ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_fold1

genki10/ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_fold1

ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_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 15, 2025
Updated: Mar 15, 2025
SHA: fcd33d92

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV21_k7_task1_organization_k7_k7_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: 1.1277
Qwk: 0.2419
Mse: 1.1267
Rmse: 1.0614

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	6	7.8576	0.0	7.8551	2.8027
No log	2.0	12	4.5652	0.0	4.5632	2.1362
No log	3.0	18	2.2524	0.1387	2.2507	1.5002
No log	4.0	24	1.6625	0.0054	1.6610	1.2888
No log	5.0	30	1.7506	0.0031	1.7489	1.3224
No log	6.0	36	1.2605	0.0112	1.2590	1.1221
No log	7.0	42	0.9801	0.1933	0.9785	0.9892
No log	8.0	48	1.0921	0.2400	1.0904	1.0442
No log	9.0	54	1.3362	0.2231	1.3344	1.1552
No log	10.0	60	1.3678	0.1789	1.3661	1.1688
No log	11.0	66	1.3289	0.2197	1.3274	1.1521
No log	12.0	72	1.1816	0.2692	1.1802	1.0864
No log	13.0	78	2.3414	0.0197	2.3392	1.5294
No log	14.0	84	1.6899	0.1670	1.6883	1.2994
No log	15.0	90	1.7738	0.1289	1.7721	1.3312
No log	16.0	96	2.0528	0.0877	2.0509	1.4321
No log	17.0	102	1.6880	0.1826	1.6866	1.2987
No log	18.0	108	2.1421	0.0805	2.1406	1.4631
No log	19.0	114	1.8824	0.0751	1.8808	1.3714
No log	20.0	120	1.5011	0.1766	1.4996	1.2246
No log	21.0	126	1.4781	0.1804	1.4766	1.2152
No log	22.0	132	1.2925	0.1735	1.2912	1.1363
No log	23.0	138	1.3834	0.1829	1.3821	1.1756
No log	24.0	144	1.6894	0.1164	1.6879	1.2992
No log	25.0	150	1.5751	0.1305	1.5736	1.2544
No log	26.0	156	1.7565	0.0898	1.7548	1.3247
No log	27.0	162	1.5216	0.1741	1.5201	1.2329
No log	28.0	168	1.3976	0.1915	1.3963	1.1817
No log	29.0	174	1.3936	0.1795	1.3924	1.1800
No log	30.0	180	0.9736	0.2573	0.9727	0.9862
No log	31.0	186	1.2302	0.2079	1.2291	1.1086
No log	32.0	192	1.2354	0.2076	1.2343	1.1110
No log	33.0	198	1.0787	0.2315	1.0776	1.0381
No log	34.0	204	1.4908	0.1344	1.4893	1.2204
No log	35.0	210	1.2113	0.1921	1.2101	1.1000
No log	36.0	216	1.0730	0.2297	1.0721	1.0354
No log	37.0	222	1.0789	0.2805	1.0779	1.0382
No log	38.0	228	1.1666	0.2383	1.1653	1.0795
No log	39.0	234	1.7503	0.1016	1.7487	1.3224
No log	40.0	240	0.9960	0.2605	0.9950	0.9975
No log	41.0	246	1.1083	0.2289	1.1072	1.0523
No log	42.0	252	1.1760	0.2408	1.1748	1.0839
No log	43.0	258	1.3916	0.1580	1.3902	1.1791
No log	44.0	264	1.0780	0.2126	1.0769	1.0377
No log	45.0	270	0.9871	0.2471	0.9862	0.9931
No log	46.0	276	1.3170	0.1937	1.3157	1.1470
No log	47.0	282	1.2749	0.2041	1.2736	1.1286
No log	48.0	288	1.4722	0.1546	1.4708	1.2128
No log	49.0	294	1.0345	0.2455	1.0335	1.0166
No log	50.0	300	1.1687	0.2258	1.1675	1.0805
No log	51.0	306	1.0566	0.2612	1.0555	1.0274
No log	52.0	312	1.2750	0.1872	1.2738	1.1286
No log	53.0	318	1.1509	0.2325	1.1498	1.0723
No log	54.0	324	1.3100	0.1845	1.3088	1.1440
No log	55.0	330	0.8748	0.2842	0.8740	0.9349
No log	56.0	336	1.0572	0.2760	1.0562	1.0277
No log	57.0	342	1.0112	0.2708	1.0102	1.0051
No log	58.0	348	1.2921	0.1872	1.2910	1.1362
No log	59.0	354	1.0353	0.2316	1.0344	1.0171
No log	60.0	360	1.1395	0.2230	1.1384	1.0670
No log	61.0	366	1.4607	0.1391	1.4593	1.2080
No log	62.0	372	1.0303	0.2261	1.0293	1.0146
No log	63.0	378	1.1960	0.2116	1.1949	1.0931
No log	64.0	384	1.1703	0.2435	1.1693	1.0813
No log	65.0	390	1.1980	0.2163	1.1970	1.0941
No log	66.0	396	1.0385	0.2655	1.0376	1.0186
No log	67.0	402	1.1614	0.2249	1.1604	1.0772
No log	68.0	408	1.1187	0.2298	1.1177	1.0572
No log	69.0	414	1.1093	0.2388	1.1082	1.0527
No log	70.0	420	1.0889	0.2316	1.0879	1.0430
No log	71.0	426	1.0780	0.2379	1.0771	1.0378
No log	72.0	432	1.1975	0.2016	1.1964	1.0938
No log	73.0	438	1.1752	0.2092	1.1741	1.0835
No log	74.0	444	1.0446	0.2512	1.0436	1.0216
No log	75.0	450	1.1294	0.2326	1.1283	1.0622
No log	76.0	456	1.1437	0.2395	1.1427	1.0690
No log	77.0	462	1.2147	0.1964	1.2136	1.1016
No log	78.0	468	1.0848	0.2711	1.0838	1.0411
No log	79.0	474	1.1561	0.2409	1.1551	1.0747
No log	80.0	480	1.1300	0.2402	1.1290	1.0625
No log	81.0	486	1.1959	0.2180	1.1948	1.0931
No log	82.0	492	1.0391	0.2541	1.0382	1.0189
No log	83.0	498	1.1581	0.2324	1.1570	1.0757
0.3563	84.0	504	1.0865	0.2656	1.0855	1.0419
0.3563	85.0	510	1.1277	0.2419	1.1267	1.0614

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 15, 2025
Updated: Mar 15, 2025