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Updated documentation
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adamspd committed Jan 22, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/tests.yml
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# This workflow will install Python dependencies, run tests and lint with a single version of Python
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python

name: Spam Detection Testing
name: tests

on:
push:
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7 changes: 4 additions & 3 deletions README.md
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# Spam-Detector-AI

![Tests](https://github.com/adamspd/spam-detection-project/actions/workflows/tests.yml/badge.svg)
[![PyPI version](https://badge.fury.io/py/spam-detector-ai.svg)](https://badge.fury.io/py/spam-detector-ai)
[![Current Release Version](https://img.shields.io/github/release/adamspd/spam-detection-project.svg?style=flat-square&logo=github)](https://github.com/adamspd/spam-detection-project/releases)
[![pypi Version](https://img.shields.io/pypi/v/spam-detector-ai.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spam-detector-ai/)
[![PyPi downloads](https://static.pepy.tech/personalized-badge/spam-detector-ai?period=total&units=international_system&left_color=grey&right_color=orange&left_text=pip%20downloads)](https://pypi.org/project/spacy/)
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![GitHub last commit](https://img.shields.io/github/last-commit/adamspd/spam-detection-project)](https://github.com/adamspd/spam-detection-project/commit/main)
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## Project Structure

- `classifiers/`: Contains the different classifiers (Naive Bayes, Random Forest, SVM).
- `classifiers/`: Contains the different classifiers (Naive Bayes, Random Forest, SVM, XGB & Logistic Regression).
- `data/`: Contains the sample dataset for training the classifiers.
- `loading_and_processing/`: Contains utility functions for loading and preprocessing data.
- `models/`: Contains the trained models and their vectorizers.
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## Notes

The project contains 3 pre-trained models that can be used directly if you want to skip the training step.
The project contains 5 pre-trained models that can be used directly if you want to skip the training step.
If you don't want to use the package, you can use the API that I have deployed
[here](https://spam-detection-api.adamspierredavid.com/).

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2 changes: 1 addition & 1 deletion spam_detector_ai/__init__.py
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__description__ = "A package for detecting and filtering spam messages using Machine Learning models."
__package_name__ = "spam-detector-ai"
__url__ = "https://github.com/adamspd/spam-detection-project"
__version__ = "2.1.0"
__version__ = "2.1.1"
__test_version__ = False

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