---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: arabert-hate-speech
  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. -->

# arabert-hate-speech

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5588
- Accuracy: 0.9451
- Precision: 0.9464
- Recall: 0.9451
- F1: 0.9450

## 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: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6993        | 1.0   | 100  | 1.5844          | 0.3845   | 0.3695    | 0.3845 | 0.3545 |
| 1.2593        | 2.0   | 200  | 1.0278          | 0.7662   | 0.7766    | 0.7662 | 0.7634 |
| 0.8076        | 3.0   | 300  | 0.6558          | 0.9056   | 0.9076    | 0.9056 | 0.9059 |
| 0.6413        | 4.0   | 400  | 0.5866          | 0.9282   | 0.9310    | 0.9282 | 0.9280 |
| 0.5734        | 5.0   | 500  | 0.5556          | 0.9451   | 0.9457    | 0.9451 | 0.9450 |
| 0.5203        | 6.0   | 600  | 0.5825          | 0.9338   | 0.9389    | 0.9338 | 0.9344 |
| 0.4843        | 7.0   | 700  | 0.5588          | 0.9451   | 0.9464    | 0.9451 | 0.9450 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1