+ U\i".^RIt^RIHt^RIHt^RIHtHtHtH t H t H t ^RI H t Ht] P!]4t]!RR44t]!RR 44tR R lt!R R ]4t!RR]4t!RR]4tRRltRtRRltRRltR#)N) defaultdict) dataclass)AnyDictListOptionalTupleUnion)logging yaml_dumpca]tRt^ toRtRtRtRtRtRt Rt Rt Rt Rt RtRtRt]V3RlRl4tV3RlRltV3RlRltV3R ltR tVtR#) EvalResultuC 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. Args: 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". Nc <V^8dQhRS[/#return)tuple)format __classdict__s"u/Users/agent/.cache/uv/archive-v0/6ZL4-8mCcRNgGE9wi6M6J/lib/python3.14/site-packages/huggingface_hub/repocard_data.py __annotate__EvalResult.__annotate__s  5 c tVPVPVPVPVP3#)z9Returns a tuple that uniquely identifies this evaluation.) task_type dataset_typedataset_config dataset_splitdataset_revisionselfs&runique_identifierEvalResult.unique_identifiers9 NN           ! !   rc$<V^8dQhRRRS[/#)rotherrrbool)rrs"rrrs  < D rc VPP4F3wr#VR8XdKVR8wgK\W4\W48wgK2R# R#)z` Return True if `self` and `other` describe exactly the same metric but with a different value. metric_value verify_tokenFT)__dict__itemsgetattr)r!r%key_s&& ris_equal_except_value EvalResult.is_equal_except_valuesK mm))+FCn$n$);wu?R)R ,rc<V^8dQhRR/#)rrN)rrs"rrrsbbtbrcXVPeVPf \R4hR#R#)NzAIf `source_name` is provided, `source_url` must also be provided.) source_name source_url ValueErrorr s&r __post_init__EvalResult.__post_init__s,    'DOO,C`a a-D 'rc<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[S[,;R&S[S[,;R&S[S[,;R &S[S[S[S[3,,;R &S[S[,;R &S[S[,;R &S[S[S[S[3,,;R &S[S[,;R&S[S[,;R&S[S[,;R&S[S[,;R&#)rrr dataset_name metric_typer) task_namerrr dataset_args metric_name metric_config metric_argsverifiedr*r5r6)strrrrr')rrs"rrr sEjNkrsz{BCJKV}#WbSM(cjC='krsm*sz4S>*1{B#%CLC='MT$sCx.)0UZtn#[`3-&ah#%ip $qrr3)__name__ __module__ __qualname____firstlineno____doc__r=rrrr>r?r@rArBr*r5r6propertyr"r0r8__annotate_func____static_attributes____classdictcell__rs@rrr s-R $I %)N$(M'+.2L"&K $(M-1K $H#'L"&K!%J     bbgrrca]tRt^toRtRV3RlRlltRtRtRV3RlRlltR t R t RV3R lR llt RV3R lRllt V3RlRlt V3RlRltV3RlRltV3RlRltRtVtR#)CardDataaStructure containing metadata from a RepoCard. [`CardData`] is the parent class of [`ModelCardData`] and [`DatasetCardData`]. Metadata can be exported as a dictionary or YAML. Export can be customized to alter the representation of the data (example: flatten evaluation results). `CardData` behaves as a dictionary (can get, pop, set values) but do not inherit from `dict` to allow this export step. c <V^8dQhRS[/#)rignore_metadata_errorsr&)rrs"rrCardData.__annotate__s%%t%rc <VPPV4R#N)r+update)r!rQkwargss&&,r__init__CardData.