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: str ignore_metadata_errors: bool = False )

init

< source >

( content: str ignore_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:

>>> 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: 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.

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] = None token: typing.Optional[str] = None ignore_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:

>>> 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: 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

< 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:

>>> 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: str default: typing.Any = None )

Get value for a given metadata key.

pop

< source >

( key: str default: 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: str ignore_metadata_errors: bool = False )

from_template

< source >

( 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.

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:

>>> 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] = None license: typing.Optional[str] = None library_name: typing.Optional[str] = None tags: typing.Optional[typing.List[str]] = None datasets: typing.Optional[typing.List[str]] = None metrics: typing.Optional[typing.List[str]] = None eval_results: typing.Optional[typing.List[huggingface_hub.repocard_data.EvalResult]] = None model_name: typing.Optional[str] = None ignore_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:

>>> 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: str ignore_metadata_errors: bool = False )

from_template

< source >

( 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.

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:

>>> 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] = 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.Optional[typing.List[str]] = 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 ignore_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.
configs (Union[str, List[str]], optional) — A list of the available dataset configs for the dataset.
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.

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: str ignore_metadata_errors: bool = False )

SpaceCardData

class huggingface_hub.SpaceCardData

< source >

( title: typing.Optional[str] = None sdk: typing.Optional[str] = None sdk_version: typing.Optional[str] = None python_version: typing.Optional[str] = None app_file: typing.Optional[str] = None app_port: typing.Optional[int] = None license: typing.Optional[str] = None duplicated_from: typing.Optional[str] = None models: typing.Optional[typing.List[str]] = None datasets: typing.Optional[typing.List[str]] = None tags: typing.Optional[typing.List[str]] = None ignore_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:

>>> 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: 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

< 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:

>>> 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: 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

< source >

( 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

< source >

( 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)

Hub Python Library

Repository Cards

Repo Card

class huggingface_hub.RepoCard

__init__

from_template

load

push_to_hub

save

validate

Card Data

class huggingface_hub.CardData

get

pop

to_dict

to_yaml

Model Cards

ModelCard

class huggingface_hub.ModelCard

from_template

ModelCardData

class huggingface_hub.ModelCardData

Dataset Cards

DatasetCard

class huggingface_hub.DatasetCard

from_template

DatasetCardData

class huggingface_hub.DatasetCardData

Space Cards

SpaceCard

class huggingface_hub.SpaceCard

SpaceCardData

class huggingface_hub.SpaceCardData

Utilities

EvalResult

class huggingface_hub.EvalResult

is_equal_except_value

model_index_to_eval_results

huggingface_hub.repocard_data.model_index_to_eval_results

eval_results_to_model_index

huggingface_hub.repocard_data.eval_results_to_model_index

metadata_eval_result

huggingface_hub.metadata_eval_result

metadata_update

huggingface_hub.metadata_update

init