metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cybersecurity-ner
results: []
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: 0.1996
- Precision: 0.7901
- Recall: 0.7708
- F1: 0.7803
- Accuracy: 0.9487
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 | 167 | 0.2305 | 0.6823 | 0.7752 | 0.7257 | 0.9334 |
| No log | 2.0 | 334 | 0.1971 | 0.7673 | 0.7601 | 0.7637 | 0.9456 |
| 0.2227 | 3.0 | 501 | 0.1912 | 0.7839 | 0.7563 | 0.7698 | 0.9477 |
| 0.2227 | 4.0 | 668 | 0.1902 | 0.7877 | 0.7934 | 0.7905 | 0.9511 |
| 0.2227 | 5.0 | 835 | 0.1996 | 0.7901 | 0.7708 | 0.7803 | 0.9487 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0