NER Darija MAR FSBM

🚀 NER Model for Moroccan Dialect (Darija)

This is a Named Entity Recognition (NER) model tailored for Moroccan Arabic (Darija). It can accurately recognize various entities in text, offering great value for information - extraction tasks from social media and news articles.

🚀 Quick Start

To utilize this model, you need to load it via the Hugging Face Transformers library. Here's a basic example:

from transformers import pipeline

# Load the model
nlp = pipeline("ner", model="mohannad-tazi/ner-darija-darner")

# Use the model
text = "محمد كان في الرباط."
result = nlp(text)
print(result)

✨ Features

• Named Entity Recognition: Capable of identifying entities like person names, locations, organizations, and miscellaneous entities in Moroccan Arabic (Darija) text.
• Based on BERT Architecture: Built upon the BERT architecture, which is well - known for its strong performance in natural language processing tasks.
• Fine - tuned on DarNERcorp: The model is fine - tuned on the DarNERcorp dataset, making it more suitable for Moroccan Arabic NER tasks.

📦 Installation

No specific installation steps are provided in the original document.

📚 Documentation

Model Description

This model is a Named Entity Recognition (NER) model fine - tuned on the DarNERcorp dataset. It aims to recognize entities such as person names, locations, organizations, and miscellaneous entities in Moroccan Arabic (Darija) text. Being based on the BERT architecture, it is highly useful for tasks like information extraction from social media or news articles.

Model Architecture

Intended Use

This model is designed for Named Entity Recognition tasks in Moroccan Arabic. It can identify and classify entities such as:

• PER: Person names (e.g., "محمد", "فاطمة")
• LOC: Locations (e.g., "الرباط", "طنجة")
• ORG: Organizations (e.g., "البنك المغربي", "جامعة الحسن الثاني")
• MISC: Miscellaneous entities

Use Cases

• Social media analysis: Extracting entities from Moroccan Arabic posts and tweets.
• News summarization: Identifying important entities in news articles.
• Information extraction: Extracting named entities from informal or formal texts.

Evaluation Results

The model achieves the following results on the evaluation dataset:

Dataset

The model is trained on the DarNERcorp dataset, a corpus specifically designed for Named Entity Recognition in the Moroccan Arabic dialect. The dataset includes sentences labeled with named entity tags such as PER, LOC, ORG, and MISC.

Preprocessing Steps

• Tokenization using the BERT tokenizer.
• Alignment of labels with tokenized inputs (considering word - piece tokens).
• Padding and truncating sentences to a fixed length for uniformity.

Limitations

• The model is trained on a specific corpus and may not generalize well to all Moroccan Arabic texts.
• Performance may vary depending on text quality and tagging consistency in the dataset.