---
license: apache-2.0
datasets:
- ucirvine/sms_spam
language:
- en
- hi
- te
metrics:
- accuracy
- f1
base_model:
- distilbert/distilbert-base-uncased
tags:
- text_classification
- spam_detection
- distilbert
---
# Spam Detection using DistilBERT

This model is a fine-tuned `distilbert-base-uncased` transformer for binary
spam classification (spam vs ham).

## Labels
- 0 → Ham
- 1 → Spam

## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("<your-username>/spam-detection-distilbert")
model = AutoModelForSequenceClassification.from_pretrained("<your-username>/spam-detection-distilbert")

inputs = tokenizer(
    "You won a free iPhone!",
    return_tensors="pt",
    truncation=True,
    padding="max_length",
    max_length=128
)

with torch.no_grad():
    outputs = model(**inputs)

prediction = torch.argmax(outputs.logits, dim=1).item()
print("SPAM" if prediction == 1 else "HAM")
```


## 🔗 GitHub Repository

Code for training and inference is available here:  
https://github.com/revanthreddy0906/spam-detection-distilbert.git