safaa 0.0.1

Created as a part of the 2023 Google Summer of Code project: Reducing Fossology's False Positive Copyrights, the purpose is to be able to predict whether a given copyright output from the Fossology software is a false positive or not. It is also able to remove extra text from a copyright notice.

Safaa

Safaa is a Python package designed for handling false positive detection in copyright notices. Additionally, it can declutter copyright notices, removing unnecessary extra text.

Features

• Load pre-trained models or train your own.
• Integration with scikit-learn for training and prediction.
• Integrated with spaCy for named entity recognition and decluttering tasks.
• Preprocessing tools to ensure data consistency and quality.
• Ability to handle local or default model directories.

Installation

To install Safaa, simply use pip:

pip install safaa

Usage

Initialization

from safaa.Safaa import *
agent = SafaaAgent()

Preprocessing Data

data = ["Your raw data here"]
preprocessed_data = agent.preprocess_data(data)

Predicting False Positives

predictions = agent.predict(data)

Decluttering Copyright Notices

decluttered_data = agent.declutter(data, predictions)

Training Models

To train the false positive detector:

training_data = ["Your training data here"]
labels = ["Your labels here"]
agent.train_false_positive_detector_model(training_data, labels)

To train the named entity recognition model:

train_path = "path/to/train.spacy"
dev_path = "path/to/dev.spacy"
agent.train_ner_model(train_path, dev_path)

Saving Trained Models

save_path = "path/to/save"
agent.save(save_path)

Dependencies

• scikit-learn
• spaCy
• joblib
• regex
• os
• shutil

License

This project is licensed under the GNU LESSER GENERAL PUBLIC LICENSE, Version 2.1, February 1999.

Contact Information

• Name: Abdelrahman Jamal
• Email: [e-mail]
• LinkedIn: linkedin.com/in/abdelrahmanjamal