Captured before automated identity checks (Oct 14, 2026): accurate as of its capture on Sep 11, 2026; our capture on Oct 21, 2026 found the same text, but its identity was not checked by them.
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This page describes what the document states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability may vary by jurisdiction. Methodology
“For model repos created after August 2024, this is not the case anymore, so you need to set library_name: transformers explicitly.”
“You can add a not-for-all-audiences tag to your model card metadata. When this tag is present, a message will be displayed on the model page indicating that the model is not for all audiences.”
“this approach is discouraged, and repo creators should use the explicit library_name as much as possible.”
“If required, you can also specify a custom license by adding other as the license value and specifying the name and a link to the license in the metadata.”
“You can 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.”
“Specifying library_name in the model card (recommended if your model is not a transformers model).”
These guidelines tell anyone publishing a model on Hugging Face what they must include in their model card and how to set key metadata fields. You are required to document your model's intended uses, limitations, biases, training data, training details, and evaluation results. Metadata like the license, pipeline tag, and library name are not just labels—they control what gets displayed on your model page and how other users can find and interact with your model.
The Hugging Face Model Card Guidelines establish mandatory disclosure and metadata requirements for models hosted on the Hugging Face Hub. Creators are required to include in the model card: intended uses and potential limitations (including biases and ethical considerations), training datasets, training parameters and experimental information, and evaluation results. Metadata fields—including license (with custom licenses requiring an 'other' value plus a name and link), pipeline tag, and library_name—carry direct functional consequences: the pipeline tag controls the interactive widget and underlying APIs, structured evaluation results are parsed and rendered as a Hub widget, and the license is publicly displayed and used as a filter. For model repos created after August 2024, explicit declaration of library_name: transformers is required, as automatic detection no longer applies.
For a user browsing or downloading models on Hugging Face, these guidelines mean that model pages are required to disclose intended uses, limitations, biases, ethical considerations, training datasets, and evaluation results—giving users structured information to assess a model before using it. When a creator adds the not-for-all-audiences tag, Hugging Face automatically displays a warning on that model's page. Users can filter models by license, which is drawn directly from the metadata the creator is required to provide.
Which mapped governance frameworks each document engages, tied to the specific provisions that engage them.
We withheld clauses we had published from this document. Each withholding is recorded in our correction log:
“You can add a not-for-all-audiences tag to your model card metadata. When this tag is present, a message will be displayed on the model page indicating that the model is not for all audiences.”
“For model repos created after August 2024, this is not the case anymore, so you need to set library_name: transformers explicitly.”
“this approach is discouraged, and repo creators should use the explicit library_name as much as possible.”
“If required, you can also specify a custom license by adding other as the license value and specifying the name and a link to the license in the metadata.”
“You can 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.”
“Specifying library_name in the model card (recommended if your model is not a transformers model).”
“The license will be displayed on the model page and users will be able to filter models by license.”
“The model card should describe: the model its intended uses & potential limitations, including biases and ethical considerations”
“the training params and experimental info (you can embed or link to an experiment tracking platform for reference)”
“which datasets were used to train your model”
“the model's evaluation results”
“This tag is also used to determine which widget to use for the model and which APIs to use under the hood.”
Every distinct legal provision identified in this document. Featured provisions appear above with analysis.
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