metadata
base_model: bert-base-multilingual-cased
tags:
- persian-nlp
- text-classification
- traffic-crash-detection
- bert
- information-extraction
license: apache-2.0
language:
- fa
pipeline_tag: text-classification
inference: false
BERT-Crash-nonCrash-Classification
Fine-tuned BERT for detecting crash-related Persian social media texts.
📄 Paper: Extracting traffic crash information from social media: an LLM-based approach – Transportation Letters (2026)
🎯 What it does
Binary classifier to determine whether a given Persian social media text is related to a traffic crash or not.
⚙️ Fine-tuning
- Base Model:
bert-base-multilingual-cased - Data: Proprietary Persian social media crash dataset (Damavand County, Iran)
📊 Performance
| Task | Metric | Score |
|---|---|---|
| Crash Detection (Binary) | Accuracy | 91.1% |
🚀 Quick Start
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crash-information-extraction/BERT-Crash-nonCrash-Classification")
tokenizer = AutoTokenizer.from_pretrained("crash-information-extraction/BERT-Crash-nonCrash-Classification")
text = "تصادف در خیابان آزادی ۲ کشته داشت"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax().item()