Repo Cards and Repo Card Data

Repository Cards

The huggingface_hub library provides a Python interface to create, share, and update Model/Dataset Cards. Visit the dedicated documentation page for a deeper view of what Model Cards on the Hub are, and how they work under the hood. You can also check out our Model Cards guide to get a feel for how you would use these utilities in your own projects.

Repo Card

The RepoCard object is the parent class of ModelCard, DatasetCard and SpaceCard.

class huggingface_hub.RepoCard

<source>

( content: strignore_metadata_errors: bool = False )

__init__

<source>

( content: strignore_metadata_errors: bool = False )

Parameters

• content (str) — The content of the Markdown file.

Initialize a RepoCard from string content. The content should be a Markdown file with a YAML block at the beginning and a Markdown body.

Example:

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>>> from huggingface_hub.repocard import RepoCard
>>> text = '''
... ---
... language: en
... license: mit
... ---
...
... # My repo
... '''
>>> card = RepoCard(text)
>>> card.data.to_dict()
{'language': 'en', 'license': 'mit'}
>>> card.text
'\n# My repo\n'

Raises the following error:

• ValueError when the content of the repo card metadata is not a dictionary.

from_template

<source>

( card_data: CardDatatemplate_path: typing.Optional[str] = None**template_kwargs ) → huggingface_hub.repocard.RepoCard

Parameters

• card_data (huggingface_hub.CardData) — A huggingface_hub.CardData instance containing the metadata you want to include in the YAML header of the repo card on the Hugging Face Hub.

• template_path (str, optional) — A path to a markdown file with optional Jinja template variables that can be filled in with template_kwargs. Defaults to the default template.

Returns

huggingface_hub.repocard.RepoCard

A RepoCard instance with the specified card data and content from the template.

Initialize a RepoCard from a template. By default, it uses the default template.

Templates are Jinja2 templates that can be customized by passing keyword arguments.

load

<source>

( repo_id_or_path: typing.Union[str, pathlib.Path]repo_type: typing.Optional[str] = Nonetoken: typing.Optional[str] = Noneignore_metadata_errors: bool = False ) → huggingface_hub.repocard.RepoCard

Parameters

• repo_id_or_path (Union[str, Path]) — The repo ID associated with a Hugging Face Hub repo or a local filepath.

• repo_type (str, optional) — The type of Hugging Face repo to push to. Defaults to None, which will use use “model”. Other options are “dataset” and “space”. Not used when loading from a local filepath. If this is called from a child class, the default value will be the child class’s repo_type.

• token (str, optional) — Authentication token, obtained with huggingface_hub.HfApi.login method. Will default to the stored token.

• ignore_metadata_errors (str) — If True, errors while parsing the metadata section will be ignored. Some information might be lost during the process. Use it at your own risk.

Returns

huggingface_hub.repocard.RepoCard

The RepoCard (or subclass) initialized from the repo’s README.md file or filepath.

Initialize a RepoCard from a Hugging Face Hub repo’s README.md or a local filepath.

Example:

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>>> from huggingface_hub.repocard import RepoCard
>>> card = RepoCard.load("nateraw/food")
>>> assert card.data.tags == ["generated_from_trainer", "image-classification", "pytorch"]

push_to_hub

<source>

( repo_id: strtoken: typing.Optional[str] = Nonerepo_type: typing.Optional[str] = Nonecommit_message: typing.Optional[str] = Nonecommit_description: typing.Optional[str] = Nonerevision: typing.Optional[str] = Nonecreate_pr: typing.Optional[bool] = Noneparent_commit: typing.Optional[str] = None ) → str

Parameters

• repo_id (str) — The repo ID of the Hugging Face Hub repo to push to. Example: “nateraw/food”.

• token (str, optional) — Authentication token, obtained with huggingface_hub.HfApi.login method. Will default to the stored token.

• repo_type (str, optional, defaults to “model”) — The type of Hugging Face repo to push to. Options are “model”, “dataset”, and “space”. If this function is called by a child class, it will default to the child class’s repo_type.

• commit_message (str, optional) — The summary / title / first line of the generated commit.

• commit_description (str, optional) — The description of the generated commit.

• revision (str, optional) — The git revision to commit from. Defaults to the head of the "main" branch.

• create_pr (bool, optional) — Whether or not to create a Pull Request with this commit. Defaults to False.

