MohammadJRanjbar/parsbert-persian-punctuation

PersianPunc: ParsBERT for Persian Punctuation Restoration

This model is a fine-tuned version of ParsBERT for Persian punctuation restoration.

Model Description

• Language: Persian (Farsi)
• Task: Token Classification (Punctuation Restoration)
• Base Model: ParsBERT
• Dataset: PersianPunc (17M samples)
• Training Subset: 1M samples

Performance

Evaluated on 1,000 test sentences:

• Macro-averaged F1: 91.33%
• Micro-averaged F1: 97.28%
• Full Sentence Match Rate: 61.80%

Per-class Performance:

Punctuation	Precision	Recall	F1-Score
Persian Comma (،)	84.08%	76.35%	80.03%
Period (.)	98.55%	98.86%	98.71%
Question (؟)	87.50%	90.32%	88.89%
Colon (:)	91.37%	89.55%	90.45%

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

# Load model and tokenizer
model_name = "MohammadJRanjbar/parsbert-persian-punctuation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Prepare input (text without punctuation)
text = "سلام چطوری امروز هوا خیلی خوبه"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)

# Label mapping
id2label = {
    0: "EMPTY",
    1: "COMMA",
    2: "QUESTION", 
    3: "PERIOD",
    4: "COLON"
}

# Map predictions to punctuation
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
labels = [id2label[p.item()] for p in predictions[0]]

# Reconstruct text with punctuation
punct_map = {
    "COMMA": "،",
    "QUESTION": "؟",
    "PERIOD": ".",
    "COLON": ":"
}

result = []
for token, label in zip(tokens, labels):
    if token in ["[CLS]", "[SEP]", "[PAD]"]:
        continue
    result.append(token)
    if label in punct_map:
        result.append(punct_map[label])

punctuated_text = " ".join(result).replace(" ##", "")
print(punctuated_text)

Training Details

• Training Data: 989,000 samples from PersianPunc dataset
• Validation Data: 10,000 samples
• Test Data: 1,000 samples
• Epochs: 3
• Batch Size: 680 (effective, with gradient accumulation)
• Learning Rate: 2e-5
• Optimizer: AdamW
• Weight Decay: 0.01

Citation

If you use this model, please cite:

@misc{kalahroodi2026persianpunclargescaledatasetbertbased,
      title={PersianPunc: A Large-Scale Dataset and BERT-Based Approach for Persian Punctuation Restoration}, 
      author={Mohammad Javad Ranjbar Kalahroodi and Heshaam Faili and Azadeh Shakery},
      year={2026},
      eprint={2603.05314},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.05314}, 
}

License

MIT License

Authors

• Mohammad Javad Ranjbar Kalahroodi (University of Tehran)
• Heshaam Faili (University of Tehran)
• Azadeh Shakery (University of Tehran & IPM)

Contact

For questions or issues, please contact: mohammadjranjbar@ut.ac.ir