Model Card Toolkit

The Model Card Toolkit (MCT) streamlines and automates generation of Model Cards [1], machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables the sharing model metadata and metrics with researchers, developers, reporters, and more.

Some use cases of model cards include:

• Facilitating the exchange of information between model builders and product developers.
• Informing users of ML models to make better-informed decisions about how to use them (or how not to use them).
• Providing model information required for effective public oversight and accountability.

Installation

The Model Card Toolkit is hosted on PyPI, and can be installed with pip install model-card-toolkit (or pip install model-card-toolkit --use-deprecated=legacy-resolver for versions of pip starting with 20.3). See the installation guide for more details.

Getting Started

import model_card_toolkit

# Initialize the Model Card Toolkit with a path to store generate assets
model_card_output_path = ...
mct = model_card_toolkit.ModelCardToolkit(model_card_output_path)

# Initialize the model_card_toolkit.ModelCard, which can be freely populated
model_card = mct.scaffold_assets()
model_card.model_details.name = 'My Model'

# Write the model card data to a proto file
mct.update_model_card(model_card)

# Return the model card document as an HTML page
html = mct.export_format()

Model Card Generation on TFX

If you are using TensorFlow Extended (TFX), you can incorporate model card generation into your TFX pipeline via the ModelCardGenerator component. See our guide for more details, this case study for a demonstration.

Schema

Model cards are stored in proto as an intermediate format. You can see the model card JSON schema in the schema directory.

References

[1] https://arxiv.org/abs/1810.03993