What metrics does the skill provide to evaluate the adapted model?

It automatically calculates and reports key performance indicators such as accuracy, precision, recall, and F1-score.

Can I use this for both image and text models?

Yes, the skill is designed to handle various modalities, including computer vision models like ResNet and natural language models like BERT.

Which machine learning frameworks are supported?

The skill generates code compatible with major Python frameworks, primarily focusing on PyTorch and TensorFlow.

Does it save the training results?

Yes, the skill saves the adapted model, training logs, and performance metrics, and it generates documentation of the process.

Does the skill handle data preprocessing?

Yes, it includes logic for data validation and preprocessing steps to ensure your dataset matches the input requirements of the pre-trained model.

Transfer Learning Adapter

Name: Transfer Learning Adapter
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Automates the fine-tuning and adaptation of pre-trained machine learning models for custom datasets and specialized tasks.

About

The Transfer Learning Adapter skill streamlines the complex process of model adaptation by generating production-ready Python code using frameworks like PyTorch and TensorFlow. It bridges the gap between general-purpose pre-trained models and specific domain requirements, handling everything from data validation and preprocessing to performance metric calculation. This skill is ideal for developers looking to leverage state-of-the-art architectures for niche classification, sentiment analysis, or forecasting tasks without the resource-intensive requirement of training models from scratch.

Key Features

Standardized artifact saving and documentation generation
Support for major ML frameworks including PyTorch and TensorFlow
Automated Python code generation for model fine-tuning
Built-in data validation and training error handling
0 GitHub stars
Automatic calculation of KPIs like F1-score, precision, and recall

Use Cases

Optimizing pre-trained time-series models for custom forecasting pipelines
Adapting BERT or other LLMs for domain-specific sentiment analysis
Fine-tuning vision models like ResNet50 for specialized image classification

About

Key Features

Standardized artifact saving and documentation generation
Support for major ML frameworks including PyTorch and TensorFlow
Automated Python code generation for model fine-tuning
Built-in data validation and training error handling
0 GitHub stars
Automatic calculation of KPIs like F1-score, precision, and recall

Use Cases

Optimizing pre-trained time-series models for custom forecasting pipelines
Adapting BERT or other LLMs for domain-specific sentiment analysis
Fine-tuning vision models like ResNet50 for specialized image classification