model_card templates

We recommend sharing information about each model in a model_card.

The canonical documentation about model cards is at https://huggingface.co/docs

One of the easiest way to get started is by using our template card. Simply copy template.README.md to model_cards/USERNAME/MODELNAME/README.md and fill it out while studying demo.README.md for an example of a model model_card and referring to rest of this short document if you are not sure about some specific fields.

YAML metadata section

Here is a sample of a typical yaml metadata section:

---
language:
- ru
- en
thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
tags:
- translation
- fsmt
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
- sacrebleu
---

Important:

• This section has to be first in the document, before any markdown sections.
• The data in this section doesn't need to be repeated again in the subsequent markdown sections - since it'll be automatically expanded into a user-friendly format on the website.

language

ISO 639-1 code for your language (e.g. ru, en, de, or multilingual)

thumbnail

"url to a thumbnail used in social sharing"

tags:

• array
• of
• tags

license

One of the valid license identifiers. e.g.:

- apache-2.0
- mit 

datasets

One or more dataset identifiers.

Example:

- wmt19
- wmt16

You will find the supported list at https://huggingface.co/datasets

metrics:

One or more metric identifiers. e.g.:

- bleu
- rouge
- sacrebleu

You will find the supported list at https://huggingface.co/metrics

Markdown

Model name

Model description

You can embed local or remote images using ![](...)

Intended uses & limitations

How to use

# You can include sample code which will be formatted

Limitations and bias

Provide examples of latent issues and potential remediations.

Training data

Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.

Training procedure

Preprocessing, hardware used, hyperparameters...

Eval results

BibTeX entry and citation info

@inproceedings{...,
  year={2020}
}