metalearning-class 0.1.6

A library for meta-learning in Python with neural networks and transformers.

Metalearning Library

Project Purpose and Goals

This project provides a Python library for metalearning, focusing on developing and applying advanced machine learning techniques that learn how to learn. The primary goal is to offer a robust and extensible framework for building metalearning models, particularly those leveraging neural networks and transformer architectures, to solve complex regression and classification problems more efficiently and generalize better across various tasks.

The library aims to:

• Facilitate the development of metalearning algorithms.
• Provide implementations of key metalearning components (e.g., meta-learners, task encoders).
• Enable rapid experimentation with different metalearning approaches.
• Support diverse applications by providing flexible model structures.

Last version updates:

[0.1.6] - 2026-05-14

Changed

• Added weights compression, on topology insertion to Sofon.

[0.1.5]

Changed

• Removed task_id parameter in submit_result and load_test_input from Genetic Start.

Basic Usage Examples

Here's a simple example of how you might use a hypothetical MetalearningModel class:

import metalearning_class as mtl
import pandas as pd
# Other imports

# Import data from a Sofon challenge
train_data = ml.subscribe_and_get_task("challenge_taskname")

# Initialize the metalearning model
ml = mtl.Metalearning(gpu=False)

# Login and get token with your Sofon account
# SUGGESTION: use dotenv
ml.login("username", "password")

# Train the model (this is highly dependent on the actual implementation)

[...]

Contribution

We soon will become open-source, and welcome contributions to the Metalearning Library!

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