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

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

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4669
- Accuracy: 0.8315
- F1: 0.8135
- Precision: 0.8121
- Recall: 0.8150

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5788        | 1.0   | 105  | 0.5623          | 0.6755   | 0.4813 | 0.6766    | 0.5352 |
| 0.478         | 2.0   | 210  | 0.4430          | 0.7746   | 0.7444 | 0.7501    | 0.7401 |
| 0.4087        | 3.0   | 315  | 0.3948          | 0.8096   | 0.7835 | 0.7911    | 0.7777 |
| 0.4004        | 4.0   | 420  | 0.3868          | 0.8080   | 0.7917 | 0.7864    | 0.7998 |
| 0.3216        | 5.0   | 525  | 0.4005          | 0.8106   | 0.7928 | 0.7888    | 0.7980 |
| 0.3144        | 6.0   | 630  | 0.3878          | 0.8299   | 0.8062 | 0.8153    | 0.7994 |
| 0.2598        | 7.0   | 735  | 0.4040          | 0.8258   | 0.8084 | 0.8053    | 0.8121 |
| 0.2234        | 8.0   | 840  | 0.4280          | 0.8284   | 0.8108 | 0.8083    | 0.8137 |
| 0.2088        | 9.0   | 945  | 0.4580          | 0.8320   | 0.8154 | 0.8121    | 0.8194 |
| 0.1775        | 10.0  | 1050 | 0.4669          | 0.8315   | 0.8135 | 0.8121    | 0.8150 |


### Framework versions

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