Skip to main content

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.

Generated model card image

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.

The ModelCardGenerator component is moving to the TFX Addons library and will no longer be packaged in Model Card Toolkit from version 2.0.0. Before you can use the component, you will need to install the tfx-addons package:

pip install tfx-addons[model_card_generator]

This page will be updated to include the new links for the Model Cards in TFX guide and the end-to-end demo when the migration is completed.

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

Metadata

Release files for model-card-toolkit 2.0.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for model-card-toolkit 2.0.0
File Interpreter ABI Platform
model_card_toolkit-2.0.0-py3-none-any.whl Python 3 none any Details

Release files / model_card_toolkit-2.0.0-py3-none-any.whl

Download URL model_card_toolkit-2.0.0-py3-none-any.whl
Size 68.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b195c4678529c6f14096b60ee263c81ff7c81c27773f90e81e55d06923d95844
BLAKE2b-256 checksum
How to use checksums
e686e4c3f63cd6dbba95e22d56f4f5e486f3dc78762b3b053f7ee0b50bafd0c5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.9.16

Release history Release notifications | RSS feed

This release

2.0.0 This release

1 release file

1.3.2

2 release files

1.3.1

2 release files

1.3.0

2 release files

1.2.0

2 release files

1.1.0

2 release files

1.0.0

2 release files

0.1.3

2 release files

0.1.2

2 release files

0.1.1

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page