huggingface_hub/pr_4113/ko/package_reference/cards.md

# 리포지토리 카드[[repository-cards]]

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 페이지](https://huggingface.co/docs/hub/models-cards)를 방문하세요. 또한 이러한 유틸리티를 자신의 프로젝트에서 어떻게 사용할 수 있는지 감을 잡기 위해 [모델 카드 가이드](../how-to-model-cards)를 확인할 수 있습니다.

## 리포지토리 카드[[huggingface_hub.RepoCard]][[huggingface_hub.RepoCard]]

`RepoCard` 객체는 [ModelCard](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.ModelCard), [DatasetCard](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.DatasetCard) 및 `SpaceCard`의 상위 클래스입니다.

#### huggingface_hub.RepoCard[[huggingface_hub.RepoCard]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L37)

__init__huggingface_hub.RepoCard.__init__https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L42[{"name": "content", "val": ": str"}, {"name": "ignore_metadata_errors", "val": ": bool = False"}]- **content** (`str`) -- The content of the Markdown file.0
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:
```python
>>> 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'

```

> [!TIP]
> Raises the following error:
>
>     - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
>       when the content of the repo card metadata is not a dictionary.

**Parameters:**

content (`str`) : The content of the Markdown file.
#### from_template[[huggingface_hub.RepoCard.from_template]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L289)

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.

**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](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.RepoCard)`

A RepoCard instance with the specified card data and content from the
template.
#### load[[huggingface_hub.RepoCard.load]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L135)

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

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

```

**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 "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](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.RepoCard)`

The RepoCard (or subclass) initialized from the repo's
README.md file or filepath.
#### push_to_hub[[huggingface_hub.RepoCard.push_to_hub]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L226)

Push a RepoCard to a Hugging Face Hub repo.

**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 too concurrently.

**Returns:**

``str``

URL of the commit which updated the card metadata.
#### save[[huggingface_hub.RepoCard.save]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L115)

Save a RepoCard to a file.

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

```

**Parameters:**

filepath (`Union[Path, str]`) : Filepath to the markdown file to save.
#### validate[[huggingface_hub.RepoCard.validate]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L189)

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()](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.RepoCard.push_to_hub).

> [!TIP]
> Raises the following errors:
>
>     - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
>       if the card fails validation checks.
>     - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
>       if the request to the Hub API fails for any other reason.

**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`.

## 카드 데이터[[huggingface_hub.CardData]][[huggingface_hub.CardData]]

[CardData](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.CardData) 객체는 [ModelCardData](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.ModelCardData)와 [DatasetCardData](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.DatasetCardData)의 상위 클래스입니다.

#### huggingface_hub.CardData[[huggingface_hub.CardData]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L165)

Structure containing metadata from a RepoCard.

[CardData](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.CardData) is the parent class of [ModelCardData](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.ModelCardData) and [DatasetCardData](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.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.

gethuggingface_hub.CardData.gethttps://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L222[{"name": "key", "val": ": str"}, {"name": "default", "val": ": typing.Any = None"}]
Get value for a given metadata key.
#### pop[[huggingface_hub.CardData.pop]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L227)

Pop value for a given metadata key.
#### to_dict[[huggingface_hub.CardData.to_dict]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L178)

Converts CardData to a dict.

**Returns:**

``dict``

CardData represented as a dictionary ready to be dumped to a YAML
block for inclusion in a README.md file.
#### to_yaml[[huggingface_hub.CardData.to_yaml]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L198)

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

**Parameters:**

line_break (str, *optional*) : The line break to use when dumping to yaml.

**Returns:**

``str``

CardData represented as a YAML block.

## 모델 카드[[model-cards]]

### ModelCard[[huggingface_hub.ModelCard]][[huggingface_hub.ModelCard]]

#### huggingface_hub.ModelCard[[huggingface_hub.ModelCard]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L333)

from_templatehuggingface_hub.ModelCard.from_templatehttps://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L338[{"name": "card_data", "val": ": ModelCardData"}, {"name": "template_path", "val": ": str | None = None"}, {"name": "template_str", "val": ": str | None = None"}, {"name": "**template_kwargs", "val": ""}]- **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.0[huggingface_hub.ModelCard](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#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:
```python
>>> 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
... )

```

**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](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.ModelCard)`

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

### ModelCardData[[huggingface_hub.ModelCardData]][[huggingface_hub.ModelCardData]]

#### huggingface_hub.ModelCardData[[huggingface_hub.ModelCardData]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L265)

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

Example:
```python
>>> 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']}

```

**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 (`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.

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.

