This model is a fine-tuned version of DistilBERT for Named Entity Recognition (NER) focused on PII detection and CV parsing in the Moroccan context.

It is designed to extract structured information from multilingual text including Darija (Moroccan Arabic), Arabic, and French, commonly found in resumes and semi-structured documents.

Developed by: Youssef Lamaachi
Model type: Token Classification (NER)
Language(s): Arabic, French, English (Darija included)
License: Apache 2.0
Finetuned from model: distilbert-base-uncased

Uses

Direct Use

This model can be directly used for:

Extracting PII from text
Parsing CVs and resumes
Structuring candidate information
Data anonymization pipelines

Downstream Use

HRTech platforms
Applicant Tracking Systems (ATS)
Document processing pipelines
AI-powered recruitment tools

Out-of-Scope Use

Surveillance or mass tracking systems
Legal or medical critical decision-making
Highly noisy OCR without preprocessing

Bias, Risks, and Limitations

Model is trained on Moroccan CV-style data → may not generalize globally
May struggle with:
- Informal Darija spelling variations
- OCR errors or noisy inputs
Potential bias depending on dataset annotation quality

Recommendations

Use preprocessing (clean text / OCR correction)
Fine-tune further for other domains if needed
Avoid sensitive or unethical use cases

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("lamaachi/distilbert-moroccan-pii-classifier")
model = AutoModelForTokenClassification.from_pretrained("your-username/distilbert-moroccan-pii-classifier")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)

text = "Smiya dyali Youssef, num 0612345678, email: test@gmail.com"
result = nlp(text)

print(result)

Lamaachi/distilbert-moroccan-pii-classifier

🇲🇦 DistilBERT Moroccan PII & CV Entity Extraction

Model Details

Model Description