finetuned-deberta-base-10pct

cynthiachan/finetuned-deberta-base-10pct

finetuned-deberta-base-10pct is a token classification model from cynthiachan. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: deberta
Access: Public
Created: Aug 31, 2022
Updated: Aug 31, 2022
SHA: a23444c0

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Datasets

cynthiachan/FeedRef_10pct

Model card

From the Hugging Face model README

training

This model is a fine-tuned version of microsoft/deberta-base on the cynthiachan/FeedRef_10pct dataset. It achieves the following results on the evaluation set:

Loss: 0.0810
Attackid Precision: 1.0
Attackid Recall: 1.0
Attackid F1: 1.0
Attackid Number: 6
Cve Precision: 1.0
Cve Recall: 1.0
Cve F1: 1.0
Cve Number: 11
Defenderthreat Precision: 0.0
Defenderthreat Recall: 0.0
Defenderthreat F1: 0.0
Defenderthreat Number: 2
Domain Precision: 1.0
Domain Recall: 0.9565
Domain F1: 0.9778
Domain Number: 23
Email Precision: 1.0
Email Recall: 1.0
Email F1: 1.0
Email Number: 3
Filepath Precision: 0.8841
Filepath Recall: 0.8788
Filepath F1: 0.8815
Filepath Number: 165
Hostname Precision: 1.0
Hostname Recall: 1.0
Hostname F1: 1.0
Hostname Number: 12
Ipv4 Precision: 1.0
Ipv4 Recall: 1.0
Ipv4 F1: 1.0
Ipv4 Number: 12
Md5 Precision: 0.8333
Md5 Recall: 0.9615
Md5 F1: 0.8929
Md5 Number: 52
Sha1 Precision: 0.6667
Sha1 Recall: 0.8571
Sha1 F1: 0.75
Sha1 Number: 7
Sha256 Precision: 0.9565
Sha256 Recall: 1.0
Sha256 F1: 0.9778
Sha256 Number: 44
Uri Precision: 0.0
Uri Recall: 0.0
Uri F1: 0.0
Uri Number: 1
Overall Precision: 0.9014
Overall Recall: 0.9201
Overall F1: 0.9107
Overall Accuracy: 0.9851

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Attackid Precision	Attackid Recall	Attackid F1	Attackid Number	Cve Precision	Cve Recall	Cve F1	Cve Number	Defenderthreat Number	Domain Precision	Domain Recall	Domain F1	Domain Number	Email Precision	Email Recall	Email F1	Email Number	Filepath Precision	Filepath Recall	Filepath F1	Filepath Number	Hostname Precision	Hostname Recall	Hostname F1	Hostname Number	Ipv4 Precision	Ipv4 Recall	Ipv4 F1	Ipv4 Number	Md5 Precision	Md5 Recall	Md5 F1	Md5 Number	Sha1 Precision	Sha1 Recall	Sha1 F1	Sha1 Number	Sha256 Precision	Sha256 Recall	Sha256 F1	Sha256 Number	Uri Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.3797	0.37	500	0.1998	0.0	0.0	0.0	6	0.0	0.0	0.0	11	2	0.0286	0.0435	0.0345	23	0.0	0.0	0.0	3	0.5108	0.7152	0.5960	165	0.1774	0.9167	0.2973	12	0.4	0.5	0.4444	12	0.3194	0.4423	0.3710	52	0.0	0.0	0.0	7	0.4588	0.8864	0.6047	44	1	0.3875	0.5858	0.4664	0.9593
0.1713	0.75	1000	0.1619	0.6	0.5	0.5455	6	0.5	0.6364	0.56	11	2	0.6957	0.6957	0.6957	23	0.0	0.0	0.0	3	0.6879	0.6545	0.6708	165	0.5217	1.0	0.6857	12	0.5714	1.0	0.7273	12	0.6667	0.8846	0.7603	52	0.0	0.0	0.0	7	0.7692	0.9091	0.8333	44	1	0.6685	0.7219	0.6942	0.9664
0.1152	1.12	1500	0.1096	0.8333	0.8333	0.8333	6	1.0	1.0	1.0	11	2	0.7826	0.7826	0.7826	23	1.0	1.0	1.0	3	0.7202	0.8424	0.7765	165	1.0	1.0	1.0	12	0.4444	1.0	0.6154	12	0.6944	0.9615	0.8065	52	0.0	0.0	0.0	7	0.8723	0.9318	0.9011	44	1	0.7312	0.8609	0.7908	0.9751
0.1089	1.5	2000	0.1243	1.0	1.0	1.0	6	0.9167	1.0	0.9565	11	2	0.9048	0.8261	0.8636	23	1.0	1.0	1.0	3	0.8011	0.8788	0.8382	165	0.6667	1.0	0.8	12	0.9091	0.8333	0.8696	12	0.7812	0.9615	0.8621	52	0.0	0.0	0.0	7	0.7857	1.0	0.88	44	1	0.8065	0.8876	0.8451	0.9750
0.0947	1.87	2500	0.0913	0.75	1.0	0.8571	6	1.0	1.0	1.0	11	2	0.8462	0.9565	0.8980	23	0.3333	0.6667	0.4444	3	0.8035	0.8424	0.8225	165	0.6	1.0	0.7500	12	1.0	1.0	1.0	12	0.7969	0.9808	0.8793	52	0.0	0.0	0.0	7	0.8302	1.0	0.9072	44	1	0.7952	0.8846	0.8375	0.9792
0.0629	2.25	3000	0.0940	1.0	0.8333	0.9091	6	1.0	1.0	1.0	11	2	0.9565	0.9565	0.9565	23	1.0	1.0	1.0	3	0.8671	0.8303	0.8483	165	1.0	1.0	1.0	12	1.0	1.0	1.0	12	0.9273	0.9808	0.9533	52	0.25	0.1429	0.1818	7	0.8776	0.9773	0.9247	44	1	0.8946	0.8787	0.8866	0.9825
0.0442	2.62	3500	0.1012	1.0	1.0	1.0	6	0.9167	1.0	0.9565	11	2	0.9091	0.8696	0.8889	23	0.75	1.0	0.8571	3	0.8182	0.8727	0.8446	165	1.0	1.0	1.0	12	1.0	1.0	1.0	12	0.92	0.8846	0.9020	52	0.5	1.0	0.6667	7	0.9565	1.0	0.9778	44	1	0.8616	0.9024	0.8815	0.9818
0.0401	3.0	4000	0.0810	1.0	1.0	1.0	6	1.0	1.0	1.0	11	2	1.0	0.9565	0.9778	23	1.0	1.0	1.0	3	0.8841	0.8788	0.8815	165	1.0	1.0	1.0	12	1.0	1.0	1.0	12	0.8333	0.9615	0.8929	52	0.6667	0.8571	0.75	7	0.9565	1.0	0.9778	44	1	0.9014	0.9201	0.9107	0.9851

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu102
Datasets 2.4.0
Tokenizers 0.12.1

Task: Token Classification
Library: transformers
Type: deberta
License: mit
Created: Aug 31, 2022
Updated: Aug 31, 2022