QomSSLab/Anonymizer-xlm-roberta

QomSSLab/Anonymizer-4b

QomSSLab/Anonymizer-4b is a fine-tuned Gemma 3 4B model designed to anonymize Persian legal texts by masking or replacing all personally identifiable information (PII). It is trained on the QomSSLab/Anonymized_Cases dataset.

💡 Use Cases

• Data privacy for legal document processing.
• Preprocessing step for building publicly shareable Persian legal corpora.
• Protecting PII in judicial NLP pipelines.

🧠 Model Details

• Base Model: Gemma 3 4B
• Language: Persian (Farsi)
• Training Data: Synthetic and real anonymized Persian legal cases.
• Task: Text-to-text generation (anonymization)

📦 Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from transformers import pipeline


model = AutoModelForTokenClassification.from_pretrained("QomSSLab/Anonymizer-xlm-roberta",  device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("QomSSLab/Anonymizer-xlm-roberta")
ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text="پرونده‌ای درباره ازدواج بین هانیه و عبدالرحیم با اطلاعات هویتی متعدد"

entities = ner(text)

for ent in entities:
    print(f"Entity: {ent['word'],ent['start'], ent['end']}, Type: {ent['entity_group']}, Score: {ent['score']:.4f}")

📊 Evaluation

The model was evaluated qualitatively on a diverse collection of Persian legal documents. It effectively identifies and anonymizes a range of personally identifiable information (PII), including:

• Full names
• National IDs
• Addresses
• Dates of birth
• Case numbers
• Geographic locations

The model is particularly well-suited for preprocessing court cases for research, public data release, or downstream tasks like summarization and classification while preserving privacy.

Limitations

• May occasionally miss rare or out-of-distribution PII formats.
• Not guaranteed to anonymize very short or extremely noisy texts.
• Trained primarily on formal legal language; performance may degrade on informal Persian.

📁 Dataset

This model was fine-tuned on the QomSSLab/Anonymized_Cases dataset, which includes manually and synthetically anonymized court documents and legal filings in Persian. The dataset contains a mix of real and simulated entities, helping the model generalize across varied legal formats and writing styles.