|
--- |
|
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 |
