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crash-information-extraction/
BERT-Crashtype-Classification

Name: BERT-Crashtype-Classification
Availability: InStock

persian-nlp

text-classification

traffic-crash-detection

crash-type-classification

bert

information-extraction

google-bert/bert-base-multilingual-cased

finetune

apache-2.0

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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 – 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)

vehicle with two-wheeled vehicle
Two-wheeled vehicle–pedestrian
vehicle with fixed object or ran off road
rollover or fall
multiple car
vehicle–animal
vehicle–pedestrian
vehicle with single other vehicle
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