---
language:
- ar  # Arabic
metrics:
- perplexity  # Metric used to evaluate the model
base_model:
- google-bert/bert-base-uncased  # The original base model used
pipeline_tag: mask-generation  # The task this model performs
datasets:
- big_arabic_train  # Dataset used for training
- big_arabic_val    # Dataset used for validation
library_name: transformers  # Framework used (Hugging Face Transformers)
tags:
- egyptian-arabic  # Add relevant tags to describe your model
- fine-tuned
- arabert
license: apache-2.0  # Add a license (choose one appropriate for your work)
---

# EgBERT: Fine-Tuned AraBERT for Egyptian Arabic

## Model Description

EgBERT is a fine-tuned version of the pre-trained AraBERT model designed for Egyptian Arabic. This model was developed to enhance performance on tasks requiring understanding and generation of Egyptian dialect text, with a focus on Masked Language Modeling (MLM). The fine-tuning process involved a custom dataset containing colloquial Egyptian Arabic, making the model particularly suited for casual and conversational text.

Key Features:
- Based on **[aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert)**.
- Fine-tuned specifically for **Egyptian Arabic**.
- Optimized for **Masked Language Modeling (MLM)** tasks.

## Training Details

- **Dataset**: 
  - A custom dataset of Egyptian Arabic collected from conversational text sources.
  - Preprocessed to include common colloquial phrases and reduce noise in data.
- **Training Setup**:
  - Pre-trained model: `aubmindlab/bert-base-arabert`
  - Fine-tuning performed for 3 epochs with a batch size of 16.
  - Learning rate: 2e-5.
  - MLM Probability: 15%.

## Evaluation Results

### Model Perplexity
- **Baseline Model**: 36.2377
- **Fine-Tuned Model**: 26.5359

The fine-tuned model outperforms the baseline AraBERT model in terms of perplexity, indicating better performance on MLM tasks in Egyptian Arabic.

## How to Use

Here’s an example of how to use EgBERT in your project:

```python
from transformers import AutoTokenizer, AutoModelForMaskedLM

# Load the fine-tuned model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("noortamerr/EgBERT")
model = AutoModelForMaskedLM.from_pretrained("noortamerr/EgBERT")

# Input text with a masked token
text = "الكورة في مصر [MASK] حاجة كل الناس بتتابعها."

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt")
mask_token_index = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]

with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits

# Decode the top 5 predictions for the [MASK] token
mask_token_logits = predictions[0, mask_token_index, :]
top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
predicted_words = [tokenizer.decode([token]) for token in top_5_tokens]

print(f"Predicted words: {predicted_words}")
,,,


@misc{EgBERT,
  author = {Noor Tamer, Roba Mahmoud, Orchid Hazem},
  title = {EgBERT: Fine-Tuned AraBERT for Egyptian Arabic},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/noortamerr/EgBERT}
}