transformers safetensors embeddings darija arabic DarijaBERT camelbert fine-tuning fill-mask ar dataset:HANTIFARAH/combined_darija_dataset_cleaned base_model:SI2M-Lab/DarijaBERT base_model:finetune:SI2M-Lab/DarijaBERT endpoints_compatible region:us
---
base_model:
- SI2M-Lab/DarijaBERT
datasets:
- HANTIFARAH/combined_darija_dataset_cleaned
language:
- ar
library_name: transformers
metrics:
- accuracy
pipeline_tag: fill-mask
tags:
- embeddings
- darija
- arabic
- DarijaBERT
- camelbert
- fine-tuning
---

# Model Card for Fine-Tuned SI2M_DarijaBERT and CamelBERT

This model card outlines the fine-tuning of **SI2M_DarijaBERT** on a trunc of a large Moroccan Darija dataset scraped from youtube transcriptions and other websites that you can find here : https://huggingface.co/datasets/HANTIFARAH/combined_darija_dataset_cleaned . These transformer model were fine-tuned for the purpose embedding generation in Moroccan Darija, enhancing it performance on specific NLP tasks and tested it Embeddings on text Classification tasks.

## Model Details

### Model Description

The **SI2M_DarijaBERT** model have been fine-tuned on Moroccan Darija texts. the model is based on the BERT architecture and specialize in generating embeddings for text classification tasks in Moroccan Darija.

- **Developed by:** [BAGUENNA Mohammed-Amine]
- **Model type:** Transformer-based (BERT architecture)
- **Language(s) (NLP):** Moroccan Darija (Arabic dialect)
- **Finetuned from model:** SI2M_DarijaBERT



### Recommendations

Users should take care to ensure their data falls within the domain of Moroccan Darija text. Further fine-tuning with more specialized data is recommended for domain-specific applications (e.g., medical language).

## How to Get Started with the Model

You can use the models with the following code:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model = AutoModel.from_pretrained("bagamine/SI2M_DarijaBERTV1")
tokenizer = AutoTokenizer.from_pretrained("bagamine/SI2M_DarijaBERTV1")
```