metadata
license: apache-2.0
tags: []
datasets:
- custom
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: >-
188.210.113.80 - - [26/Jan/2019:20:17:17 +0330] "GET
/image/4158/productModel/200x200 HTTP/1.1" 200 4022
example_title: Example Log
model-index:
- name: ner-test3
results:
- task:
type: token-classification
dataset:
name: custom_dataset
type: Signalit custom dataset
metrics:
- type: Global Strict F1
value: 0
- type: results Partial F1
value: 0
- type: TIM Strict F1
value: 0
- type: TIM Partial F1
value: 0
- type: KV Strict F1
value: 0
- type: KV Partial F1
value: 0
- type: IP Strict F1
value: 0
- type: IP Partial F1
value: 0
ner-test3
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1173
- Precision: 0.7826
- Recall: 0.8182
- F1: 0.8
- Accuracy: 0.7826
Model description
Fine-tuned Transformer based on the distilBERT architecture using Pytorch for detecting: Timestamps, KV and IPs.
Intended uses & limitations
Can be used on any system log containing timestamps, keyvalues and ips.
Training and evaluation data
Trained over 12000 logs: 3000 Apache, 1000 Csv, 1000 Dns, 3600 KV, 1000 Syslog and 3100 Miscellaneous logs. Evaluated on a small corpus of unseen logs labelled by hand.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 50
- eval_batch_size: 50
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.6299 | 1.0 | 1 | 1.2697 | 0.6522 | 0.6818 | 0.6667 | 0.6522 |
| 1.2767 | 2.0 | 2 | 1.1173 | 0.7826 | 0.8182 | 0.8 | 0.7826 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3