bert-sms-detector

bert-sms-detector is a fine-tuned BERT-based model for SMS spam detection.
It classifies input text messages as spam or ham (not spam).

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Model Details

• Base Model: bert-base-uncased
• Task: Text classification (spam detection)
• Dataset: UC Irvine SMS Spam Collection
• Fine-tuning: The model has been fine-tuned to detect spam messages from SMS text.

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Usage

!pip install transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_name = "alanjoshua2005/bert-sms-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

texts = [
    "Congratulations! You've won a free ticket to the Bahamas.",
    "Hey, are we still meeting tomorrow at 5 PM?",
    "Free entry in 2 tickets to the concert. Text WIN to 80088."
]

label_map = {"LABEL_0": "Not Spam", "LABEL_1": "Spam"}

results = classifier(texts)

for text, result in zip(texts, results):
    print(f"Text: {text}\nPrediction: {label_map[result['label']]}\n")