• parent_commit (str, optional) — The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported. If specified and create_pr is False, the commit will fail if revision does not point to parent_commit. If specified and create_pr is True, the pull request will be created from parent_commit. Specifying parent_commit ensures the repo has not changed before committing the changes, and can be especially useful if the repo is updated / committed to concurrently.

Returns

str

URL of the commit which updated the card metadata.

Push a RepoCard to a Hugging Face Hub repo.

save

<source>

( filepath: typing.Union[pathlib.Path, str] )

Parameters

• filepath (Union[Path, str]) — Filepath to the markdown file to save.

Save a RepoCard to a file.

Example:

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>>> from huggingface_hub.repocard import RepoCard
>>> card = RepoCard("---\nlanguage: en\n---\n# This is a test repo card")
>>> card.save("/tmp/test.md")

validate

<source>

( repo_type: typing.Optional[str] = None )

Parameters

• repo_type (str, optional, defaults to “model”) — The type of Hugging Face repo to push to. Options are “model”, “dataset”, and “space”. If this function is called from a child class, the default will be the child class’s repo_type.

Validates card against Hugging Face Hub’s card validation logic. Using this function requires access to the internet, so it is only called internally by huggingface_hub.repocard.RepoCard.push_to_hub().

Raises the following errors:

• ValueError if the card fails validation checks.
• HTTPError if the request to the Hub API fails for any other reason.

Card Data

The CardData object is the parent class of ModelCardData and DatasetCardData.

class huggingface_hub.CardData

<source>

( ignore_metadata_errors: bool = False**kwargs )

Structure containing metadata from a RepoCard.

CardData is the parent class of ModelCardData and DatasetCardData.

Metadata can be exported as a dictionary or YAML. Export can be customized to alter the representation of the data (example: flatten evaluation results). CardData behaves as a dictionary (can get, pop, set values) but do not inherit from dict to allow this export step.

get

<source>

( key: strdefault: typing.Any = None )

Get value for a given metadata key.

pop

<source>

( key: strdefault: typing.Any = None )

Pop value for a given metadata key.

to_dict

<source>

( ) → dict

Returns

dict

CardData represented as a dictionary ready to be dumped to a YAML block for inclusion in a README.md file.

Converts CardData to a dict.

to_yaml

<source>

( line_break = None ) → str

Parameters

• line_break (str, optional) — The line break to use when dumping to yaml.

Returns

str

CardData represented as a YAML block.

Dumps CardData to a YAML block for inclusion in a README.md file.

Model Cards

ModelCard

class huggingface_hub.ModelCard

<source>

( content: strignore_metadata_errors: bool = False )

from_template

<source>

( card_data: ModelCardDatatemplate_path: typing.Optional[str] = None**template_kwargs ) → huggingface_hub.ModelCard

Parameters

• card_data (huggingface_hub.ModelCardData) — A huggingface_hub.ModelCardData instance containing the metadata you want to include in the YAML header of the model card on the Hugging Face Hub.

• template_path (str, optional) — A path to a markdown file with optional Jinja template variables that can be filled in with template_kwargs. Defaults to the default template.

Returns

huggingface_hub.ModelCard

A ModelCard instance with the specified card data and content from the template.

Initialize a ModelCard from a template. By default, it uses the default template, which can be found here: https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md

Templates are Jinja2 templates that can be customized by passing keyword arguments.

Example:

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>>> from huggingface_hub import ModelCard, ModelCardData, EvalResult

>>> # Using the Default Template
>>> card_data = ModelCardData(
...     language='en',
...     license='mit',
...     library_name='timm',
...     tags=['image-classification', 'resnet'],
...     datasets=['beans'],
...     metrics=['accuracy'],
... )
>>> card = ModelCard.from_template(
...     card_data,
...     model_description='This model does x + y...'
... )

>>> # Including Evaluation Results
>>> card_data = ModelCardData(
...     language='en',
...     tags=['image-classification', 'resnet'],
...     eval_results=[
...         EvalResult(
...             task_type='image-classification',
...             dataset_type='beans',
...             dataset_name='Beans',
...             metric_type='accuracy',
...             metric_value=0.9,
...         ),
...     ],
...     model_name='my-cool-model',
... )
>>> card = ModelCard.from_template(card_data)

>>> # Using a Custom Template
>>> card_data = ModelCardData(
...     language='en',
...     tags=['image-classification', 'resnet']
... )
>>> card = ModelCard.from_template(
...     card_data=card_data,
...     template_path='./src/huggingface_hub/templates/modelcard_template.md',
...     custom_template_var='custom value',  # will be replaced in template if it exists
... )

