ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3 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: 1aebf79a

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Base models

Model card

From the Hugging Face model README

ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

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: 0.6679
Qwk: 0.5834
Mse: 0.6689
Rmse: 0.8178

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	5	9.5642	0.0	9.5621	3.0923
No log	2.0	10	5.9851	0.0463	5.9838	2.4462
No log	3.0	15	3.2332	0.0	3.2322	1.7978
No log	4.0	20	1.6370	0.0266	1.6365	1.2792
No log	5.0	25	1.2232	0.0202	1.2229	1.1058
No log	6.0	30	1.2467	0.0237	1.2469	1.1166
No log	7.0	35	1.1589	0.1035	1.1598	1.0770
No log	8.0	40	0.9494	0.2416	0.9500	0.9747
No log	9.0	45	0.7569	0.3031	0.7581	0.8707
No log	10.0	50	0.7071	0.3614	0.7082	0.8415
No log	11.0	55	0.9105	0.3152	0.9123	0.9551
No log	12.0	60	0.7480	0.4175	0.7497	0.8658
No log	13.0	65	0.6533	0.5705	0.6544	0.8090
No log	14.0	70	0.7001	0.4685	0.7013	0.8374
No log	15.0	75	0.6464	0.5712	0.6474	0.8046
No log	16.0	80	0.6815	0.5721	0.6825	0.8262
No log	17.0	85	0.7944	0.4627	0.7958	0.8921
No log	18.0	90	0.7714	0.4993	0.7727	0.8790
No log	19.0	95	0.7166	0.5411	0.7175	0.8471
No log	20.0	100	0.7909	0.5398	0.7921	0.8900
No log	21.0	105	0.6796	0.5655	0.6806	0.8250
No log	22.0	110	0.7905	0.4987	0.7917	0.8898
No log	23.0	115	0.7650	0.5299	0.7664	0.8755
No log	24.0	120	0.6905	0.5022	0.6914	0.8315
No log	25.0	125	0.7081	0.5217	0.7093	0.8422
No log	26.0	130	0.7710	0.5168	0.7725	0.8789
No log	27.0	135	0.7750	0.5090	0.7763	0.8811
No log	28.0	140	0.7481	0.5642	0.7495	0.8657
No log	29.0	145	0.7209	0.5537	0.7221	0.8498
No log	30.0	150	0.6817	0.5491	0.6827	0.8262
No log	31.0	155	0.7170	0.5997	0.7177	0.8472
No log	32.0	160	0.6529	0.5809	0.6539	0.8086
No log	33.0	165	0.6436	0.5858	0.6445	0.8028
No log	34.0	170	0.6628	0.5715	0.6637	0.8147
No log	35.0	175	0.7115	0.5775	0.7126	0.8442
No log	36.0	180	0.7181	0.5475	0.7193	0.8481
No log	37.0	185	0.6683	0.6017	0.6693	0.8181
No log	38.0	190	0.6822	0.5568	0.6834	0.8267
No log	39.0	195	0.6527	0.5747	0.6538	0.8085
No log	40.0	200	0.6900	0.5910	0.6907	0.8311
No log	41.0	205	0.6897	0.5729	0.6909	0.8312
No log	42.0	210	0.6821	0.5789	0.6831	0.8265
No log	43.0	215	0.6956	0.5589	0.6968	0.8348
No log	44.0	220	0.6970	0.5661	0.6981	0.8355
No log	45.0	225	0.6891	0.5457	0.6903	0.8308
No log	46.0	230	0.6835	0.5679	0.6847	0.8275
No log	47.0	235	0.6567	0.5949	0.6576	0.8109
No log	48.0	240	0.6571	0.5896	0.6580	0.8112
No log	49.0	245	0.6805	0.5790	0.6816	0.8256
No log	50.0	250	0.6698	0.5614	0.6710	0.8191
No log	51.0	255	0.6355	0.5828	0.6365	0.7978
No log	52.0	260	0.6363	0.5828	0.6372	0.7982
No log	53.0	265	0.6657	0.5943	0.6667	0.8165
No log	54.0	270	0.6806	0.5910	0.6816	0.8256
No log	55.0	275	0.6731	0.5889	0.6739	0.8209
No log	56.0	280	0.6909	0.5533	0.6920	0.8319
No log	57.0	285	0.7455	0.6020	0.7464	0.8640
No log	58.0	290	0.6503	0.5870	0.6513	0.8070
No log	59.0	295	0.7066	0.5516	0.7078	0.8413
No log	60.0	300	0.6777	0.5837	0.6786	0.8238
No log	61.0	305	0.6932	0.5611	0.6944	0.8333
No log	62.0	310	0.6672	0.6019	0.6681	0.8174
No log	63.0	315	0.6861	0.5628	0.6872	0.8290
No log	64.0	320	0.6990	0.5951	0.7000	0.8367
No log	65.0	325	0.6691	0.5878	0.6700	0.8186
No log	66.0	330	0.6643	0.5732	0.6654	0.8157
No log	67.0	335	0.6713	0.5901	0.6722	0.8199
No log	68.0	340	0.6705	0.5814	0.6716	0.8195
No log	69.0	345	0.6439	0.5847	0.6449	0.8031
No log	70.0	350	0.6634	0.5791	0.6645	0.8151
No log	71.0	355	0.6788	0.5744	0.6798	0.8245
No log	72.0	360	0.6584	0.5866	0.6594	0.8120
No log	73.0	365	0.6783	0.5816	0.6794	0.8243
No log	74.0	370	0.6450	0.5900	0.6459	0.8037
No log	75.0	375	0.6669	0.5738	0.6680	0.8173
No log	76.0	380	0.6620	0.5820	0.6630	0.8143
No log	77.0	385	0.6679	0.5834	0.6689	0.8178

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