---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: cyber_bert
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# cyber_bert

This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4547
- Accuracy: 0.8159
- F1: 0.8066
- Precision: 0.8011
- Recall: 0.8300

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4657        | 1.0   | 144  | 0.4140          | 0.7960   | 0.7774 | 0.7732    | 0.7833 |
| 0.3537        | 2.0   | 288  | 0.4115          | 0.8023   | 0.7904 | 0.7843    | 0.8083 |
| 0.3624        | 3.0   | 432  | 0.4559          | 0.7986   | 0.7906 | 0.7885    | 0.8192 |
| 0.312         | 4.0   | 576  | 0.4269          | 0.8174   | 0.8066 | 0.8000    | 0.8257 |
| 0.2929        | 5.0   | 720  | 0.4547          | 0.8159   | 0.8066 | 0.8011    | 0.8300 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1