Writing model documentation

The model documentation that is displayed on tfhub.dev is written in the Markdown format.

Types of markdown documentation

There are 3 types of markdown documentation used in tfhub.dev:

  • Publisher markdown - contains information about a publisher (learn more in the becoming a publisher guide).
  • Model markdown - contains information about a specific model. (learn more in the contribute a model guide).
  • Collection markdown - contains information about a publisher-defined collection of models. (learn more in the creating a collection guide).

Content organization

The following content organization is recommended when contributing to the TensorFlow Hub GitHub repository:

  • each publisher directory is in the assets directory.
  • each publisher directory contains optional models and collections directories
  • each model should have its own directory under assets/publisher_name/models
  • each collection should have its own directory under assets/publisher_name/collections

Publisher and collection markdowns are unversioned, while models can have different versions. Each model version requires a separate markdown file named after the version it describes (i.e. 1.md, 2).

All model versions for a given model should be located in the model directory.

Below is an illustration on how the markdown contant is organized:

├── publisher_name_a
│   ├── publisher_name_a.md  -> Documentation of the publisher.
│   └── models
│       └── model          -> Model name with slashes encoded as sub-path.
│           ├── 1.md       -> Documentation of the model version 1.
│           └── 2.md       -> Documentation of the model version 2.
├── publisher_name_b
│   ├── publisher_name_b.md  -> Documentation of the publisher.
│   ├── models
│   │   └── ...
│   └── collections
│       └── collection     -> Documentation for the collection feature.
│           └── 1.md
├── publisher_name_c
│   └── ...
└── ...

Common metadata properties

The collection and model markdowns can contain metadata properties. These are represented as markdown comments after the first line of the markdown file.

For example, <!-- network-architecture: BigGAN --> states that a model or a collection of models have BigGAN architecture.

The following metadata properties can be used in both model and collection markdown files:

  • module-type: the problem domain, for example "Text embedding" or "Image classifier"
  • dataset: the dataset the model(s) was trained on.
  • netowk-architecture: the network architecture.
  • language: commonly used for text models or collections to describe the language of the dataset the model was trained on. This property is not necessary for non-text models, e.g. an image classifier.