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cybersecurity_ner
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: 0.1383
- Precision: 0.6115
- Recall: 0.6154
- F1: 0.6134
- Accuracy: 0.9657
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 176 | 0.1601 | 0.4801 | 0.4776 | 0.4788 | 0.9553 |
| No log | 2.0 | 352 | 0.1371 | 0.5934 | 0.5737 | 0.5834 | 0.9612 |
| 0.1455 | 3.0 | 528 | 0.1320 | 0.5702 | 0.6207 | 0.5944 | 0.9620 |
| 0.1455 | 4.0 | 704 | 0.1343 | 0.6015 | 0.6175 | 0.6094 | 0.9646 |
| 0.1455 | 5.0 | 880 | 0.1383 | 0.6115 | 0.6154 | 0.6134 | 0.9657 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
