---
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