## 데이터 세트 카드[[cards#dataset-cards]]

ML 커뮤니티에서는 데이터 세트 카드를 데이터 카드라고도 합니다.

### DatasetCard[[huggingface_hub.DatasetCard]][[huggingface_hub.DatasetCard]]

#### huggingface_hub.DatasetCard[[huggingface_hub.DatasetCard]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L414)

from_templatehuggingface_hub.DatasetCard.from_templatehttps://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L419[{"name": "card_data", "val": ": DatasetCardData"}, {"name": "template_path", "val": ": str | None = None"}, {"name": "template_str", "val": ": str | None = None"}, {"name": "**template_kwargs", "val": ""}]- **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.0[huggingface_hub.DatasetCard](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#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:
```python
>>> 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
... )

```

**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](/docs/huggingface_hub/pr_4113/ko/package_reference/cards#huggingface_hub.DatasetCard)`

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

### DatasetCardData[[huggingface_hub.DatasetCardData]][[huggingface_hub.DatasetCardData]]

#### huggingface_hub.DatasetCardData[[huggingface_hub.DatasetCardData]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L394)

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

**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: 'n1T', 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.

## 공간 카드[[space-cards]]

### SpaceCard[[huggingface_hub.SpaceCardData]][[huggingface_hub.SpaceCard]]

#### huggingface_hub.SpaceCard[[huggingface_hub.SpaceCard]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L479)

### SpaceCardData[[huggingface_hub.SpaceCardData]][[huggingface_hub.SpaceCardData]]

#### huggingface_hub.SpaceCardData[[huggingface_hub.SpaceCardData]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L474)

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:
```python
>>> 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'}
```

**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 (list`str`, *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.

## 유틸리티[[utilities]]

### EvalResult[[huggingface_hub.EvalResult]][[huggingface_hub.EvalResult]]

#### huggingface_hub.EvalResult[[huggingface_hub.EvalResult]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L13)

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_valuehuggingface_hub.EvalResult.is_equal_except_valuehttps://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L145[{"name": "other", "val": ": EvalResult"}]

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

**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](https://huggingface.co/spaces/autoevaluate/model-evaluator) 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](https://huggingface.co/spaces/autoevaluate/model-evaluator) 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".

### model_index_to_eval_results[[huggingface_hub.repocard_data.model_index_to_eval_results]][[huggingface_hub.repocard_data.model_index_to_eval_results]]

#### huggingface_hub.repocard_data.model_index_to_eval_results[[huggingface_hub.repocard_data.model_index_to_eval_results]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L555)

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:
```python
>>> 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'

```

**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.

### eval_results_to_model_index[[huggingface_hub.repocard_data.eval_results_to_model_index]][[huggingface_hub.repocard_data.eval_results_to_model_index]]

#### huggingface_hub.repocard_data.eval_results_to_model_index[[huggingface_hub.repocard_data.eval_results_to_model_index]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard_data.py#L671)

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:
```python
>>> 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}]}]}]

```

**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.

### metadata_eval_result[[huggingface_hub.metadata_eval_result]][[huggingface_hub.metadata_eval_result]]

#### huggingface_hub.metadata_eval_result[[huggingface_hub.metadata_eval_result]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L551)

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

Example:
```python
>>> 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

```

**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](https://huggingface.co/spaces/autoevaluate/model-evaluator) 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](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.

**Returns:**

``dict``

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

### metadata_update[[huggingface_hub.metadata_update]][[huggingface_hub.metadata_update]]

#### huggingface_hub.metadata_update[[huggingface_hub.metadata_update]]

[Source](https://github.com/huggingface/huggingface_hub/blob/vr_4113/src/huggingface_hub/repocard.py#L679)

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
the default ModelCard or DatasetCard template. For `space` repo, an error is thrown
as a Space cannot exist without a `README.md` file.

Example:
```python
>>> 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)

```

**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 too concurrently.

**Returns:**

``str``

URL of the commit which updated the card metadata.