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Annotated Model Card Template

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Annotated Model Card Template

Template file


Fully filling out a model card requires input from a few different roles. (One person may have more than one role.) We’ll refer to these roles as the developer, who writes the code and runs training; the sociotechnic, who is skilled at analyzing the interaction of technology and society long-term (this includes lawyers, ethicists, sociologists, or rights advocates); and the project organizer, who understands the overall scope and reach of the model, can roughly fill out each part of the card, and who serves as a contact person for model card updates.

Instructions are provided below, in italics.

Template variable names appear in monospace.

Model Name

Section Overview: Provide the model name and a 1-2 sentence summary of what the model is.



Table of Contents

Section Overview: Provide this with links to each section, to enable people to easily jump around/use the file in other locations with the preserved TOC/print out the content/etc.

Model Details

Section Overview: This section provides basic information about what the model is, its current status, and where it came from. It should be useful for anyone who wants to reference the model.

Model Description


Provide basic details about the model. This includes the architecture, version, if it was introduced in a paper, if an original implementation is available, and the creators. Any copyright should be attributed here. General information about training procedures, parameters, and important disclaimers can also be mentioned in this section.

  • Developed by: developers

List (and ideally link to) the people who built the model.

  • Funded by: funded_by

List (and ideally link to) the funding sources that financially, computationally, or otherwise supported or enabled this model.

  • Shared by [optional]: shared_by

List (and ideally link to) the people/organization making the model available online.

  • Model type: model_type

You can name the “type” as:

1. Supervision/Learning Method

2. Machine Learning Type

3. Modality

  • Language(s) [NLP]: language

Use this field when the system uses or processes natural (human) language.

  • License: license

Name and link to the license being used.

  • Finetuned From Model [optional]: base_model

If this model has another model as its base, link to that model here.

Model Sources optional

  • Repository: repo
  • Paper [optional]: paper
  • Demo [optional]: demo

Provide sources for the user to directly see the model and its details. Additional kinds of resources – training logs, lessons learned, etc. – belong in the More Information section. If you include one thing for this section, link to the repository.


Section Overview: This section addresses questions around how the model is intended to be used in different applied contexts, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. Note this section is not intended to include the license usage details. For that, link directly to the license.

Direct Use


Explain how the model can be used without fine-tuning, post-processing, or plugging into a pipeline. An example code snippet is recommended.

Downstream Use optional


Explain how this model can be used when fine-tuned for a task or when plugged into a larger ecosystem or app. An example code snippet is recommended.

Out-of-Scope Use


List how the model may foreseeably be misused (used in a way it will not work for) and address what users ought not do with the model.

Bias, Risks, and Limitations

Section Overview: This section identifies foreseeable harms, misunderstandings, and technical and sociotechnical limitations. It also provides information on warnings and potential mitigations. Bias, risks, and limitations can sometimes be inseparable/refer to the same issues. Generally, bias and risks are sociotechnical, while limitations are technical:

  • A bias is a stereotype or disproportionate performance (skew) for some subpopulations.
  • A risk is a socially-relevant issue that the model might cause.
  • A limitation is a likely failure mode that can be addressed following the listed Recommendations.


What are the known or foreseeable issues stemming from this model?



What are recommendations with respect to the foreseeable issues? This can include everything from “downsample your image” to filtering explicit content.

Training Details

Section Overview: This section provides information to describe and replicate training, including the training data, the speed and size of training elements, and the environmental impact of training. This relates heavily to the Technical Specifications as well, and content here should link to that section when it is relevant to the training procedure. It is useful for people who want to learn more about the model inputs and training footprint. It is relevant for anyone who wants to know the basics of what the model is learning.

Training Data


Write 1-2 sentences on what the training data is. Ideally this links to a Dataset Card for further information. Links to documentation related to data pre-processing or additional filtering may go here as well as in More Information.

Training Procedure optional



Detail tokenization, resizing/rewriting (depending on the modality), etc.

Speeds, Sizes, Times


Detail throughput, start/end time, checkpoint sizes, etc.


Section Overview: This section describes the evaluation protocols, what is being measured in the evaluation, and provides the results. Evaluation is ideally constructed with factors, such as domain and demographic subgroup, and metrics, such as accuracy, which are prioritized in light of foreseeable error contexts and groups. Target fairness metrics should be decided based on which errors are more likely to be problematic in light of the model use. You can also specify your model’s evaluation results in a structured way in the model card metadata. Results are parsed by the Hub and displayed in a widget on the model page. See

Testing Data, Factors & Metrics

Testing Data


Ideally this links to a Dataset Card for the testing data.



What are the foreseeable characteristics that will influence how the model behaves? This includes domain and context, as well as population subgroups. Evaluation should ideally be disaggregated across factors in order to uncover disparities in performance.



What metrics will be used for evaluation in light of tradeoffs between different errors?



Results should be based on the Factors and Metrics defined above.



What do the results say? This can function as a kind of tl;dr for general audiences.

Model Examination optional

Section Overview: This is an experimental section some developers are beginning to add, where work on explainability/interpretability may go.


Environmental Impact

Section Overview: Summarizes the information necessary to calculate environmental impacts such as electricity usage and carbon emissions.

  • Hardware Type: hardware_type
  • Hours used: hours_used
  • Cloud Provider: cloud_provider
  • Compute Region: cloud_region
  • Carbon Emitted: co2_emitted

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Technical Specifications optional

Section Overview: This section includes details about the model objective and architecture, and the compute infrastructure. It is useful for people interested in model development. Writing this section usually requires the model developer to be directly involved.

Model Architecture and Objective


Compute Infrastructure




What are the minimum hardware requirements, e.g. processing, storage, and memory requirements?



Citation optional

Section Overview: The developers’ preferred citation for this model. This is often a paper.





Glossary optional

Section Overview: This section defines common terms and how metrics are calculated.


Clearly define terms in order to be accessible across audiences.

More Information optional

Section Overview: This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.


Model Card Authors optional

Section Overview: This section lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.


Model Card Contact

Section Overview: Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors


How to Get Started with the Model

Section Overview: Provides a code snippet to show how to use the model.


Please cite as: Ozoani, Ezi and Gerchick, Marissa and Mitchell, Margaret. Model Card Guidebook. Hugging Face, 2022.