Section Overview: Provide the model name and a 1-2 sentence summary of what the model is.
model_id
model_summary
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.
Section Overview: This section provides basic information about what the model is, its current status, where it came from. It should be useful for anyone who wants to reference the model.
model_description
Provide details about the model. This includes the architecture, version, if it was introduced in a paper, if an implementation is available, and the creators. Any copyright should be attributed here. General information about training procedures, parameters, important disclaimers can also be mentioned in this section.
developers
List (and ideally link to) the people who built the model.
funded_by
List (and ideally link to) the funding sources that financially, computationally, or otherwise supported or enabled this model.
shared_by
List (and ideally link to) the people/organization making the model available online.
model_type
You can name the “type” as:
1. Supervision/Learning Method
2. Machine Learning Type
3. Modality
language
Use this field when the system uses or processes natural (human) language..
license
Name and link to the license being used.
base_model
** this model has another model as its base, link to that model here.
repopaperdemo
Provide sources for the user to 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: questions around how the model is intended to be used in different applied contexts, discusses the foreseeable users of the model (including those ... by the model). 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
Explain how this model can be used and 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.
Section Overview: This section identifies harms, misunderstandings, and technical and sociotechnical limitations. It also provides 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:
bias_risks_limitations
What are the known or foreseeable issues stemming from this model?
bias_recommendations
What are recommendations with respect to the foreseeable issues? This can include everything from “downsample your image” to filtering explicit content..
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. Technical Specifications as well, and content here should link to that section when it is relevant to the training procedure. useful for people who want to learn more about the model inputs training footprint. for anyone who wants to know the basics of what the model is learning.
training_data
Write 1-2 sentences related to data pre-processing or additional filtering More Information.
preprocessing
Detail tokenization, resizing/rewriting (depending on the modality), etc.
speeds_sizes_times
Detail throughput, start/end time, checkpoint sizes, etc.
Section Overview: evaluation protocols. Target fairness metrics should be decided based on errors are more likely to be identified in light of the model use. specify model’s evaluation results in a structured way in the model card metadata. parsed and displayed in a widget on the model page. See https://huggingface.co/docs/hub/model-cards#evaluation-results.
testing_data
Ideally this links to a Dataset Card for testing data.
testing_factors
What are the foreseeable circumstances 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.
testing_metrics
metrics for evaluation in light of tradeoffs between different errors?
results
Results based on the Factors and Metrics defined above.
results_summary
What do the results say? This can function as a kind of tl;dr for general audiences..
Section Overview: examination
model_examination
Section Overview: Summarizes the information necessary to calculate environmental impacts .
hardware_typehours_usedcloud_providercloud_regionco2_emitted
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in source.
Section Overview: This section includes details about the model architecture, and the compute infrastructure.
model_specs
compute_infrastructure
hardware_requirements
What are the minimum hardware requirements, e.g. processing, storage, and memory requirements?
software
Section Overview: The developers’ preferred citation for this model.
citation_bibtex
citation_apa
Section Overview: This section defines common terms and how metrics are calculated.
glossary
Clearly define terms in order to be accessible across audiences.
Section Overview: lessons learned and more .
more_information
Section Overview: who create the model card, .
model_card_authors
Section Overview: contact
model_card_contact
Section Overview: Provides a code snippet to show how to use the model.
get_started_code