__init__s V$rc \P!VP4pVPV4VP 4UUu/uF wr#VfK W#bK upp#uuppi)zConverts 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. )copydeepcopyr+_to_dictr,)r! data_dictr.values& rto_dictCardData.to_dictsKMM$--0  i -6__->T->zs%  ->TTTs AAc R#)zUse this method in child classes to alter the dict representation of the data. Alter the dict in-place. Args: data_dict (`dict`): The raw dict representation of the card data. Nr3r!r]s&&rr\CardData._to_dicts rNcF<V^8dQhRS[S[S[,,RS[/#)roriginal_orderrrrrC)rrs"rrrRs)YYxS 7JYVYYrc fV'd|V\\VPP44\V4, 4,Uu/uF(pW0P9gKW0PV,bK* upVn\ VP 4RVR7P 4#uupi)zDumps CardData to a YAML block for inclusion in a README.md file. Args: line_break (str, *optional*): The line break to use when dumping to yaml. Returns: `str`: CardData represented as a YAML block. F) sort_keys line_break)listsetr+keysr r_strip)r!rireks&&& rto_yamlCardData.to_yamls ($s4==3E3E3G/H3~K^/^*___A %$==##_DM 5ZPVVXX s B.%B.c,\VP4#rT)reprr+r s&r__repr__CardData.__repr__sDMM""rc"VP4#rT)ror s&r__str__CardData.__str__s||~rc,<V^8dQhRS[RS[RS[/#rr.defaultrrCr)rrs"rrrRs"33s3S3C3rc HVPPV4pVfV#T#z#Get value for a given metadata key.)r+get)r!r.rzr^s&&& rr~ CardData.gets% !!#&-w2U2rc,<V^8dQhRS[RS[RS[/#ryr{)rrs"rrrRs"//s/S/C/rc 8VPPW4#)z#Pop value for a given metadata key.)r+pop)r!r.rzs&&&rr CardData.pops}}  ..rc&<V^8dQhRS[RS[/#rr.rr{)rrs"rrrRs""s"s"rc (VPV,#r}r+r!r.s&&r __getitem__CardData.__getitem__s}}S!!rc*<V^8dQhRS[RS[RR/#)rr.r^rNr{)rrs"rrrRs"##s#3#4#rc "W PV&R#)z#Set value for a given metadata key.Nr)r!r.r^s&&&r __setitem__CardData.__setitem__s" crc&<V^8dQhRS[RS[/#r)rCr')rrs"rrrRs$$$$rc WP9#)z%Check if a given metadata key is set.rrs&&r __contains__CardData.__contains__smm##rc <V^8dQhRS[/#r)int)rrs"rrrRs"""rc ,\VP4#)z'Return the number of metadata keys set.)lenr+r s&r__len__CardData.__len__s4==!!rr)F)NNrT)rDrErFrGrHrWr_r\rorsrvr~rrrrrrKrLrMs@rrOrOsq%% U YY$#33 //""##$$""rrOcV^8dQhR\\\\\,3,,R\\,R\\,/#)r eval_results model_namer)rr rrrC)rs"rrrsC5T*-=!=>?  *rc2Vf.#\V\4'dV.p\V\4'd;\;QJdRV4F 'dK RM RM !RV44'g\ R\ V4 R24hVf \ R4hV#)Nc3B"TFp\V\4xK R#5irT) isinstancer).0rs& r )_validate_eval_results..s4eXdSTZ:5N5NXdsFTzM`eval_results` should be of type `EvalResult` or a list of `EvalResult`, got .z7Passing `eval_results` requires `model_name` to be set.)rrrjallr7type)rrs&&r_validate_eval_resultsrs , ++$~ lD ) )4eXd4e4eXd4e1e1e[\`am\n[oop q  RSS rcaa]tRtRtoRtRRRRRRRRRRR RR RR RR RR RRRRRRR/ V3RlV3RllltRtRtVtV;t #) ModelCardDatai aiModel Card Metadata that is used by Hugging Face Hub when included at the top of your README.md Args: 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. 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']} ``` base_modelNdatasetsrlanguage library_namelicense license_name license_linkmetricsr pipeline_tagtagsrQFc<V^8dQhRS[S[S[S[S[,3,,RS[S[S[S[S[,3,,RS[S[S[,,RS[S[S[S[S[,3,,RS[S[,RS[S[,RS[S[,RS[S[,R S[S[S[,,R S[S[,R S[S[,R S[S[S[,,R S[/ #)rrrrrrrrrrrrrrQ)rr rCrrr')rrs"rrModelCardData.__annotate__Is8U8UU3S >238U5d3i01 8U tJ/0 8U 5d3i01 8Usm8U#8Usm8Usm8U$s)$8USM8Usm8UtCy!8U!