Open-source Log Analysis Model DistilBert - Free to Process and Analyze Log Data

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Log Analysis Model DistilBert

Developed by teoogherghi

A log analysis model based on DistilBert for processing and analyzing log data

Text Classification

Transformers

Open Source License:Apache-2.0 #Log Analysis #Lightweight BERT #Text Classification

Downloads 132

Release Time : 5/8/2024

Model Overview

This model is a fine-tuned log analysis model based on the DistilBert architecture, primarily used for log data classification and analysis tasks.

Model Features

Lightweight Architecture

Based on the DistilBert architecture, it reduces model size and computational requirements while maintaining performance.

Efficient Training

Trained for 5 epochs, achieving a low loss value on the evaluation set.

Model Capabilities

Log Classification

Log Analysis

Text Classification

Use Cases

System Monitoring

Anomaly Log Detection

Identify abnormal patterns in system logs

Effectively classifies normal and abnormal logs

IT Operations

Automatic Log Classification

Automatically categorize large volumes of logs into different categories

Improves operational efficiency

🚀 Log-Analysis-Model-DistilBert

This model is a fine - tuned version of distilbert-base-uncased on an unknown dataset. It offers valuable insights for log analysis tasks, achieving a loss of 0.0453 on the evaluation set.

🚀 Quick Start

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: 0.0453

📚 Documentation

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss
0.0736	0.0982	500	0.0615
0.0561	0.1964	1000	0.0625
0.0547	0.2946	1500	0.0549
0.0655	0.3929	2000	0.0593
0.0605	0.4911	2500	0.0541
0.0739	0.5893	3000	0.0547
0.0474	0.6875	3500	0.0629
0.051	0.7857	4000	0.0563
0.0758	0.8839	4500	0.0607
0.0676	0.9821	5000	0.0509
0.0645	1.0803	5500	0.0564
0.0531	1.1786	6000	0.0561
0.0409	1.2768	6500	0.0596
0.0297	1.3750	7000	0.0703
0.058	1.4732	7500	0.0613
0.0486	1.5714	8000	0.0532
0.0459	1.6696	8500	0.0599
0.0846	1.7678	9000	0.0583
0.0586	1.8660	9500	0.0560
0.099	1.9643	10000	0.0503
0.0576	2.0625	10500	0.0573
0.049	2.1607	11000	0.0505
0.0489	2.2589	11500	0.0490
0.0611	2.3571	12000	0.0494
0.056	2.4553	12500	0.0476
0.03	2.5535	13000	0.0540
0.0536	2.6517	13500	0.0478
0.0752	2.7500	14000	0.0521
0.0476	2.8482	14500	0.0590
0.0402	2.9464	15000	0.0601
0.041	3.0446	15500	0.0520
0.053	3.1428	16000	0.0480
0.0315	3.2410	16500	0.0494
0.0326	3.3392	17000	0.0511
0.044	3.4374	17500	0.0520
0.0681	3.5357	18000	0.0467
0.0406	3.6339	18500	0.0479
0.0505	3.7321	19000	0.0480
0.0539	3.8303	19500	0.0453
0.025	3.9285	20000	0.0504
0.0598	4.0267	20500	0.0477
0.039	4.1249	21000	0.0498
0.0474	4.2231	21500	0.0494
0.037	4.3214	22000	0.0489
0.0303	4.4196	22500	0.0503
0.0545	4.5178	23000	0.0485
0.0466	4.6160	23500	0.0484
0.0461	4.7142	24000	0.0478
0.0478	4.8124	24500	0.0478
0.0473	4.9106	25000	0.0477

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cpu
Datasets 2.19.1
Tokenizers 0.19.1

📄 License

This model is released under the Apache - 2.0 license.

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