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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
datasets:
- offenseval_2020
metrics:
- accuracy
- f1
- precision
- recall
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: 0.9425287356321839
- name: F1
type: f1
value: 0.8543689320388349
- name: Precision
type: precision
value: 0.875
- name: Recall
type: recall
value: 0.8346883468834688
---
<!-- 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: 0.2500
- Accuracy: 0.9425
- F1: 0.8544
- Precision: 0.875
- Recall: 0.8347
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 490 | 0.1377 | 0.9321 | 0.8263 | 0.8551 | 0.7995 |
| 0.1418 | 2.0 | 980 | 0.0967 | 0.9321 | 0.8121 | 0.9210 | 0.7263 |
| 0.0898 | 3.0 | 1470 | 0.1082 | 0.9442 | 0.8517 | 0.9185 | 0.7940 |
| 0.0595 | 4.0 | 1960 | 0.1530 | 0.9338 | 0.8358 | 0.8370 | 0.8347 |
| 0.0405 | 5.0 | 2450 | 0.1559 | 0.9442 | 0.8579 | 0.8825 | 0.8347 |
| 0.0194 | 6.0 | 2940 | 0.2175 | 0.9398 | 0.8541 | 0.8364 | 0.8726 |
| 0.0153 | 7.0 | 3430 | 0.1994 | 0.9392 | 0.8452 | 0.8707 | 0.8211 |
| 0.0102 | 8.0 | 3920 | 0.2154 | 0.9403 | 0.8541 | 0.8439 | 0.8645 |
| 0.0093 | 9.0 | 4410 | 0.2296 | 0.9409 | 0.8470 | 0.8872 | 0.8103 |
| 0.0047 | 10.0 | 4900 | 0.2406 | 0.9420 | 0.8524 | 0.8768 | 0.8293 |
| 0.0038 | 11.0 | 5390 | 0.2530 | 0.9436 | 0.8591 | 0.8674 | 0.8509 |
| 0.0051 | 12.0 | 5880 | 0.2500 | 0.9425 | 0.8544 | 0.875 | 0.8347 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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