ModelCardData

class huggingface_hub.ModelCardData

<source>

( language: typing.Union[typing.List[str], str, NoneType] = Nonelicense: typing.Optional[str] = Nonelibrary_name: typing.Optional[str] = Nonetags: typing.Optional[typing.List[str]] = Nonedatasets: typing.Optional[typing.List[str]] = Nonemetrics: typing.Optional[typing.List[str]] = Noneeval_results: typing.Optional[typing.List[huggingface_hub.repocard_data.EvalResult]] = Nonemodel_name: typing.Optional[str] = Noneignore_metadata_errors: bool = False**kwargs )

Parameters

• language (Union[str, List[str]], optional) — Language of model’s training data or metadata. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like “code”, “multilingual”. Defaults to None.

• license (str, optional) — License of this model. Example: apache-2.0 or any license from https://huggingface.co/docs/hub/repositories-licenses. Defaults to None.
• library_name (str, optional) — Name of library used by this model. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts. Defaults to None.
• tags (List[str], optional) — List of tags to add to your model that can be used when filtering on the Hugging Face Hub. Defaults to None.

• datasets (List[str], optional) — List of datasets that were used to train this model. Should be a dataset ID found on https://hf.co/datasets. Defaults to None.
• metrics (List[str], optional) — List of metrics used to evaluate this model. Should be a metric name that can be found at https://hf.co/metrics. Example: ‘accuracy’. Defaults to None.
• eval_results (Union[List[EvalResult], EvalResult], optional) — List of huggingface_hub.EvalResult that define evaluation results of the model. If provided, model_name is used to as a name on PapersWithCode’s leaderboards. Defaults to None.

• model_name (str, optional) — A name for this model. It is used along with eval_results to construct the model-index within the card’s metadata. The name you supply here is what will be used on PapersWithCode’s leaderboards. If None is provided then the repo name is used as a default. Defaults to None.

• ignore_metadata_errors (str) — If True, errors while parsing the metadata section will be ignored. Some information might be lost during the process. Use it at your own risk.

• kwargs (dict, optional) — Additional metadata that will be added to the model card. Defaults to None.

Model Card Metadata that is used by Hugging Face Hub when included at the top of your README.md

Example:

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>>> from huggingface_hub import ModelCardData
>>> card_data = ModelCardData(
...     language="en",
...     license="mit",
...     library_name="timm",
...     tags=['image-classification', 'resnet'],
... )
>>> card_data.to_dict()
{'language': 'en', 'license': 'mit', 'library_name': 'timm', 'tags': ['image-classification', 'resnet']}

Dataset Cards

Dataset cards are also known as Data Cards in the ML Community.

DatasetCard

class huggingface_hub.DatasetCard

<source>

( content: strignore_metadata_errors: bool = False )

from_template

<source>

( card_data: DatasetCardDatatemplate_path: typing.Optional[str] = None**template_kwargs ) → huggingface_hub.DatasetCard

Parameters

• card_data (huggingface_hub.DatasetCardData) — A huggingface_hub.DatasetCardData instance containing the metadata you want to include in the YAML header of the dataset card on the Hugging Face Hub.

• template_path (str, optional) — A path to a markdown file with optional Jinja template variables that can be filled in with template_kwargs. Defaults to the default template.

Returns

huggingface_hub.DatasetCard

A DatasetCard instance with the specified card data and content from the template.

Initialize a DatasetCard from a template. By default, it uses the default template, which can be found here: https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md

Templates are Jinja2 templates that can be customized by passing keyword arguments.

Example:

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>>> from huggingface_hub import DatasetCard, DatasetCardData

>>> # Using the Default Template
>>> card_data = DatasetCardData(
...     language='en',
...     license='mit',
...     annotations_creators='crowdsourced',
...     task_categories=['text-classification'],
...     task_ids=['sentiment-classification', 'text-scoring'],
...     multilinguality='monolingual',
...     pretty_name='My Text Classification Dataset',
... )
>>> card = DatasetCard.from_template(
...     card_data,
...     pretty_name=card_data.pretty_name,
... )

