Muthukumar045454/SMS_Spam_detection

Spam Detection Model This repository contains a machine learning model trained to detect spam messages. The model is designed to classify messages as either spam or not spam, based on the content of the message. It is suitable for use in email filters, SMS classification systems, and other applications requiring spam detection.

Model Overview Model Type: [e.g., BERT, LSTM, Logistic Regression, etc.] Trained on: [Briefly describe the dataset you used, e.g., SMS Spam Collection dataset] Input: Text messages (string format) Output: 0 (Not Spam), 1 (Spam) Installation To use this model, you'll need to install the transformers library from Hugging Face. You can install it using pip:

bash Copy pip install transformers Additionally, you might need torch for PyTorch-based models or tensorflow for TensorFlow-based models:

bash Copy pip install torch # For PyTorch models pip install tensorflow # For TensorFlow models How to Use the Model Load the Model You can easily load the model and tokenizer using Hugging Face's transformers library.

python Copy from transformers import pipeline

Load the pre-trained model

model = pipeline('text-classification', model='muthukumar045454/SMS_spam_detection')

Example input

message = "Congratulations! You've won a $1000 gift card. Click here to claim."

Prediction

result = model(message) print(result) Model Inference The model will output a classification result, indicating whether the message is spam (1) or not spam (0).

Training Details Dataset: https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset Preprocessing: [Any specific preprocessing done to the dataset, e.g., tokenization, removing stop words] Training Framework: [e.g., PyTorch, TensorFlow] Hyperparameters: [List of key hyperparameters used during training, such as learning rate, batch size, epochs] Model Evaluation Accuracy: [Provide the model's accuracy or other evaluation metrics on the validation/test dataset] Confusion Matrix: [Optional – include confusion matrix for deeper evaluation] License This model is licensed under the [insert your preferred license]. Please see the LICENSE file for more details.