ASAP_FineTuningBERT_AugV10_k5_task1_organization_k5_k5_fold0

genki10/ASAP_FineTuningBERT_AugV10_k5_task1_organization_k5_k5_fold0

ASAP_FineTuningBERT_AugV10_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 13, 2025
Updated: Feb 13, 2025
SHA: 695c27d7

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV10_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: 0.6470
Qwk: 0.5342
Mse: 0.6470
Rmse: 0.8043

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	7.4385	0.0	7.4385	2.7274
No log	2.0	8	5.5992	0.0109	5.5992	2.3663
No log	3.0	12	3.8203	0.0039	3.8203	1.9546
No log	4.0	16	2.3105	0.1330	2.3105	1.5200
No log	5.0	20	1.5966	0.0316	1.5966	1.2636
No log	6.0	24	1.4215	0.0575	1.4215	1.1923
No log	7.0	28	1.1716	0.0638	1.1716	1.0824
No log	8.0	32	1.1238	0.1175	1.1238	1.0601
No log	9.0	36	0.9043	0.2546	0.9043	0.9510
No log	10.0	40	0.6863	0.4237	0.6863	0.8284
No log	11.0	44	0.6085	0.3950	0.6085	0.7800
No log	12.0	48	0.7215	0.3828	0.7215	0.8494
No log	13.0	52	0.6229	0.3377	0.6229	0.7892
No log	14.0	56	0.7862	0.3657	0.7862	0.8867
No log	15.0	60	0.6017	0.4607	0.6017	0.7757
No log	16.0	64	0.6221	0.4752	0.6221	0.7887
No log	17.0	68	0.6717	0.4513	0.6717	0.8196
No log	18.0	72	0.6696	0.4834	0.6696	0.8183
No log	19.0	76	0.6838	0.4923	0.6838	0.8269
No log	20.0	80	0.6652	0.4939	0.6652	0.8156
No log	21.0	84	0.6916	0.4975	0.6916	0.8316
No log	22.0	88	0.7089	0.5007	0.7089	0.8420
No log	23.0	92	0.7182	0.4659	0.7182	0.8475
No log	24.0	96	0.6829	0.4982	0.6829	0.8264
No log	25.0	100	0.6718	0.4954	0.6718	0.8196
No log	26.0	104	0.7048	0.4840	0.7048	0.8395
No log	27.0	108	0.6561	0.4831	0.6561	0.8100
No log	28.0	112	0.7119	0.4796	0.7119	0.8438
No log	29.0	116	0.6899	0.4794	0.6899	0.8306
No log	30.0	120	0.7140	0.4735	0.7140	0.8450
No log	31.0	124	0.7598	0.4949	0.7598	0.8717
No log	32.0	128	0.7013	0.4811	0.7013	0.8374
No log	33.0	132	0.6456	0.4788	0.6456	0.8035
No log	34.0	136	0.6595	0.5023	0.6595	0.8121
No log	35.0	140	0.6887	0.4748	0.6887	0.8299
No log	36.0	144	0.7052	0.4913	0.7052	0.8398
No log	37.0	148	0.7118	0.5121	0.7118	0.8437
No log	38.0	152	0.7263	0.5048	0.7263	0.8522
No log	39.0	156	0.8658	0.4481	0.8658	0.9305
No log	40.0	160	0.7138	0.4779	0.7138	0.8449
No log	41.0	164	0.8064	0.4671	0.8064	0.8980
No log	42.0	168	0.6795	0.4623	0.6795	0.8243
No log	43.0	172	0.7334	0.4726	0.7334	0.8564
No log	44.0	176	0.6779	0.4647	0.6779	0.8233
No log	45.0	180	0.6890	0.5223	0.6890	0.8301
No log	46.0	184	0.7157	0.4636	0.7157	0.8460
No log	47.0	188	0.6671	0.5331	0.6671	0.8168
No log	48.0	192	0.6792	0.4774	0.6792	0.8241
No log	49.0	196	0.7300	0.4770	0.7300	0.8544
No log	50.0	200	0.6736	0.5020	0.6736	0.8207
No log	51.0	204	0.7447	0.4787	0.7447	0.8630
No log	52.0	208	0.6743	0.5201	0.6743	0.8211
No log	53.0	212	0.6843	0.5126	0.6843	0.8272
No log	54.0	216	0.6995	0.5114	0.6995	0.8364
No log	55.0	220	0.7117	0.5036	0.7117	0.8436
No log	56.0	224	0.7407	0.4997	0.7407	0.8606
No log	57.0	228	0.6596	0.5056	0.6596	0.8122
No log	58.0	232	0.6968	0.5343	0.6968	0.8347
No log	59.0	236	0.6617	0.4848	0.6617	0.8134
No log	60.0	240	0.6434	0.5469	0.6434	0.8021
No log	61.0	244	0.6443	0.5250	0.6443	0.8027
No log	62.0	248	0.6638	0.5430	0.6638	0.8147
No log	63.0	252	0.6609	0.5322	0.6609	0.8129
No log	64.0	256	0.6839	0.5415	0.6839	0.8270
No log	65.0	260	0.6508	0.5242	0.6508	0.8067
No log	66.0	264	0.6547	0.5286	0.6547	0.8092
No log	67.0	268	0.6548	0.5169	0.6548	0.8092
No log	68.0	272	0.6973	0.5244	0.6973	0.8350
No log	69.0	276	0.7028	0.4969	0.7028	0.8383
No log	70.0	280	0.7052	0.5036	0.7052	0.8397
No log	71.0	284	0.7070	0.5100	0.7070	0.8408
No log	72.0	288	0.7007	0.5166	0.7007	0.8371
No log	73.0	292	0.7103	0.5041	0.7103	0.8428
No log	74.0	296	0.6673	0.5130	0.6673	0.8169
No log	75.0	300	0.6870	0.5194	0.6870	0.8289
No log	76.0	304	0.6651	0.5231	0.6651	0.8155
No log	77.0	308	0.6961	0.5278	0.6961	0.8344
No log	78.0	312	0.6498	0.5201	0.6498	0.8061
No log	79.0	316	0.6422	0.5393	0.6422	0.8014
No log	80.0	320	0.6470	0.5342	0.6470	0.8043

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 13, 2025
Updated: Feb 13, 2025