8594
No log	13.0	78	0.8035	0.5002	0.8035	0.8964
No log	14.0	84	0.7581	0.5051	0.7581	0.8707
No log	15.0	90	0.7233	0.5089	0.7233	0.8505
No log	16.0	96	0.8051	0.4401	0.8051	0.8972
No log	17.0	102	0.7567	0.4941	0.7567	0.8699
No log	18.0	108	0.8707	0.4863	0.8707	0.9331
No log	19.0	114	1.1974	0.3453	1.1974	1.0943
No log	20.0	120	0.7859	0.4799	0.7859	0.8865
No log	21.0	126	1.0138	0.3648	1.0138	1.0069
No log	22.0	132	0.7047	0.5068	0.7047	0.8395
No log	23.0	138	0.8307	0.4661	0.8307	0.9114
No log	24.0	144	0.7614	0.5073	0.7614	0.8726
No log	25.0	150	0.8226	0.4655	0.8226	0.9070
No log	26.0	156	0.8165	0.4613	0.8165	0.9036
No log	27.0	162	0.9831	0.4721	0.9831	0.9915
No log	28.0	168	1.0394	0.4185	1.0394	1.0195
No log	29.0	174	0.7373	0.5216	0.7373	0.8587
No log	30.0	180	0.9787	0.4235	0.9787	0.9893
No log	31.0	186	0.8639	0.4846	0.8639	0.9295
No log	32.0	192	0.7515	0.5432	0.7515	0.8669
No log	33.0	198	0.9501	0.4566	0.9501	0.9747
No log	34.0	204	0.9949	0.4107	0.9949	0.9974
No log	35.0	210	1.0571	0.4636	1.0571	1.0281
No log	36.0	216	0.9711	0.4803	0.9711	0.9854
No log	37.0	222	0.9057	0.4351	0.9057	0.9517
No log	38.0	228	1.0173	0.4779	1.0173	1.0086
No log	39.0	234	0.8642	0.5086	0.8642	0.9296
No log	40.0	240	0.8859	0.4795	0.8859	0.9412
No log	41.0	246	0.8663	0.4843	0.8663	0.9307
No log	42.0	252	0.8377	0.5049	0.8377	0.9152
No log	43.0	258	0.8322	0.5052	0.8322	0.9122
No log	44.0	264	0.6407	0.5504	0.6407	0.8004
No log	45.0	270	0.9204	0.4813	0.9204	0.9594
No log	46.0	276	0.9213	0.4563	0.9213	0.9598
No log	47.0	282	1.0325	0.4318	1.0325	1.0161
No log	48.0	288	0.6882	0.5536	0.6882	0.8296
No log	49.0	294	0.6289	0.5331	0.6289	0.7931
No log	50.0	300	0.8150	0.5143	0.8150	0.9028
No log	51.0	306	0.8232	0.5139	0.8232	0.9073
No log	52.0	312	0.8732	0.5006	0.8732	0.9344
No log	53.0	318	0.7829	0.4945	0.7829	0.8848
No log	54.0	324	0.7116	0.4936	0.7116	0.8436
No log	55.0	330	0.7365	0.4995	0.7365	0.8582
No log	56.0	336	0.8263	0.4929	0.8263	0.9090
No log	57.0	342	0.7782	0.5277	0.7782	0.8822
No log	58.0	348	0.8694	0.5090	0.8694	0.9324
No log	59.0	354	0.8633	0.5002	0.8633	0.9292
No log	60.0	360	0.8805	0.4966	0.8805	0.9383
No log	61.0	366	0.7954	0.5146	0.7954	0.8919
No log	62.0	372	0.6620	0.5549	0.6620	0.8137
No log	63.0	378	0.9616	0.4712	0.9616	0.9806
No log	64.0	384	0.8070	0.5131	0.8070	0.8983
No log	65.0	390	0.7670	0.5010	0.7670	0.8758
No log	66.0	396	0.7910	0.5145	0.7910	0.8894
No log	67.0	402	0.7502	0.5119	0.7502	0.8662
No log	68.0	408	0.9059	0.4825	0.9059	0.9518
No log	69.0	414	0.7574	0.4987	0.7574	0.8703
No log	70.0	420	0.6676	0.5253	0.6676	0.8171
No log	71.0	426	0.9502	0.4772	0.9502	0.9748
No log	72.0	432	0.6744	0.5363	0.6744	0.8212
No log	73.0	438	0.7808	0.5107	0.7808	0.8836
No log	74.0	444	0.7827	0.5203	0.7827	0.8847
No log	75.0	450	0.7132	0.5079	0.7132	0.8445
No log	76.0	456	0.7905	0.5028	0.7905	0.8891
No log	77.0	462	0.8389	0.5044	0.8389	0.9159
No log	78.0	468	0.6327	0.5523	0.6327	0.7954
No log	79.0	474	0.7644	0.5113	0.7644	0.8743
No log	80.0	480	0.7303	0.5021	0.7303	0.8546
No log	81.0	486	0.7201	0.5023	0.7201	0.8486
No log	82.0	492	0.7375	0.5133	0.7375	0.8588

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 8, 2025
Updated: Mar 8, 2025