Edit model card

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

NLLB Fine-tuned for Darija to Modern Standard Arabic Translation

This model is a fine-tuned version of facebook/nllb-200-distilled-600M for translating Moroccan Darija (ary) to Modern Standard Arabic (ar). The model was fine-tuned on a custom dataset using the Hugging Face transformers library. The model is developed by : Tachicart Ridouane, Bouzoubaa Karim

Model Details

  • Base Model: facebook/nllb-200-distilled-600M
  • Fine-tuning Library: Hugging Face transformers
  • Languages Supported: Moroccan Darija (ary), Modern Standard Arabic (ar)
  • Training Dataset: Custom dataset of Moroccan Darija and Modern Standard Arabic pairs in JSON format.

Performance

The model has been evaluated on a validation set to ensure translation quality. While it excels at capturing colloquial Moroccan Arabic, ongoing improvements and additional data can further enhance its performance.

Limitations

Dataset Size: The custom dataset consists of 21,000 samples, which may limit coverage of diverse expressions and rare terms. Colloquial Variations: Moroccan Arabic has many dialectal variations, which might not all be covered equally.

How to Use

You can use the model with the transformers library as follows:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("tachicart/nllb-ft-darija")
model = AutoModelForSeq2SeqLM.from_pretrained("tachicart/nllb-ft-darija")

# Example translation
inputs = tokenizer("ูƒูŠูุงุด ู†ู‚ุฏุฑ ู†ุฑุจุญ ุจุฒุงู ุฏูŠุงู„ ุงู„ูู„ูˆุณ ุจุงู„ุฒุฑุจุฉ  ", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month
29
Safetensors
Model size
615M params
Tensor type
F32
ยท
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.