Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/tensorflow/model-card-toolkit

A toolkit that streamlines and automates the generation of model cards
https://github.com/tensorflow/model-card-toolkit

deep-learning machine-learning model-cards responsible-ai responsible-ml tensorflow transparency

Last synced: 2 months ago
JSON representation

A toolkit that streamlines and automates the generation of model cards

Host: GitHub
URL: https://github.com/tensorflow/model-card-toolkit
Owner: tensorflow
License: apache-2.0
Created: 2020-07-24T16:48:58.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2023-07-26T12:05:00.000Z (10 months ago)
Last Synced: 2024-01-07T01:44:56.097Z (5 months ago)
Topics: deep-learning, machine-learning, model-cards, responsible-ai, responsible-ml, tensorflow, transparency
Language: Python
Homepage: https://www.tensorflow.org/responsible_ai/model_card_toolkit/guide
Size: 9.05 MB
Stars: 396
Watchers: 20
Forks: 82
Open Issues: 7
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS

Lists

awesome-model-cards - TensorFlow model card toolkit - model-card-toolkit-for.html)) (Further reading list)
awesome-production-machine-learning - Model Card Toolkit - card-toolkit.svg?style=social) - streamlines and automates generation of [Model Cards](https://modelcards.withgoogle.com/about). (Metadata Management)
awesome-seml - Model Card Toolkit - Streamlines and automates the generation of model cards; for model documentation. (Tooling)
awesome-production-machine-learning - Model Card Toolkit - card-toolkit.svg?style=social) - streamlines and automates generation of [Model Cards](https://modelcards.withgoogle.com/about) (Metadata Management)
Awesome-AIML-Data-Ops - Model Card Toolkit - card-toolkit.svg?style=social) - streamlines and automates generation of [Model Cards](https://modelcards.withgoogle.com/about) (Metadata Management)
awesome-python-machine-learning-resources - GitHub - 85% open · ⏱️ 28.04.2022): (模型的可解释性)
awesome-privacy-engineering - Model Card Toolkit - Google's Model Card Toolkit streamlines and automates generation of [Model Cards](https://modelcards.withgoogle.com/about), machine learning documents that provide context and transparency into a model's development and performance. (Awesome Privacy Engineering [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) / Machine Learning and Algorithmic Bias)
jimsghstars - tensorflow/model-card-toolkit - A toolkit that streamlines and automates the generation of model cards (Python)
awesome-machine-learning-interpretability - tensorflow/model-card-toolkit - card-toolkit?style=social) | "streamlines and automates generation of Model Cards, machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables you to share model metadata and metrics with researchers, developers, reporters, and more.” | (Technical Resources / Open Source/Access Responsible AI Software Packages)

README

        # Model Card Toolkit

[![CI][ci_badge]][ci_link]

[![PyPI][pypi_badge]][pypi_link]

[![Documentation][docs_badge]][docs_link]

The Model Card Toolkit (MCT) streamlines and automates generation of

[Model Cards](https://modelcards.withgoogle.com/about) [1], machine learning documents

that provide context and transparency into a model's development and performance.

Integrating the MCT into your ML pipeline enables you to share 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](https://raw.githubusercontent.com/tensorflow/model-card-toolkit/main/model_card_toolkit/documentation/guide/images/model_card.png)

## Installation

The Model Card Toolkit is hosted on [PyPI](https://pypi.org/project/model-card-toolkit/),

and requires Python 3.7 or later.

Installing the basic, framework agnostic package:

```sh

pip install model-card-toolkit

```

If you are generating model cards for TensorFlow models, install the optional

TensorFlow dependencies to use Model Card Toolkit's TensorFlow utilities:

```sh

pip install model-card-toolkit[tensorflow]

```

You may need to append the `--use-deprecated=legacy-resolver` flag when running

versions of pip starting with 20.3.

See [the installation guide](model_card_toolkit/documentation/guide/install.md)

for more installation options.

## Getting Started

    import model_card_toolkit as mct

    # Initialize the Model Card Toolkit with a path to store generate assets

    model_card_output_path = ...

    toolkit = mct.ModelCardToolkit(model_card_output_path)

    # Initialize the ModelCard, which can be freely populated

    model_card = toolkit.scaffold_assets()

    model_card.model_details.name = 'My Model'

    # Write the model card data to a proto file

    toolkit.update_model_card(model_card)

    # Return the model card document as an HTML page

    html = toolkit.export_format()

## Model Card Generation on TFX

If you are using [TensorFlow Extended (TFX)](https://www.tensorflow.org/tfx), you can

incorporate model card generation into your TFX pipeline via the `ModelCardGenerator`

component.

The `ModelCardGenerator` component has moved to the

[TFX Addons](https://github.com/tensorflow/tfx-addons) library and is no longer

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:

```sh

pip install tfx-addons[model_card_generator]

```

See the [ModelCardGenerator guide](https://github.com/tensorflow/tfx-addons/blob/main/tfx_addons/model_card_generator/README.md)

and run the [case study notebook](https://github.com/tensorflow/tfx-addons/blob/main/examples/model_card_generator/MLMD_Model_Card_Toolkit_Demo.ipynb)

to learn more about the component.

## 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

[ci_badge]: https://github.com/tensorflow/model-card-toolkit/actions/workflows/ci.yml/badge.svg

[ci_link]: https://github.com/tensorflow/model-card-toolkit/actions/workflows/ci.yml

[pypi_badge]: https://badge.fury.io/py/model-card-toolkit.svg

[pypi_link]: https://badge.fury.io/py/model-card-toolkit

[docs_badge]: https://img.shields.io/badge/TensorFow-page-orange

[docs_link]: https://www.tensorflow.org/responsible_ai/model_card_toolkit/guide