ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold1

genki10/ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold1

ASAP_FineTuningBERT_AugV15_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 4, 2025
Updated: Mar 4, 2025
SHA: 070c35b1

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV15_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: 0.8602
Qwk: 0.5811
Mse: 0.8604
Rmse: 0.9276

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	8	5.7256	0.0074	5.7235	2.3924
No log	2.0	16	1.9217	0.1202	1.9202	1.3857
No log	3.0	24	1.5178	0.0	1.5161	1.2313
No log	4.0	32	1.7011	0.0	1.6995	1.3037
No log	5.0	40	1.6980	0.0	1.6965	1.3025
No log	6.0	48	1.5203	0.0211	1.5188	1.2324
No log	7.0	56	1.4443	0.0211	1.4429	1.2012
No log	8.0	64	1.4752	0.0379	1.4737	1.2140
No log	9.0	72	1.4637	0.1027	1.4622	1.2092
No log	10.0	80	1.3292	0.0211	1.3278	1.1523
No log	11.0	88	1.7092	0.0	1.7077	1.3068
No log	12.0	96	1.5437	0.0106	1.5422	1.2419
No log	13.0	104	1.2722	0.0482	1.2709	1.1273
No log	14.0	112	1.6158	0.0990	1.6144	1.2706
No log	15.0	120	1.2371	0.0888	1.2357	1.1116
No log	16.0	128	1.0590	0.0888	1.0577	1.0285
No log	17.0	136	1.0009	0.1010	0.9998	0.9999
No log	18.0	144	0.8204	0.2984	0.8194	0.9052
No log	19.0	152	1.0136	0.0787	1.0125	1.0062
No log	20.0	160	0.8568	0.3094	0.8559	0.9251
No log	21.0	168	0.7434	0.3483	0.7424	0.8616
No log	22.0	176	0.7585	0.4205	0.7576	0.8704
No log	23.0	184	0.6740	0.4077	0.6731	0.8205
No log	24.0	192	0.8968	0.1410	0.8959	0.9465
No log	25.0	200	0.6303	0.4652	0.6294	0.7934
No log	26.0	208	0.5931	0.4446	0.5924	0.7696
No log	27.0	216	0.9133	0.1974	0.9124	0.9552
No log	28.0	224	0.8172	0.2335	0.8164	0.9036
No log	29.0	232	0.5811	0.4973	0.5806	0.7620
No log	30.0	240	0.6558	0.4104	0.6551	0.8094
No log	31.0	248	1.0104	0.3820	1.0100	1.0050
No log	32.0	256	0.5700	0.5378	0.5695	0.7547
No log	33.0	264	0.8041	0.5020	0.8037	0.8965
No log	34.0	272	0.7999	0.5213	0.7995	0.8941
No log	35.0	280	0.8354	0.5098	0.8350	0.9138
No log	36.0	288	0.8432	0.5075	0.8429	0.9181
No log	37.0	296	0.5085	0.6246	0.5086	0.7131
No log	38.0	304	1.0335	0.4264	1.0332	1.0165
No log	39.0	312	0.9593	0.4558	0.9590	0.9793
No log	40.0	320	0.5826	0.6212	0.5825	0.7632
No log	41.0	328	0.9444	0.5174	0.9443	0.9717
No log	42.0	336	0.6554	0.5998	0.6551	0.8094
No log	43.0	344	0.9107	0.5256	0.9104	0.9541
No log	44.0	352	1.0554	0.5035	1.0552	1.0272
No log	45.0	360	0.5983	0.6537	0.5983	0.7735
No log	46.0	368	1.3488	0.3996	1.3486	1.1613
No log	47.0	376	0.8757	0.5343	0.8757	0.9358
No log	48.0	384	0.6267	0.6308	0.6268	0.7917
No log	49.0	392	2.2251	0.3082	2.2250	1.4916
No log	50.0	400	0.8351	0.5607	0.8350	0.9138
No log	51.0	408	0.6641	0.6018	0.6642	0.8150
No log	52.0	416	1.3337	0.4572	1.3337	1.1549
No log	53.0	424	0.6613	0.6194	0.6614	0.8133
No log	54.0	432	0.6065	0.6468	0.6068	0.7790
No log	55.0	440	0.9552	0.5433	0.9553	0.9774
No log	56.0	448	0.7358	0.5784	0.7359	0.8579
No log	57.0	456	0.7406	0.6123	0.7409	0.8608
No log	58.0	464	0.5256	0.6515	0.5258	0.7251
No log	59.0	472	1.1214	0.4748	1.1215	1.0590
No log	60.0	480	1.2031	0.4217	1.2030	1.0968
No log	61.0	488	0.7006	0.5875	0.7005	0.8370
No log	62.0	496	0.6754	0.5871	0.6754	0.8218
1.7815	63.0	504	0.8432	0.5740	0.8435	0.9184
1.7815	64.0	512	0.9858	0.5554	0.9861	0.9930
1.7815	65.0	520	0.8602	0.5811	0.8604	0.9276

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 4, 2025
Updated: Mar 4, 2025