--- language: - fa tags: - persian - sentence-completion - text-classification - bert license: mit datasets: - MohammadJRanjbar/PersianPunc metrics: - f1 - accuracy base_model: - HooshvareLab/bert-base-parsbert-uncased pipeline_tag: text-classification --- # Persian Sentence Completion Classifier A BERT-based classifier that determines whether a Persian sentence is **Complete** or **Incomplete**. Designed for use in ASR post-processing pipelines (e.g. after speech-to-text). ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch MODEL_ID = "MohammadJRanjbar/persian-sentence-completion" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID) model.eval() label_map = {0: "Incomplete", 1: "Complete"} def classify_sentences(sentences, batch_size=16): results = [] for i in range(0, len(sentences), batch_size): batch = sentences[i:i+batch_size] inputs = tokenizer(batch, return_tensors="pt", padding=True, truncation=True, max_length=128) with torch.no_grad(): outputs = model(**inputs) preds = torch.argmax(outputs.logits, dim=-1) results.extend([label_map[p.item()] for p in preds]) return results # Example texts = ["امروز هوا خیلی عالی", "امروز هوا خیلی عالی است."] print(classify_sentences(texts)) # → ['Incomplete', 'Complete'] ``` ## Citation If you use this model, please cite the following works: ```bibtex @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}, } @misc{kalahroodi2025parsvoicelargescalemultispeakerpersian, title = {ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis}, author = {Mohammad Javad Ranjbar Kalahroodi and Heshaam Faili and Azadeh Shakery}, year = {2025}, eprint = {2510.10774}, archivePrefix = {arXiv}, primaryClass = {cs.SD}, url = {https://arxiv.org/abs/2510.10774}, } ```