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리포지토리 카드

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리포지토리 카드

huggingface_hub 라이브러리는 모델/데이터 세트 카드를 생성, 공유 및 업데이트하기 위한 Python 인터페이스를 제공합니다. Hub의 모델 카드가 무엇이며 내부적으로 어떻게 작동하는지 더 깊이 있게 알아보려면 전용 문서 페이지를 방문하세요. 또한 이러한 유틸리티를 자신의 프로젝트에서 어떻게 사용할 수 있는지 감을 잡기 위해 모델 카드 가이드를 확인할 수 있습니다.

리포지토리 카드

RepoCard 객체는 ModelCard, DatasetCard 및 SpaceCard의 상위 클래스입니다.

class huggingface_hub.RepoCard

< >

( content: str ignore_metadata_errors: bool = False )

__init__

< >

( 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

< >

( card_data: CardData template_path: Optional = None template_str: Optional = 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

< >

( repo_id_or_path: Union repo_type: Optional = None token: Optional = 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

< >

( repo_id: str token: Optional = None repo_type: Optional = None commit_message: Optional = None commit_description: Optional = None revision: Optional = None create_pr: Optional = None parent_commit: Optional = 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: Union )

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: Optional = 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.

카드 데이터

CardData 객체는 ModelCardData와 DatasetCardData의 상위 클래스입니다.

class huggingface_hub.CardData

< >

( 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

< >

( key: str default: Any = None )

Get value for a given metadata key.

pop

< >

( key: str default: Any = None )

Pop value for a given metadata key.

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 original_order: Optional = 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.

모델 카드

ModelCard

class huggingface_hub.ModelCard

< >

( content: str ignore_metadata_errors: bool = False )

from_template

< >

( card_data: ModelCardData template_path: Optional = None template_str: Optional = 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

< >

( base_model: Union = None datasets: Optional = None eval_results: Optional = None language: Union = None library_name: Optional = None license: Optional = None license_name: Optional = None license_link: Optional = None metrics: Optional = None model_name: Optional = None pipeline_tag: Optional = None tags: Optional = None ignore_metadata_errors: bool = False **kwargs )

Parameters

  • base_model (str or List[str], optional) — The identifier of the base model from which the model derives. This is applicable for example if your model is a fine-tune or adapter of an existing model. The value must be the ID of a model on the Hub (or a list of IDs if your model derives from multiple models). 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.
  • 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.
  • 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.
  • library_name (str, optional) — Name of library used by this model. Example: keras or any library from https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries.ts. 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.
  • license_name (str, optional) — Name of the license of this model. Defaults to None. To be used in conjunction with license_link. Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a name. In that case, use license instead.
  • license_link (str, optional) — Link to the license of this model. Defaults to None. To be used in conjunction with license_name. Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a link. In that case, use license instead.
  • 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.
  • 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.
  • pipeline_tag (str, optional) — The pipeline tag associated with the model. Example: “text-classification”.
  • 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.
  • 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']}

데이터 세트 카드

ML 커뮤니티에서는 데이터 세트 카드를 데이터 카드라고도 합니다.

DatasetCard

class huggingface_hub.DatasetCard

< >

( content: str ignore_metadata_errors: bool = False )

from_template

< >

( card_data: DatasetCardData template_path: Optional = None template_str: Optional = 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

< >

( language: Union = None license: Union = None annotations_creators: Union = None language_creators: Union = None multilinguality: Union = None size_categories: Union = None source_datasets: Optional = None task_categories: Union = None task_ids: Union = None paperswithcode_id: Optional = None pretty_name: Optional = None train_eval_index: Optional = None config_names: Union = 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.
  • 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

공간 카드

SpaceCard

class huggingface_hub.SpaceCard

< >

( content: str ignore_metadata_errors: bool = False )

SpaceCardData

class huggingface_hub.SpaceCardData

< >

( title: Optional = None sdk: Optional = None sdk_version: Optional = None python_version: Optional = None app_file: Optional = None app_port: Optional = None license: Optional = None duplicated_from: Optional = None models: Optional = None datasets: Optional = None tags: Optional = 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'}

유틸리티

EvalResult

class huggingface_hub.EvalResult

< >

( task_type: str dataset_type: str dataset_name: str metric_type: str metric_value: Any task_name: Optional = None dataset_config: Optional = None dataset_split: Optional = None dataset_revision: Optional = None dataset_args: Optional = None metric_name: Optional = None metric_config: Optional = None metric_args: Optional = None verified: Optional = None verify_token: Optional = None source_name: Optional = None source_url: Optional = 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.
  • source_name (str, optional) — The name of the source of the evaluation result. Example: “Open LLM Leaderboard”.
  • source_url (str, optional) — The URL of the source of the evaluation result. Example: ”https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard”.

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: List ) → 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: List ) → 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: Any dataset_pretty_name: str dataset_id: str metrics_config: Optional = None metrics_verified: bool = False dataset_config: Optional = None dataset_split: Optional = None dataset_revision: Optional = None metrics_verification_token: Optional = 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: Dict repo_type: Optional = None overwrite: bool = False token: Optional = None commit_message: Optional = None commit_description: Optional = None revision: Optional = None create_pr: bool = False parent_commit: Optional = 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)
< > Update on GitHub

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