---
license: apache-2.0
datasets:
- arbml/SANAD
language:
- ar
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: text-classification
library_name: transformers
tags:
- modernbert
- arabic
---

# ModernBERT Arabic Model Card

## Overview

> [!NOTE]
> This is an Experimental Arabic Model demonstrates how ModernBERT can be adapted to Arabic for tasks like topic classification.

This is an Experimental Arabic version of [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base),trained ONLY on Topic Classification Task using the base model of original modernbert with a custom Arabic trained tokenizer with the following details:
- **Dataset:** Arabic Wikipedia
- **Size:** 1.8 GB
- **Tokens:** 228,788,529 tokens

This model demonstrates how ModernBERT can be adapted to Arabic for tasks like topic classification.

## Model Eval Details
- **Epochs:** 3
- **Evaluation Metrics:**
  - **F1 Score:** 0.95
  - **Loss:** 0.1998
- **Training Step:** 47,862

## Dataset Used For Training:

- [SANAD DATASET](https://huggingface.co/datasets/arbml/SANAD) was used for training and testing which contains 7 different topics such as Politics, Finance, Medical, Culture, Sport , Tech and Religion.

## How to Use

The model can be used for text classification using the `transformers` library. Below is an example:

```python
from transformers import pipeline

# Load model from huggingface.co/models using our repository ID
classifier = pipeline(
    task="text-classification",
    model="Omartificial-Intelligence-Space/AraModernBert-Topic-Classifier",
)

sample = '''
PUT SOME TEXT HERE TO CLASSIFY ITS TOPIC
'''

classifier(sample)

# [{'label': 'health', 'score': 0.6779336333274841}]
```