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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
