ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold4

genki10/ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold4

ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold4 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: 514addc6

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV15_k10_task1_organization_k10_k10_fold4

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: 1.0235
Qwk: 0.5782
Mse: 1.0235
Rmse: 1.0117

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	4.8800	0.0128	4.8800	2.2091
No log	2.0	16	2.1418	0.0943	2.1418	1.4635
No log	3.0	24	1.8317	0.1017	1.8317	1.3534
No log	4.0	32	1.8512	0.1103	1.8512	1.3606
No log	5.0	40	1.5151	0.0636	1.5151	1.2309
No log	6.0	48	1.4049	0.1524	1.4049	1.1853
No log	7.0	56	1.5266	0.2023	1.5266	1.2356
No log	8.0	64	1.3452	0.1012	1.3452	1.1598
No log	9.0	72	1.1609	0.1191	1.1609	1.0775
No log	10.0	80	1.4837	0.1976	1.4837	1.2181
No log	11.0	88	1.1454	0.1379	1.1454	1.0702
No log	12.0	96	1.0565	0.1228	1.0565	1.0279
No log	13.0	104	0.9001	0.1689	0.9001	0.9487
No log	14.0	112	1.3876	0.1874	1.3876	1.1780
No log	15.0	120	0.6525	0.5217	0.6525	0.8078
No log	16.0	128	1.0033	0.3757	1.0033	1.0017
No log	17.0	136	0.6122	0.4004	0.6122	0.7824
No log	18.0	144	0.6422	0.5905	0.6422	0.8014
No log	19.0	152	0.5379	0.4943	0.5379	0.7334
No log	20.0	160	0.5494	0.4854	0.5494	0.7412
No log	21.0	168	0.4919	0.5694	0.4919	0.7014
No log	22.0	176	0.6835	0.4290	0.6835	0.8267
No log	23.0	184	0.6845	0.6141	0.6845	0.8274
No log	24.0	192	0.5546	0.6074	0.5546	0.7447
No log	25.0	200	0.5004	0.5932	0.5004	0.7074
No log	26.0	208	0.5119	0.6519	0.5119	0.7155
No log	27.0	216	0.7313	0.6063	0.7313	0.8552
No log	28.0	224	0.5345	0.6638	0.5345	0.7311
No log	29.0	232	0.6538	0.6260	0.6538	0.8086
No log	30.0	240	0.6928	0.5849	0.6928	0.8323
No log	31.0	248	0.5930	0.6531	0.5930	0.7701
No log	32.0	256	0.5610	0.6779	0.5610	0.7490
No log	33.0	264	0.7662	0.5970	0.7662	0.8753
No log	34.0	272	0.6701	0.6183	0.6701	0.8186
No log	35.0	280	0.9798	0.5258	0.9798	0.9898
No log	36.0	288	0.7307	0.6326	0.7307	0.8548
No log	37.0	296	0.6949	0.6527	0.6949	0.8336
No log	38.0	304	0.8462	0.6179	0.8462	0.9199
No log	39.0	312	0.5769	0.6841	0.5769	0.7595
No log	40.0	320	0.8232	0.6074	0.8232	0.9073
No log	41.0	328	0.5848	0.6858	0.5848	0.7647
No log	42.0	336	0.7205	0.6398	0.7205	0.8488
No log	43.0	344	1.3897	0.4449	1.3897	1.1789
No log	44.0	352	0.6446	0.6823	0.6446	0.8029
No log	45.0	360	0.9175	0.5764	0.9175	0.9579
No log	46.0	368	0.6346	0.6647	0.6346	0.7966
No log	47.0	376	0.6594	0.6435	0.6594	0.8120
No log	48.0	384	0.8138	0.6107	0.8138	0.9021
No log	49.0	392	0.5668	0.6778	0.5668	0.7529
No log	50.0	400	1.0010	0.5545	1.0010	1.0005
No log	51.0	408	0.6501	0.6796	0.6501	0.8063
No log	52.0	416	0.7839	0.6278	0.7839	0.8854
No log	53.0	424	0.8404	0.6097	0.8404	0.9167
No log	54.0	432	0.8876	0.6064	0.8876	0.9421
No log	55.0	440	0.5828	0.6873	0.5828	0.7634
No log	56.0	448	0.7292	0.6248	0.7292	0.8539
No log	57.0	456	0.5913	0.6729	0.5913	0.7690
No log	58.0	464	0.7647	0.6250	0.7647	0.8745
No log	59.0	472	0.6290	0.6781	0.6290	0.7931
No log	60.0	480	0.7180	0.6382	0.7180	0.8473
No log	61.0	488	1.0260	0.5590	1.0260	1.0129
No log	62.0	496	0.5651	0.6733	0.5651	0.7518
1.7239	63.0	504	0.9031	0.6013	0.9031	0.9503
1.7239	64.0	512	1.2536	0.4977	1.2536	1.1196
1.7239	65.0	520	0.5690	0.6832	0.5690	0.7543
1.7239	66.0	528	0.9533	0.5853	0.9533	0.9764
1.7239	67.0	536	1.1995	0.5165	1.1995	1.0952
1.7239	68.0	544	0.6162	0.6688	0.6162	0.7850
1.7239	69.0	552	0.6403	0.6718	0.6403	0.8002
1.7239	70.0	560	0.8784	0.6078	0.8784	0.9372
1.7239	71.0	568	0.6695	0.6334	0.6695	0.8182
1.7239	72.0	576	0.9131	0.5886	0.9131	0.9556
1.7239	73.0	584	0.7037	0.6423	0.7037	0.8388
1.7239	74.0	592	0.6452	0.6736	0.6452	0.8033
1.7239	75.0	600	1.0235	0.5782	1.0235	1.0117

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