| --- |
| 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 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # ner-test3 |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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 |
|
|