>>> # Using a Custom Template
>>> card_data = DatasetCardData(
...     language='en',
...     license='mit',
... )
>>> card = DatasetCard.from_template(
...     card_data=card_data,
...     template_path='./src/huggingface_hub/templates/datasetcard_template.md',
...     custom_template_var='custom value',  # will be replaced in template if it exists
... )

DatasetCardData

class huggingface_hub.DatasetCardData

<source>

( language: typing.Union[typing.List[str], str, NoneType] = Nonelicense: typing.Union[typing.List[str], str, NoneType] = Noneannotations_creators: typing.Union[typing.List[str], str, NoneType] = Nonelanguage_creators: typing.Union[typing.List[str], str, NoneType] = Nonemultilinguality: typing.Union[typing.List[str], str, NoneType] = Nonesize_categories: typing.Union[typing.List[str], str, NoneType] = Nonesource_datasets: typing.Optional[typing.List[str]] = Nonetask_categories: typing.Union[typing.List[str], str, NoneType] = Nonetask_ids: typing.Union[typing.List[str], str, NoneType] = Nonepaperswithcode_id: typing.Optional[str] = Nonepretty_name: typing.Optional[str] = Nonetrain_eval_index: typing.Optional[typing.Dict] = Noneconfig_names: typing.Union[typing.List[str], str, NoneType] = Noneignore_metadata_errors: bool = False**kwargs )

Parameters

• language (List[str], optional) — Language of dataset’s data or metadata. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like “code”, “multilingual”.

• license (Union[str, List[str]], optional) — License(s) of this dataset. Example: apache-2.0 or any license from https://huggingface.co/docs/hub/repositories-licenses.
• annotations_creators (Union[str, List[str]], optional) — How the annotations for the dataset were created. Options are: ‘found’, ‘crowdsourced’, ‘expert-generated’, ‘machine-generated’, ‘no-annotation’, ‘other’.

• language_creators (Union[str, List[str]], optional) — How the text-based data in the dataset was created. Options are: ‘found’, ‘crowdsourced’, ‘expert-generated’, ‘machine-generated’, ‘other’

• multilinguality (Union[str, List[str]], optional) — Whether the dataset is multilingual. Options are: ‘monolingual’, ‘multilingual’, ‘translation’, ‘other’.

• size_categories (Union[str, List[str]], optional) — The number of examples in the dataset. Options are: ‘n<1K’, ‘1K1T’, and ‘other’.

• source_datasets (List[str]], optional) — Indicates whether the dataset is an original dataset or extended from another existing dataset. Options are: ‘original’ and ‘extended’.

• task_categories (Union[str, List[str]], optional) — What categories of task does the dataset support?

• task_ids (Union[str, List[str]], optional) — What specific tasks does the dataset support?

• paperswithcode_id (str, optional) — ID of the dataset on PapersWithCode.

• pretty_name (str, optional) — A more human-readable name for the dataset. (ex. “Cats vs. Dogs”)

• train_eval_index (Dict, optional) — A dictionary that describes the necessary spec for doing evaluation on the Hub. If not provided, it will be gathered from the ‘train-eval-index’ key of the kwargs.

• config_names (Union[str, List[str]], optional) — A list of the available dataset configs for the dataset.

Dataset Card Metadata that is used by Hugging Face Hub when included at the top of your README.md

Space Cards

SpaceCard

class huggingface_hub.SpaceCard

<source>

( content: strignore_metadata_errors: bool = False )

SpaceCardData

class huggingface_hub.SpaceCardData

<source>

( title: typing.Optional[str] = Nonesdk: typing.Optional[str] = Nonesdk_version: typing.Optional[str] = Nonepython_version: typing.Optional[str] = Noneapp_file: typing.Optional[str] = Noneapp_port: typing.Optional[int] = Nonelicense: typing.Optional[str] = Noneduplicated_from: typing.Optional[str] = Nonemodels: typing.Optional[typing.List[str]] = Nonedatasets: typing.Optional[typing.List[str]] = Nonetags: typing.Optional[typing.List[str]] = Noneignore_metadata_errors: bool = False**kwargs )

Parameters

• title (str, optional) — Title of the Space.

• sdk (str, optional) — SDK of the Space (one of gradio, streamlit, docker, or static).

• sdk_version (str, optional) — Version of the used SDK (if Gradio/Streamlit sdk).

• python_version (str, optional) — Python version used in the Space (if Gradio/Streamlit sdk).

• app_file (str, optional) — Path to your main application file (which contains either gradio or streamlit Python code, or static html code). Path is relative to the root of the repository.

