BERTModified-finetuned-wikitext-test

HideOnBush/BERTModified-finetuned-wikitext-test

BERTModified-finetuned-wikitext-test is a machine learning model from HideOnBush. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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

License: apache-2.0
Access: Public
Created: Nov 7, 2022
Updated: Mar 7, 2023
SHA: e67805c6

Model card

From the Hugging Face model README

BERTModified-finetuned-wikitext-test

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 18.8994
Precision: 0.25
Recall: 0.25
F1: 0.25
Accuracy: 0.25

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: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
19.9877	1.0	250	19.8070	0.0385	0.0385	0.0385	0.0385
15.4776	2.0	500	20.2930	0.0577	0.0577	0.0577	0.0577
13.1238	3.0	750	20.1112	0.0769	0.0769	0.0769	0.0769
11.1387	4.0	1000	19.9105	0.0897	0.0897	0.0897	0.0897
9.5317	5.0	1250	19.9108	0.1282	0.1282	0.1282	0.1282
8.037	6.0	1500	19.6093	0.1410	0.1410	0.1410	0.1410
6.7498	7.0	1750	19.1636	0.1474	0.1474	0.1474	0.1474
5.6472	8.0	2000	19.6709	0.1538	0.1538	0.1538	0.1538
4.6665	9.0	2250	19.2537	0.1667	0.1667	0.1667	0.1667
3.9107	10.0	2500	19.1982	0.1474	0.1474	0.1474	0.1474
3.1874	11.0	2750	18.9938	0.1731	0.1731	0.1731	0.1731
2.5846	12.0	3000	18.7462	0.2115	0.2115	0.2115	0.2115
2.1464	13.0	3250	19.0017	0.1667	0.1667	0.1667	0.1667
1.7521	14.0	3500	18.4513	0.1859	0.1859	0.1859	0.1859
1.4561	15.0	3750	18.7532	0.2051	0.2051	0.2051	0.2051
1.2254	16.0	4000	18.3970	0.2179	0.2179	0.2179	0.2179
1.0416	17.0	4250	18.9764	0.1859	0.1859	0.1859	0.1859
0.8923	18.0	4500	18.3271	0.2244	0.2244	0.2244	0.2244
0.7803	19.0	4750	18.5893	0.2436	0.2436	0.2436	0.2436
0.6839	20.0	5000	18.3505	0.2051	0.2051	0.2051	0.2051
0.6175	21.0	5250	18.6798	0.2051	0.2051	0.2051	0.2051
0.5491	22.0	5500	18.7426	0.2115	0.2115	0.2115	0.2115
0.4952	23.0	5750	18.3955	0.2179	0.2179	0.2179	0.2179
0.4441	24.0	6000	18.5502	0.2564	0.2564	0.2564	0.2564
0.4047	25.0	6250	18.9599	0.2244	0.2244	0.2244	0.2244
0.3768	26.0	6500	18.8141	0.2308	0.2308	0.2308	0.2308
0.3435	27.0	6750	18.9732	0.2436	0.2436	0.2436	0.2436
0.3164	28.0	7000	18.9216	0.2372	0.2372	0.2372	0.2372
0.2954	29.0	7250	18.6152	0.1987	0.1987	0.1987	0.1987
0.2736	30.0	7500	18.6001	0.25	0.25	0.25	0.25
0.2491	31.0	7750	19.1374	0.2436	0.2436	0.2436	0.2436
0.2359	32.0	8000	18.8624	0.25	0.25	0.25	0.25
0.2222	33.0	8250	18.3201	0.2308	0.2308	0.2308	0.2308
0.212	34.0	8500	18.7708	0.2179	0.2179	0.2179	0.2179
0.1864	35.0	8750	18.8994	0.2372	0.2372	0.2372	0.2372
0.1771	36.0	9000	18.3130	0.2308	0.2308	0.2308	0.2308
0.1703	37.0	9250	18.6183	0.2436	0.2436	0.2436	0.2436
0.1554	38.0	9500	18.8593	0.2372	0.2372	0.2372	0.2372
0.1469	39.0	9750	18.8936	0.2628	0.2628	0.2628	0.2628
0.1407	40.0	10000	18.9002	0.2372	0.2372	0.2372	0.2372
0.1328	41.0	10250	19.1827	0.2564	0.2564	0.2564	0.2564
0.1297	42.0	10500	18.5465	0.25	0.25	0.25	0.25
0.1226	43.0	10750	18.9125	0.2308	0.2308	0.2308	0.2308
0.1218	44.0	11000	19.0831	0.2308	0.2308	0.2308	0.2308
0.1136	45.0	11250	18.7969	0.2372	0.2372	0.2372	0.2372
0.1075	46.0	11500	18.7629	0.25	0.25	0.25	0.25
0.1044	47.0	11750	18.9700	0.2115	0.2115	0.2115	0.2115
0.1042	48.0	12000	18.7211	0.2628	0.2628	0.2628	0.2628
0.1008	49.0	12250	18.9104	0.2244	0.2244	0.2244	0.2244
0.1014	50.0	12500	18.7892	0.25	0.25	0.25	0.25

Framework versions

Transformers 4.24.0
Pytorch 1.13.0
Datasets 2.6.1
Tokenizers 0.13.2

License: apache-2.0
Created: Nov 7, 2022
Updated: Mar 7, 2023