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<link rel="modulepreload" href="/docs/hub/main/en/_app/immutable/chunks/Heading.8660bd0e.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Annotated Model Card Template","local":"annotated-model-card-template","sections":[{"title":"Template","local":"template","sections":[],"depth":2},{"title":"Directions","local":"directions","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="annotated-model-card-template" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#annotated-model-card-template"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Annotated Model Card Template</span></h1> <h2 class="relative group"><a id="template" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#template"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Template</span></h2> <p data-svelte-h="svelte-1oztdix"><a href="https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md" rel="nofollow">modelcard_template.md file</a></p> <h2 class="relative group"><a id="directions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#directions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Directions</span></h2> <p data-svelte-h="svelte-1ppq7fz">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 <strong>developer</strong>, who writes the code and runs training; the <strong>sociotechnic</strong>, who is skilled at analyzing the interaction of technology and society long-term (this includes lawyers, ethicists, sociologists, or rights advocates); and the <strong>project organizer</strong>, 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.</p> <ul data-svelte-h="svelte-xicjd9"><li><p>The <strong>developer</strong> is necessary for filling out <a href="#training-procedure-optional">Training Procedure</a> and <a href="#technical-specifications-optional">Technical Specifications</a>. They are also particularly useful for the “Limitations” section of <a href="#bias-risks-and-limitations">Bias, Risks, and Limitations</a>. They are responsible for providing <a href="#results">Results</a> for the Evaluation, and ideally work with the other roles to define the rest of the Evaluation: <a href="#testing-data-factors--metrics">Testing Data, Factors & Metrics</a>.</p></li> <li><p>The <strong>sociotechnic</strong> is necessary for filling out “Bias” and “Risks” within <a href="#bias-risks-and-limitations">Bias, Risks, and Limitations</a>, and particularly useful for “Out of Scope Use” within <a href="#uses">Uses</a>.</p></li> <li><p>The <strong>project organizer</strong> is necessary for filling out <a href="#model-details">Model Details</a> and <a href="#uses">Uses</a>. They might also fill out
Training Data. Project
organizers could also be in charge of Citation, Glossary, Model Card
Contact, Model Card Authors, and More Information.
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.
model_id
model_summary
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
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.
Uses
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
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
downstream_use
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
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.
bias_risks_limitations
What are the known or foreseeable
issues stemming from this model?
Recommendations
bias_recommendations
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
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
Preprocessing
preprocessing
Detail tokenization,
resizing/rewriting (depending on the modality), etc.
Speeds, Sizes, Times
speeds_sizes_times
Detail throughput, start/end
time, checkpoint sizes, etc.
Evaluation
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
https://huggingface.co/docs/hub/model-cards#evaluation-results.
Testing Data, Factors &
Metrics
Testing Data
testing_data
Ideally this links to a Dataset
Card for the testing data.
Factors
testing_factors
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.
Metrics
testing_metrics
What metrics will be used for
evaluation in light of tradeoffs between different errors?
Results
results
Results should be based on the
Factors and Metrics defined above.
Summary
results_summary
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.
model_examination
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
model_specs
Compute Infrastructure
compute_infrastructure
Hardware
hardware_requirements
What are the minimum hardware
requirements, e.g. processing, storage, and memory
requirements?
Software
software
Citation optional
Section Overview: The developers’
preferred citation for this model. This is often a paper.
BibTeX
citation_bibtex
APA
citation_apa
Glossary optional
Section Overview: This section
defines common terms and how metrics are calculated.
glossary
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.
more_information
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_authors
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
model_card_contact
How to Get Started with the
Model
Section Overview: Provides a code
snippet to show how to use the model.
get_started_code
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