• app_port (str, optional) — Port on which your application is running. Used only if sdk is docker.

• license (str, optional) — License of this model. Example: apache-2.0 or any license from https://huggingface.co/docs/hub/repositories-licenses.
• duplicated_from (str, optional) — ID of the original Space if this is a duplicated Space.

• models (Liststr, optional) — List of models related to this Space. Should be a dataset ID found on https://hf.co/models.
• datasets (List[str], optional) — List of datasets related to this Space. Should be a dataset ID found on https://hf.co/datasets.
• tags (List[str], optional) — List of tags to add to your Space that can be used when filtering on the Hub.

• ignore_metadata_errors (str) — If True, errors while parsing the metadata section will be ignored. Some information might be lost during the process. Use it at your own risk.

• kwargs (dict, optional) — Additional metadata that will be added to the space card.

Space Card Metadata that is used by Hugging Face Hub when included at the top of your README.md

To get an exhaustive reference of Spaces configuration, please visit https://huggingface.co/docs/hub/spaces-config-reference#spaces-configuration-reference.

Example:

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>>> from huggingface_hub import SpaceCardData
>>> card_data = SpaceCardData(
...     title="Dreambooth Training",
...     license="mit",
...     sdk="gradio",
...     duplicated_from="multimodalart/dreambooth-training"
... )
>>> card_data.to_dict()
{'title': 'Dreambooth Training', 'sdk': 'gradio', 'license': 'mit', 'duplicated_from': 'multimodalart/dreambooth-training'}

Utilities

EvalResult

class huggingface_hub.EvalResult

<source>

( task_type: strdataset_type: strdataset_name: strmetric_type: strmetric_value: typing.Anytask_name: typing.Optional[str] = Nonedataset_config: typing.Optional[str] = Nonedataset_split: typing.Optional[str] = Nonedataset_revision: typing.Optional[str] = Nonedataset_args: typing.Union[typing.Dict[str, typing.Any], NoneType] = Nonemetric_name: typing.Optional[str] = Nonemetric_config: typing.Optional[str] = Nonemetric_args: typing.Union[typing.Dict[str, typing.Any], NoneType] = Noneverified: typing.Optional[bool] = Noneverify_token: typing.Optional[str] = None )

Parameters

• task_type (str) — The task identifier. Example: “image-classification”.

• dataset_type (str) — The dataset identifier. Example: “common_voice”. Use dataset id from https://hf.co/datasets.
• dataset_name (str) — A pretty name for the dataset. Example: “Common Voice (French)“.

• metric_type (str) — The metric identifier. Example: “wer”. Use metric id from https://hf.co/metrics.
• metric_value (Any) — The metric value. Example: 0.9 or “20.0 ± 1.2”.

• task_name (str, optional) — A pretty name for the task. Example: “Speech Recognition”.

• dataset_config (str, optional) — The name of the dataset configuration used in load_dataset(). Example: fr in load_dataset("common_voice", "fr"). See the datasets docs for more info: https://hf.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
• dataset_split (str, optional) — The split used in load_dataset(). Example: “test”.

• dataset_revision (str, optional) — The revision (AKA Git Sha) of the dataset used in load_dataset(). Example: 5503434ddd753f426f4b38109466949a1217c2bb

• dataset_args (Dict[str, Any], optional) — The arguments passed during Metric.compute(). Example for bleu: {"max_order": 4}

• metric_name (str, optional) — A pretty name for the metric. Example: “Test WER”.

• metric_config (str, optional) — The name of the metric configuration used in load_metric(). Example: bleurt-large-512 in load_metric("bleurt", "bleurt-large-512"). See the datasets docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
• metric_args (Dict[str, Any], optional) — The arguments passed during Metric.compute(). Example for bleu: max_order: 4

• verified (bool, optional) — Indicates whether the metrics originate from Hugging Face’s evaluation service or not. Automatically computed by Hugging Face, do not set.
• verify_token (str, optional) — A JSON Web Token that is used to verify whether the metrics originate from Hugging Face’s evaluation service or not.

Flattened representation of individual evaluation results found in model-index of Model Cards.

For more information on the model-index spec, see https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1.

is_equal_except_value

<source>

( other: EvalResult )

Return True if self and other describe exactly the same metric but with a different value.

model_index_to_eval_results

huggingface_hub.repocard_data.model_index_to_eval_results

<source>

( model_index: typing.List[typing.Dict[str, typing.Any]] ) → model_name (str)

Parameters

• model_index (List[Dict[str, Any]]) — A model index data structure, likely coming from a README.md file on the Hugging Face Hub.

