bert-unformatted-network-data-test-6-types

Jios/bert-unformatted-network-data-test-6-types

bert-unformatted-network-data-test-6-types is a text classification model from Jios. Use it when you need a label for a piece of text. 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: Text Classification
Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Jun 3, 2024
Updated: Jun 10, 2024
SHA: 10878379

Try a prompt

Base models

Model card

From the Hugging Face model README

bert-unformatted-network-data-test-6-types

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

Loss: 0.1318
F1: 0.9624

EXAMPLE FULL NAMES:

label_0 = UDP-lag DDoS, label_1 = benign, label_2 = SYN flood, label_3 = NetBIOS, label_4 = MSSQL, label_5 = LDAP

Benign traffic from training data
Benign traffic from outside training data
malicious UDP-Lag DDoS attack from training data
malicious UDP-Lag DDoS attack from outside of training data
malicious SYN flood attack from training data
malicious SYN flood attack from outside of training data
malicious NetBIOS DDoS attack from training data
malicious NetBIOS DDoS attack from outside of training data
malicious MSSQL DDoS attack from training data
malicious MSSQL DDoS attack from outside of training data
malicious LDAP DDoS attack from training data
malicious LDAP DDoS attack from outside of training data

examples from CIC-DDoS2019 (formatted for model training) https://colab.research.google.com/drive/1PmLep9D3NfMhYsX0soTBhfVXFkawGgGx?authuser=0#scrollTo=ReaH6NCljdsn

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
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.1427	1.0	2250	0.1279	0.9622
0.1348	2.0	4500	0.1517	0.9624
0.1331	3.0	6750	0.1467	0.9613
0.1407	4.0	9000	0.1294	0.9623
0.1229	5.0	11250	0.1318	0.9624

Framework versions

Transformers 4.42.0.dev0
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: roberta
License: mit
Created: Jun 3, 2024
Updated: Jun 10, 2024