Version3ASAP_FineTuningBERT_AugV14_k5_task1_organization_k5_k5_fold0

genki10/Version3ASAP_FineTuningBERT_AugV14_k5_task1_organization_k5_k5_fold0

Version3ASAP_FineTuningBERT_AugV14_k5_task1_organization_k5_k5_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: Feb 20, 2025
Updated: Feb 20, 2025
SHA: 982c8972

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Base models

Model card

From the Hugging Face model README

Version3ASAP_FineTuningBERT_AugV14_k5_task1_organization_k5_k5_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: 0.6023
Qwk: 0.5410
Mse: 0.6023
Rmse: 0.7761

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	4	7.6448	0.0	7.6448	2.7649
No log	2.0	8	5.8451	0.0130	5.8451	2.4177
No log	3.0	12	4.0999	0.0039	4.0999	2.0248
No log	4.0	16	2.6114	-0.0008	2.6114	1.6160
No log	5.0	20	1.5224	0.0316	1.5224	1.2339
No log	6.0	24	1.2372	0.0316	1.2372	1.1123
No log	7.0	28	1.6796	0.1347	1.6796	1.2960
No log	8.0	32	1.0983	0.1063	1.0983	1.0480
No log	9.0	36	1.0981	0.1841	1.0981	1.0479
No log	10.0	40	1.4600	0.1748	1.4600	1.2083
No log	11.0	44	0.6814	0.4177	0.6814	0.8254
No log	12.0	48	0.7508	0.4092	0.7508	0.8665
No log	13.0	52	0.6372	0.4313	0.6372	0.7982
No log	14.0	56	0.6139	0.3837	0.6139	0.7835
No log	15.0	60	0.6505	0.4452	0.6505	0.8065
No log	16.0	64	0.6849	0.4940	0.6849	0.8276
No log	17.0	68	0.6760	0.4503	0.6760	0.8222
No log	18.0	72	0.5726	0.4836	0.5726	0.7567
No log	19.0	76	0.6516	0.4900	0.6516	0.8072
No log	20.0	80	0.6570	0.5320	0.6570	0.8105
No log	21.0	84	0.7315	0.4761	0.7315	0.8553
No log	22.0	88	0.6005	0.5059	0.6005	0.7749
No log	23.0	92	0.6547	0.4651	0.6547	0.8091
No log	24.0	96	0.6186	0.4989	0.6186	0.7865
No log	25.0	100	0.6368	0.5194	0.6368	0.7980
No log	26.0	104	0.6760	0.4877	0.6760	0.8222
No log	27.0	108	0.6251	0.5166	0.6251	0.7906
No log	28.0	112	0.6137	0.5095	0.6137	0.7834
No log	29.0	116	0.6286	0.5085	0.6286	0.7928
No log	30.0	120	0.6226	0.5020	0.6226	0.7891
No log	31.0	124	0.6852	0.4892	0.6852	0.8278
No log	32.0	128	0.6179	0.5158	0.6179	0.7861
No log	33.0	132	0.6582	0.4930	0.6582	0.8113
No log	34.0	136	0.6713	0.4980	0.6713	0.8193
No log	35.0	140	0.5930	0.5102	0.5930	0.7700
No log	36.0	144	0.7096	0.5338	0.7096	0.8424
No log	37.0	148	0.6715	0.5255	0.6715	0.8195
No log	38.0	152	0.6219	0.5271	0.6219	0.7886
No log	39.0	156	0.6119	0.5260	0.6119	0.7822
No log	40.0	160	0.7126	0.5234	0.7126	0.8442
No log	41.0	164	0.6235	0.5179	0.6235	0.7896
No log	42.0	168	0.6214	0.5054	0.6214	0.7883
No log	43.0	172	0.6545	0.5213	0.6545	0.8090
No log	44.0	176	0.6074	0.5085	0.6074	0.7794
No log	45.0	180	0.6108	0.5143	0.6108	0.7815
No log	46.0	184	0.6018	0.5105	0.6018	0.7758
No log	47.0	188	0.5894	0.5184	0.5894	0.7677
No log	48.0	192	0.5903	0.5291	0.5903	0.7683
No log	49.0	196	0.6737	0.5028	0.6737	0.8208
No log	50.0	200	0.6195	0.5146	0.6195	0.7871
No log	51.0	204	0.6218	0.5130	0.6218	0.7886
No log	52.0	208	0.6924	0.5063	0.6924	0.8321
No log	53.0	212	0.6047	0.5221	0.6047	0.7776
No log	54.0	216	0.5912	0.5119	0.5912	0.7689
No log	55.0	220	0.6512	0.5470	0.6512	0.8070
No log	56.0	224	0.6634	0.5396	0.6634	0.8145
No log	57.0	228	0.5990	0.5268	0.5990	0.7739
No log	58.0	232	0.6106	0.5280	0.6106	0.7814
No log	59.0	236	0.6068	0.5456	0.6068	0.7790
No log	60.0	240	0.5947	0.5290	0.5947	0.7712
No log	61.0	244	0.6310	0.5461	0.6310	0.7943
No log	62.0	248	0.6073	0.5398	0.6073	0.7793
No log	63.0	252	0.5870	0.5151	0.5870	0.7662
No log	64.0	256	0.6351	0.5265	0.6351	0.7969
No log	65.0	260	0.6118	0.5266	0.6118	0.7822
No log	66.0	264	0.5821	0.5034	0.5821	0.7629
No log	67.0	268	0.5836	0.5396	0.5836	0.7639
No log	68.0	272	0.6164	0.5428	0.6164	0.7851
No log	69.0	276	0.5926	0.5171	0.5926	0.7698
No log	70.0	280	0.6155	0.5227	0.6155	0.7846
No log	71.0	284	0.5976	0.5294	0.5976	0.7731
No log	72.0	288	0.6065	0.5288	0.6065	0.7788
No log	73.0	292	0.6365	0.5295	0.6365	0.7978
No log	74.0	296	0.6116	0.4980	0.6116	0.7820
No log	75.0	300	0.6023	0.5410	0.6023	0.7761

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Feb 20, 2025
Updated: Feb 20, 2025