---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
datasets:
- offenseval_2020
metrics:
- accuracy
model-index:
- name: ArabertHateSpeech
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: offenseval_2020
      type: offenseval_2020
      config: ar
      split: test
      args: ar
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9255610290093049
---

<!-- 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. -->

# ArabertHateSpeech

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the offenseval_2020 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3259
- Accuracy: 0.9256

## 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: 5e-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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1895        | 1.0   | 980  | 0.4345          | 0.9206   |
| 0.1472        | 2.0   | 1960 | 0.3537          | 0.9228   |
| 0.1365        | 3.0   | 2940 | 0.3259          | 0.9256   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3