fine-tuned-distilroberta-nosql-injection

ankush-003/fine-tuned-distilroberta-nosql-injection

fine-tuned-distilroberta-nosql-injection is a fill-mask model from ankush-003. Use it when you need the model to fill a missing word. 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: Fill-Mask
Library: transformers
License: apache-2.0
Model type: roberta
Access: Public
Created: Jul 18, 2023
Updated: Jul 18, 2023
SHA: 784294d6

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Base models

Model card

From the Hugging Face model README

fine-tuned-distilroberta-nosql-injection

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

Loss: 0.0000

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 75

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	79	0.3011
1.3847	2.0	158	0.1010
0.2018	3.0	237	0.0340
0.0999	4.0	316	0.0556
0.0999	5.0	395	0.0001
0.0509	6.0	474	0.0817
0.072	7.0	553	0.0001
0.0425	8.0	632	0.0395
0.0559	9.0	711	0.1041
0.0559	10.0	790	0.0014
0.0336	11.0	869	0.0284
0.0326	12.0	948	0.0251
0.0204	13.0	1027	0.0046
0.0136	14.0	1106	0.0293
0.0136	15.0	1185	0.0495
0.0318	16.0	1264	0.0011
0.0278	17.0	1343	0.0003
0.0201	18.0	1422	0.0004
0.0202	19.0	1501	0.0194
0.0202	20.0	1580	0.0202
0.0282	21.0	1659	0.0008
0.0277	22.0	1738	0.0401
0.0121	23.0	1817	0.0405
0.0121	24.0	1896	0.0230
0.0278	25.0	1975	0.0067
0.023	26.0	2054	0.0879
0.0247	27.0	2133	0.0168
0.0348	28.0	2212	0.0373
0.0348	29.0	2291	0.0466
0.0157	30.0	2370	0.0376
0.023	31.0	2449	0.0470
0.0101	32.0	2528	0.0522
0.0123	33.0	2607	0.0000
0.0123	34.0	2686	0.0245
0.0128	35.0	2765	0.0090
0.0089	36.0	2844	0.0000
0.0219	37.0	2923	0.0000
0.0082	38.0	3002	0.0001
0.0082	39.0	3081	0.0504
0.0123	40.0	3160	0.0000
0.0078	41.0	3239	0.0096
0.0217	42.0	3318	0.0019
0.0217	43.0	3397	0.0535
0.0117	44.0	3476	0.0253
0.0218	45.0	3555	0.0330
0.0171	46.0	3634	0.0000
0.0056	47.0	3713	0.0002
0.0056	48.0	3792	0.0025
0.0111	49.0	3871	0.0162
0.0051	50.0	3950	0.0010
0.0138	51.0	4029	0.0000
0.0041	52.0	4108	0.0000
0.0041	53.0	4187	0.0186
0.0103	54.0	4266	0.0001
0.0154	55.0	4345	0.0006
0.01	56.0	4424	0.0064
0.0076	57.0	4503	0.0044
0.0076	58.0	4582	0.0000
0.0155	59.0	4661	0.0000
0.0114	60.0	4740	0.0089
0.0176	61.0	4819	0.0000
0.0176	62.0	4898	0.0414
0.0037	63.0	4977	0.0000
0.0035	64.0	5056	0.0697
0.0086	65.0	5135	0.0206
0.0056	66.0	5214	0.0222
0.0056	67.0	5293	0.0365
0.0077	68.0	5372	0.0000
0.0109	69.0	5451	0.0004
0.0076	70.0	5530	0.0002
0.0022	71.0	5609	0.0001
0.0022	72.0	5688	0.0000
0.0078	73.0	5767	0.0011
0.0128	74.0	5846	0.0005
0.0047	75.0	5925	0.0000

Framework versions

Transformers 4.31.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.11.0

Task: Fill-Mask
Library: transformers
Type: roberta
License: apache-2.0
Created: Jul 18, 2023
Updated: Jul 18, 2023