---
language: ar
tags:
- text-classification
- spam-detection
library_name: transformers
---
# DistilBERT Spam Classification (Binary)
This model is a fine-tuned DistilBERT for binary spam/ham text classification.
## Labels
- 0: ham
- 1: spam
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
repo_id = "Abdallah-Thuieib/distilbert-spam-classification-binary"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
text = "free money now!!!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
logits = model(**inputs).logits
pred = logits.argmax(-1).item()
print(pred)
