---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cybersecurity-ner
  results: []
datasets:
- bnsapa/cybersecurity-ner
language:
- en
library_name: transformers
widget:
- text: "microsoft and google are working to build AI models"
- text: "Having obtained the necessary permissions from the user, Riltok contacts its C&C server."
- text: "Tweets in Twitter can be controversial"
---

<!-- 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 [cybersecurity-ner](https://huggingface.co/datasets/bnsapa/cybersecurity-ner) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2196
- Precision: 0.7942
- Recall: 0.7925
- F1: 0.7933
- Accuracy: 0.9508

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 167  | 0.2492          | 0.6870    | 0.7406 | 0.7128 | 0.9293   |
| No log        | 2.0   | 334  | 0.2026          | 0.7733    | 0.7346 | 0.7534 | 0.9420   |
| 0.2118        | 3.0   | 501  | 0.1895          | 0.7735    | 0.7934 | 0.7833 | 0.9493   |
| 0.2118        | 4.0   | 668  | 0.1834          | 0.7785    | 0.8189 | 0.7982 | 0.9511   |
| 0.2118        | 5.0   | 835  | 0.2060          | 0.8113    | 0.7965 | 0.8039 | 0.9522   |
| 0.0507        | 6.0   | 1002 | 0.2153          | 0.7692    | 0.8226 | 0.7950 | 0.9511   |
| 0.0507        | 7.0   | 1169 | 0.2141          | 0.7866    | 0.7962 | 0.7914 | 0.9507   |
| 0.0507        | 8.0   | 1336 | 0.2196          | 0.7942    | 0.7925 | 0.7933 | 0.9508   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0