https://github.com/MAIF/melusine

📧 Melusine: Use python to automatize your email processing workflow
https://github.com/MAIF/melusine

courriels datascience emails natural-language-processing nlp nlp-machine-learning python python3

Last synced: 7 days ago
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📧 Melusine: Use python to automatize your email processing workflow

• Host: GitHub
• URL: https://github.com/MAIF/melusine
• Owner: MAIF
• License: other
• Created: 2019-02-18T14:11:41.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-01-17T16:10:26.000Z (5 months ago)
• Last Synced: 2024-04-26T20:46:57.940Z (about 2 months ago)
• Topics: courriels, datascience, emails, natural-language-processing, nlp, nlp-machine-learning, python, python3
• Language: Python
• Homepage: https://maif.github.io/melusine
• Size: 45 MB
• Stars: 344
• Watchers: 21
• Forks: 59
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Authors: AUTHORS.rst

Lists

• awesome_quantmetry - melusine - contrib] (Natural Language Processing)
• awesome-stars - MAIF/melusine - 📧 Melusine: Use python to automatize your email processing workflow (Python)
• awesome-stars - MAIF/melusine - 📧 Melusine: Use python to automatize your email processing workflow (Python)

README

        




























🎉 **BREAKING** : New major version Melusine 3.0 is available 🎉












- **Free software**: Apache Software License 2.0

- **Documentation**: [maif.github.io/melusine](https://maif.github.io/melusine/)

- **Installation**: `pip install melusine`

- **Tutorials**: [Discover melusine](https://maif.github.io/melusine/tutorials/00_GettingStarted/)

## Overview

Discover Melusine, a comprehensive email processing library

designed to optimize your email workflow.

Leverage Melusine's advanced features to achieve:

- **Effortless Email Routing**: Ensure emails reach their intended destinations with high accuracy.

- **Smart Prioritization**: Prioritize urgent emails for timely handling and efficient task management.

- **Snippet Summaries**: Extract relevant information from lengthy emails, saving you precious time and effort.

- **Precision Filtering**: Eliminate unwanted emails from your inbox, maintaining focus and reducing clutter.

Melusine facilitates the integration of deep learning frameworks (HuggingFace, Pytorch, Tensorflow, etc),

deterministic rules (regex, keywords, heuristics) into a full email qualification workflow.

## Why Choose Melusine ?

Melusine stands out with its combination of features and advantages:

- **Pre-packaged Tools** : Melusine comes with out-of-the-box features such as

    - Segmenting an email conversation into individual messages

    - Tagging message parts (Email body, signatures, footers, etc)

    - Transferred email handling

- **Streamlined Execution** : Focus on the core email qualification logic

while Melusine handles the boilerplate code, providing debug mode, pipeline execution, code parallelization, and more.

- **Flexible Integrations** : Melusine's modular architecture enables seamless integration with various AI frameworks,

ensuring compatibility with your preferred tools.

- **Production ready** : Proven in the MAIF production environment,

Melusine provides the robustness and stability you need.

## Email Segmentation Exemple

In the following example, an email is divided into two distinct messages

separated by a transition pattern.

Each message is then tagged line by line.

This email segmentation can later be leveraged to enhance the performance of machine learning models.











## Getting started

Explore our comprehensive [documentation](https://maif.github.io/melusine/) and tested [tutorials](https://maif.github.io/melusine/tutorials/00_GettingStarted/) to get started.

Or dive into our minimal example to experience Melusine's simplicity and power:

``` Python

    from melusine.data import load_email_data

    from melusine.pipeline import MelusinePipeline

    # Load an email dataset

    df = load_email_data()

    # Load a pipeline

    pipeline = MelusinePipeline.from_config("demo_pipeline")

    # Run the pipeline

    df = pipeline.transform(df)

```

The code above executes a default pipeline and returns a qualified email dataset with columns such as:

- `messages`: List of individual messages present in each email.

- `emergency_result`: Flag to identify urgent emails.

With Melusine, you're well-equipped to transform your email handling, streamlining processes, maximizing efficiency,

and enhancing overall productivity.