---
license: apache-2.0
base_model: jackaduma/SecBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cyner_secbert
  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. -->

# cyner_secbert

This model is a fine-tuned version of [jackaduma/SecBERT](https://huggingface.co/jackaduma/SecBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1212
- Precision: 0.7047
- Recall: 0.5517
- F1: 0.6189
- Accuracy: 0.9723

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.165         | 1.42  | 500  | 0.1212          | 0.7047    | 0.5517 | 0.6189 | 0.9723   |
| 0.04          | 2.84  | 1000 | 0.1647          | 0.6924    | 0.5147 | 0.5905 | 0.9705   |
| 0.0156        | 4.26  | 1500 | 0.1803          | 0.6769    | 0.5351 | 0.5977 | 0.9714   |
| 0.0087        | 5.68  | 2000 | 0.1866          | 0.6574    | 0.5415 | 0.5938 | 0.9713   |
| 0.0036        | 7.1   | 2500 | 0.2020          | 0.6740    | 0.5492 | 0.6052 | 0.9719   |
| 0.0024        | 8.52  | 3000 | 0.2036          | 0.6697    | 0.5568 | 0.6081 | 0.9720   |
| 0.0018        | 9.94  | 3500 | 0.2084          | 0.6682    | 0.5504 | 0.6036 | 0.9715   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1