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🎉 **BREAKING** : New major version Melusine 3.0 is available 🎉
pip install melusine
Discover Melusine, a comprehensive email processing library designed to optimize your email workflow. Leverage Melusine's advanced features to achieve:
Melusine facilitates the integration of deep learning frameworks (HuggingFace, Pytorch, Tensorflow, etc), deterministic rules (regex, keywords, heuristics) into a full email qualification workflow.
Melusine stands out with its combination of features and advantages:
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.
Explore our comprehensive documentation and tested tutorials to get started. Or dive into our minimal example to experience Melusine's simplicity and power:
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.
FAQs
Melusine is a high-level library for emails processing
We found that melusine demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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