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: 0.2398
- Precision: 0.7853
- Recall: 0.7984
- F1: 0.7918
- Accuracy: 0.9504
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 | 0.2454 | 0.8038 | 0.7664 | 0.7846 | 0.9489 |
| No log | 2.0 | 334 | 0.2225 | 0.7697 | 0.8230 | 0.7954 | 0.9512 |
| 0.0449 | 3.0 | 501 | 0.2229 | 0.7883 | 0.8022 | 0.7952 | 0.9521 |
| 0.0449 | 4.0 | 668 | 0.2311 | 0.7819 | 0.8116 | 0.7965 | 0.9517 |
| 0.0449 | 5.0 | 835 | 0.2398 | 0.7853 | 0.7984 | 0.7918 | 0.9504 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0