README.md

---
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](https://doi.org/10.1080/19427867.2026.2681104) – *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
```python
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()