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A low code Machine Learning Platform.

ModelForge

ModelForge is a powerful and user-friendly platform that simplifies the machine learning process, allowing you to build and deploy models without extensive coding. It simplifies model setup and requirements with config files. Improves productivity and saves time for ML practitioners by allowing them to plug in parameters and utilize different models according to their needs. Read more

How it Works

• Load Your Dataset: Simply provide the path to your dataset CSV file and the corresponding YAML configuration file.

• Specify Model Requirements: In the YAML file, define the desired model architecture, input and output features, preprocessing steps, and evaluation metrics.

Key Features:

• Low-Code Approach: No extensive coding is required. Simply provide the necessary information in the YAML file.
• Flexibility: Customize your models by specifying different architectures, features, and preprocessing steps.
• Efficiency: ModelForge automates many time-consuming tasks, saving you valuable time.

Getting Started

Create a virtual environment, run the following command after cloning the repo

python -m venv venv

Activate the virtual environment

• Windows:

  venv\Scripts\activate

• MacOS:

    source venv/bin/activate

  Install required python packages [Make sure virtual environment is running]

pip install -r requirements.txt

To run the file

 python main.py <filename>.yaml

Project Structure:

ModelForge/
├── datasets/
│   └── ...         #sample datasets we have worked on
|
├── examples/
│   ├── roberta.yaml  # Example configuration file for roberta model
│   ├── modelarch.yaml   # model architecture
|   ├── rnn-params.yaml   # rnn encoder-decoder architecture
│   └── transformer.yaml  # transformer model
|
├── preprocessed_data/  # stores preprocessed data
│   ├── dataset.test.hdf5
│   ├── dataset.training.hdf5
│   └── dataset.validation.hdf5
|
├── samples/          # ML problems modelforge worked on
│   └── ...
│
├── readme.md
├── main.py            # Main script for running the project
└── requirements.txt