Create README.md

README.md

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+---
+base_model: bert-base-multilingual-cased
+tags:
+- persian-nlp
+- text-classification
+- traffic-crash-detection
+- crash-type-classification
+- bert
+- information-extraction
+license: apache-2.0
+language:
+- fa
+pipeline_tag: text-classification
+inference: false
+---
+
+# BERT-Crashtype-Classification
+
+**Fine-tuned BERT for classifying Persian social media texts into 9 crash types.**
+
+📄 **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
+Classifies a given Persian social media text into one of **9 types of traffic crashes**.
+
+### 🏷️ Crash Types (9 Classes)
+1. `vehicle with two-wheeled vehicle`
+2. `Two-wheeled vehicle–pedestrian`
+3. `vehicle with fixed object or ran off road`
+4. `rollover or fall`
+5. `multiple car`
+6. `vehicle–animal`
+7. `vehicle–pedestrian`
+8. `vehicle with single other vehicle`
+9. `two-wheeled vehicle with two-wheeled vehicle`
+
+## ⚙️ Fine-tuning
+- **Base Model:** `bert-base-multilingual-cased`
+- **Data:** Proprietary Persian social media crash dataset (Damavand County, Iran)
+
+## 📊 Performance
+| Task | Metric | Score |
+|------|--------|-------|
+| Crash Type Classification (9 classes) | Accuracy | **89.7%** |
+
+## 🚀 Quick Start
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("crash-information-extraction/BERT-Crashtype-Classification")
+tokenizer = AutoTokenizer.from_pretrained("crash-information-extraction/BERT-Crashtype-Classification")
+
+text = "تصادف دو خودرو در اتوبان"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+predicted_class = outputs.logits.argmax().item()
+# predicted_class is an integer from 0 to 8