---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cybersecurity-ner
  results: []
---

<!-- 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. -->

# cybersecurity-ner

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: 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