Returns

model_name (str)

The name of the model as found in the model index. This is used as the identifier for the model on leaderboards like PapersWithCode. eval_results (List[EvalResult]): A list of huggingface_hub.EvalResult objects containing the metrics reported in the provided model_index.

Takes in a model index and returns the model name and a list of huggingface_hub.EvalResult objects.

A detailed spec of the model index can be found here: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1

Example:

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>>> from huggingface_hub.repocard_data import model_index_to_eval_results
>>> # Define a minimal model index
>>> model_index = [
...     {
...         "name": "my-cool-model",
...         "results": [
...             {
...                 "task": {
...                     "type": "image-classification"
...                 },
...                 "dataset": {
...                     "type": "beans",
...                     "name": "Beans"
...                 },
...                 "metrics": [
...                     {
...                         "type": "accuracy",
...                         "value": 0.9
...                     }
...                 ]
...             }
...         ]
...     }
... ]
>>> model_name, eval_results = model_index_to_eval_results(model_index)
>>> model_name
'my-cool-model'
>>> eval_results[0].task_type
'image-classification'
>>> eval_results[0].metric_type
'accuracy'

eval_results_to_model_index

huggingface_hub.repocard_data.eval_results_to_model_index

<source>

( model_name: streval_results: typing.List[huggingface_hub.repocard_data.EvalResult] ) → model_index (List[Dict[str, Any]])

Parameters

• model_name (str) — Name of the model (ex. “my-cool-model”). This is used as the identifier for the model on leaderboards like PapersWithCode.

• eval_results (List[EvalResult]) — List of huggingface_hub.EvalResult objects containing the metrics to be reported in the model-index.

Returns

model_index (List[Dict[str, Any]])

The eval_results converted to a model-index.

Takes in given model name and list of huggingface_hub.EvalResult and returns a valid model-index that will be compatible with the format expected by the Hugging Face Hub.

Example:

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>>> from huggingface_hub.repocard_data import eval_results_to_model_index, EvalResult
>>> # Define minimal eval_results
>>> eval_results = [
...     EvalResult(
...         task_type="image-classification",  # Required
...         dataset_type="beans",  # Required
...         dataset_name="Beans",  # Required
...         metric_type="accuracy",  # Required
...         metric_value=0.9,  # Required
...     )
... ]
>>> eval_results_to_model_index("my-cool-model", eval_results)
[{'name': 'my-cool-model', 'results': [{'task': {'type': 'image-classification'}, 'dataset': {'name': 'Beans', 'type': 'beans'}, 'metrics': [{'type': 'accuracy', 'value': 0.9}]}]}]

metadata_eval_result

huggingface_hub.metadata_eval_result

<source>

( model_pretty_name: strtask_pretty_name: strtask_id: strmetrics_pretty_name: strmetrics_id: strmetrics_value: typing.Anydataset_pretty_name: strdataset_id: strmetrics_config: typing.Optional[str] = Nonemetrics_verified: bool = Falsedataset_config: typing.Optional[str] = Nonedataset_split: typing.Optional[str] = Nonedataset_revision: typing.Optional[str] = Nonemetrics_verification_token: typing.Optional[str] = None ) → dict

Parameters

• model_pretty_name (str) — The name of the model in natural language.

• task_pretty_name (str) — The name of a task in natural language.

• task_id (str) — Example: automatic-speech-recognition. A task id.

• metrics_pretty_name (str) — A name for the metric in natural language. Example: Test WER.

• metrics_id (str) — Example: wer. A metric id from https://hf.co/metrics.
• metrics_value (Any) — The value from the metric. Example: 20.0 or “20.0 ± 1.2”.

• dataset_pretty_name (str) — The name of the dataset in natural language.

• dataset_id (str) — Example: common_voice. A dataset id from https://hf.co/datasets.
• metrics_config (str, optional) — The name of the metric configuration used in load_metric(). Example: bleurt-large-512 in load_metric("bleurt", "bleurt-large-512").

• metrics_verified (bool, optional, defaults to False) — Indicates whether the metrics originate from Hugging Face’s evaluation service or not. Automatically computed by Hugging Face, do not set.
• dataset_config (str, optional) — Example: fr. The name of the dataset configuration used in load_dataset().

