metadata

language: en
license: mit
tags:
  - keras
  - lstm
  - spam-classification
  - text-classification
  - binary-classification
  - email
  - deep-learning
library_name: keras
pipeline_tag: text-classification
model_name: Spam Email Classifier (BiLSTM)
datasets:
  - SetFit/enron_spam

📧 Spam Email Classifier using BiLSTM

This model uses a Bidirectional LSTM (BiLSTM) architecture built with Keras to classify email messages as Spam or Ham. It was trained on the Enron Spam Dataset using GloVe word embeddings.

🧠 Model Architecture

Tokenizer: Keras Tokenizer trained on the Enron dataset
Embedding: Pretrained GloVe.6B.100d
Model: Embedding → BiLSTM → Dropout → Dense(sigmoid)
Input: English email/message text
Output: 0 = Ham, 1 = Spam

🧪 Example Usage

from tensorflow.keras.models import load_model
from huggingface_hub import hf_hub_download
import pickle
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Load files from HF Hub
model_path = hf_hub_download("lokas/spam-emails-classifier", "model.h5")
tokenizer_path = hf_hub_download("lokas/spam-emails-classifier", "tokenizer.pkl")

# Load model and tokenizer
model = load_model(model_path)
with open(tokenizer_path, "rb") as f:
    tokenizer = pickle.load(f)

# Prediction function
def predict_spam(text):
    seq = tokenizer.texts_to_sequences([text])
    padded = pad_sequences(seq, maxlen=50)  # must match training maxlen
    pred = model.predict(padded)[0][0]
    return "🚫 Spam" if pred > 0.5 else "✅ Not Spam"

# Example
print(predict_spam("Win a free iPhone now!"))