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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 and DatasetCard.

class huggingface_hub.repocard.RepoCard

< >

( content: str )

__init__

< >

( content: str )

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:

>>> 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

< >

( card_data: CardData template_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.

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

< >

( repo_id_or_path: typing.Union[str, pathlib.Path] repo_type: typing.Optional[str] = None token: typing.Optional[str] = None ) 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.

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:

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

push_to_hub

< >

( repo_id: str token: typing.Optional[str] = None repo_type: typing.Optional[str] = None commit_message: typing.Optional[str] = None commit_description: typing.Optional[str] = None revision: typing.Optional[str] = None create_pr: typing.Optional[bool] = None parent_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

< >

( 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:

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

validate

< >

( 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

< >

( **kwargs )

to_dict

< >

( ) 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

< >

( 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

< >

( content: str )

from_template

< >

( card_data: ModelCardData template_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.

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:

>>> 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

< >

( language: typing.Union[typing.List[str], str, NoneType] = None license: typing.Optional[str] = None library_name: typing.Optional[str] = None tags: typing.Optional[typing.List[str]] = None datasets: typing.Union[typing.List[str], str, NoneType] = None metrics: typing.Union[typing.List[str], str, NoneType] = None eval_results: typing.Optional[typing.List[huggingface_hub.repocard_data.EvalResult]] = None model_name: typing.Optional[str] = None **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 (Union[str, List[str]], optional) — Dataset or 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 (Union[str, 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.
  • 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:

>>> 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

< >

( content: str )

from_template

< >

( card_data: DatasetCardData template_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.

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:

>>> 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

< >

( language: typing.Union[typing.List[str], str, NoneType] = None license: typing.Union[typing.List[str], str, NoneType] = None annotations_creators: typing.Union[typing.List[str], str, NoneType] = None language_creators: typing.Union[typing.List[str], str, NoneType] = None multilinguality: typing.Union[typing.List[str], str, NoneType] = None size_categories: typing.Union[typing.List[str], str, NoneType] = None source_datasets: typing.Union[typing.List[str], str, NoneType] = None task_categories: typing.Union[typing.List[str], str, NoneType] = None task_ids: typing.Union[typing.List[str], str, NoneType] = None paperswithcode_id: typing.Optional[str] = None pretty_name: typing.Optional[str] = None train_eval_index: typing.Optional[typing.Dict] = None configs: typing.Union[typing.List[str], str, NoneType] = None **kwargs )

Parameters

  • language (Union[str, 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 (Union[str, 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.
  • configs (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

Utilities

EvalResult

class huggingface_hub.EvalResult

< >

( task_type: str dataset_type: str dataset_name: str metric_type: str metric_value: typing.Any task_name: typing.Optional[str] = None dataset_config: typing.Optional[str] = None dataset_split: typing.Optional[str] = None dataset_revision: typing.Optional[str] = None dataset_args: typing.Union[typing.Dict[str, typing.Any], NoneType] = None metric_name: typing.Optional[str] = None metric_config: typing.Optional[str] = None metric_args: typing.Union[typing.Dict[str, typing.Any], NoneType] = None verified: typing.Optional[bool] = None verify_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

< >

( 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

< >

( 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:

>>> 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

< >

( model_name: str eval_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:

>>> 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

< >

( model_pretty_name: str task_pretty_name: str task_id: str metrics_pretty_name: str metrics_id: str metrics_value: typing.Any dataset_pretty_name: str dataset_id: str metrics_config: typing.Optional[str] = None metrics_verified: bool = False dataset_config: typing.Optional[str] = None dataset_split: typing.Optional[str] = None dataset_revision: typing.Optional[str] = None metrics_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:

>>> 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

< >

( repo_id: str metadata: typing.Dict repo_type: typing.Optional[str] = None overwrite: bool = False token: typing.Optional[str] = None commit_message: typing.Optional[str] = None commit_description: typing.Optional[str] = None revision: typing.Optional[str] = None create_pr: bool = False parent_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:

>>> 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)