---
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: 0.2398
- 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