• dataset_split (str, optional) — Example: test. The name of the dataset split used in load_dataset().

• dataset_revision (str, optional) — Example: 5503434ddd753f426f4b38109466949a1217c2bb. The name of the dataset dataset revision used in load_dataset().

• metrics_verification_token (bool, optional) — A JSON Web Token that is used to verify whether the metrics originate from Hugging Face’s evaluation service or not.

Returns

dict

a metadata dict with the result from a model evaluated on a dataset.

Creates a metadata dict with the result from a model evaluated on a dataset.

Example:

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>>> from huggingface_hub import metadata_eval_result
>>> results = metadata_eval_result(
...         model_pretty_name="RoBERTa fine-tuned on ReactionGIF",
...         task_pretty_name="Text Classification",
...         task_id="text-classification",
...         metrics_pretty_name="Accuracy",
...         metrics_id="accuracy",
...         metrics_value=0.2662102282047272,
...         dataset_pretty_name="ReactionJPEG",
...         dataset_id="julien-c/reactionjpeg",
...         dataset_config="default",
...         dataset_split="test",
... )
>>> results == {
...     'model-index': [
...         {
...             'name': 'RoBERTa fine-tuned on ReactionGIF',
...             'results': [
...                 {
...                     'task': {
...                         'type': 'text-classification',
...                         'name': 'Text Classification'
...                     },
...                     'dataset': {
...                         'name': 'ReactionJPEG',
...                         'type': 'julien-c/reactionjpeg',
...                         'config': 'default',
...                         'split': 'test'
...                     },
...                     'metrics': [
...                         {
...                             'type': 'accuracy',
...                             'value': 0.2662102282047272,
...                             'name': 'Accuracy',
...                             'verified': False
...                         }
...                     ]
...                 }
...             ]
...         }
...     ]
... }
True

metadata_update

huggingface_hub.metadata_update

<source>

( repo_id: strmetadata: typing.Dictrepo_type: typing.Optional[str] = Noneoverwrite: bool = Falsetoken: typing.Optional[str] = Nonecommit_message: typing.Optional[str] = Nonecommit_description: typing.Optional[str] = Nonerevision: typing.Optional[str] = Nonecreate_pr: bool = Falseparent_commit: typing.Optional[str] = None ) → str

Parameters

• repo_id (str) — The name of the repository.

• metadata (dict) — A dictionary containing the metadata to be updated.

• repo_type (str, optional) — Set to "dataset" or "space" if updating to a dataset or space, None or "model" if updating to a model. Default is None.

• overwrite (bool, optional, defaults to False) — If set to True an existing field can be overwritten, otherwise attempting to overwrite an existing field will cause an error.

• token (str, optional) — The Hugging Face authentication token.

• commit_message (str, optional) — The summary / title / first line of the generated commit. Defaults to f"Update metadata with huggingface_hub"

• commit_description (str optional) — The description of the generated commit

• revision (str, optional) — The git revision to commit from. Defaults to the head of the "main" branch.

• create_pr (boolean, optional) — Whether or not to create a Pull Request from revision with that commit. Defaults to False.

• parent_commit (str, optional) — The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported. If specified and create_pr is False, the commit will fail if revision does not point to parent_commit. If specified and create_pr is True, the pull request will be created from parent_commit. Specifying parent_commit ensures the repo has not changed before committing the changes, and can be especially useful if the repo is updated / committed to concurrently.

Returns

str

URL of the commit which updated the card metadata.

Updates the metadata in the README.md of a repository on the Hugging Face Hub. If the README.md file doesn’t exist yet, a new one is created with metadata and an the default ModelCard or DatasetCard template. For space repo, an error is thrown as a Space cannot exist without a README.md file.

Example:

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>>> from huggingface_hub import metadata_update
>>> metadata = {'model-index': [{'name': 'RoBERTa fine-tuned on ReactionGIF',
...             'results': [{'dataset': {'name': 'ReactionGIF',
...                                      'type': 'julien-c/reactiongif'},
...                           'metrics': [{'name': 'Recall',
...                                        'type': 'recall',
...                                        'value': 0.7762102282047272}],
...                          'task': {'name': 'Text Classification',
...                                   'type': 'text-classification'}}]}]}
>>> url = metadata_update("hf-internal-testing/reactiongif-roberta-card", metadata)