%8Urc  <WnW nW0nW@nWPnW`nWpnWnWnWn Wn \V 4Vn VPRR4pV'd\V4wrWn W0n\*SV`X!R/VBVP'd)\/VPVP4VnR#R# \\ 3dDpT 'd\"P%R4Rp?L\'RTP( RT R24hRp?ii;i \0d<pT 'd \"P%RT R24Rp?R#\'RT 24ThRp?ii;i) model-indexNz    ('B4??TXTeTe'fIm $.)9\+B )r) rrrrrrrrrrrr rDrErFrGrHrWr\rKrL __classcell__rrs@@rrr s=~8U7;8U59 8U 48 8U 59 8U'+8U"&8U'+8U'+8U(,8U%)8U'+8U%)8U(-8U8UtCCrrcaa]tRtRtoRtRRRRRRRRRRR RR RR RR RR RRRRRRRRR/V3RlV3RllltRtRtVtV;t #)DatasetCardDataiaG Dataset Card Metadata that is used by Hugging Face Hub when included at the top of your README.md Args: 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. rNrannotations_creatorslanguage_creatorsmultilingualitysize_categoriessource_datasetstask_categoriestask_idspaperswithcode_id pretty_nametrain_eval_index config_namesrQFc<V^8dQhRS[S[S[S[S[,3,,RS[S[S[S[S[,3,,RS[S[S[S[S[,3,,RS[S[S[S[S[,3,,RS[S[S[S[S[,3,,RS[S[S[S[S[,3,,RS[S[S[,,RS[S[S[S[S[,3,,R S[S[S[S[S[,3,,R S[S[,R S[S[,R S[S[,R S[S[S[S[S[,3,,RS[/#)rrrrrrrrrrrrrrrQ)rr rCrrr')rrs"rrDatasetCardData.__annotate__sP"#"#5d3i01"#%T#Y/0 "# 'uS$s)^'<= "# $E#tCy.$9: "#"%T#Y"78"#"%T#Y"78"#"$s),"#"%T#Y"78"#5d3i01"#$C="#c]"##4."#uS$s)^45"# !%!"#rc <W0nW@nWnW nWPnW`nWpnWnWnWn Wn Wn T ;'gVPRR4Vn \SV`<!R/VBR#)train-eval-indexNr3)rrrrrrrrrrrrrrrrW)r!rrrrrrrrrrrrrrQrVrs&$$$$$$$$$$$$$$,rrWDatasetCardData.__init__sy&%9!!2  .... !2&(!1 X XFJJ?QSW4X "6"rc.VPR4VR&R#)rrN)rrbs&&rr\DatasetCardData._to_dicts(1 6H(I $%r) rrrrrrrrrrrrrrrs@@rrrs%N"#59"#48 "# AE "# >B "#<@"#<@"#04"#<@"#59"#,0"#&*"#,0"#9="# (-!"#"#HJJrrcvaa]tRtRtoRtRRRRRRRRRRR RR RR RR RR RRRRR/ V3RlV3RllltRtVtV;t#) SpaceCardDataia8 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. Args: 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. 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'} ``` titleNsdk sdk_versionpython_versionapp_fileapp_portrduplicated_frommodelsrrrQFcB<V^8dQhRS[S[,RS[S[,RS[S[,RS[S[,RS[S[,RS[S[,RS[S[,RS[S[,R S[S[S[,,R S[S[S[,,R S[S[S[,,R S[/ #) rrrrrrrrrrrrrQ)rrCrrr')rrs"rrSpaceCardData.__annotate__ s##}#c] # c] # ! #3-#3-###"##c##49%#tCy!#!%#rc  <WnW nW0nW@nWPnW`nWpnWnWnWn \V 4Vn \SV`4!R/V BR#)Nr3)rrrrrrrrrrrrrrW)r!rrrrrrrrrrrrQrVrs&$$$$$$$$$$$$,rrWSpaceCardData.__init__ sV" &,   .  #D)  "6"r) rrrrrrrrrrr) rDrErFrGrHrWrKrLrrs@@rrrs/b# $#" # &* # )- ##'##'#"&#*.#'+#)-#%)#(-###rrcV^8dQhR\\\\3,,R\\\\ ,3,/#)rrr)rrrCrr r)rs"rrr+s<eeT$sCx.-AeeCQUV`QaLaFberc .pVEFpVR,pVR,pVEFpVR,R,pVR,PR4pVR,R,pVR,R,p VR,PR4p VR,PR4p VR,PR4p VR,PR 4p VPR /4PR4pVPR /4PR 4pVR ,FpVR,pVR ,pVPR4pVPR 4pVPR4pVPR4pVPR4p\R /RVbRVbRV bRVbRVbRVbRV bRV bRV bRV bRVbRVbRVbRVbRVbRVbRVbpVPV4K EK EK XV3#)!aTakes 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 Args: 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. 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' ``` nameresultstaskrdatasetconfigsplitrevisionargssourceurlrr^rB verifyTokenrrr;r<r)r=rrrr>r?rAr@r*r5r6r3)r~rappend)rrelemrrresultrr=rr;rrrr>r5r6metricr<r)r?rAr@rBr* eval_results& rrr+s3pLF|y/Fvv.Iv**62I!),V4L!),V4L#I.228KHb155e.sG#Qa#sc3j"TF)wrVfK VfK\V4\V43xK+ R#5irTr)rrnvs& rrrs+w WX;ij;,q/<?; s 333)rrjrrkrdictr,)objs&rrrsZ#eS)**CyG#GGG C  Cyw www rc V^8dQhR\R\\,R\\\\3,,/#)rrrr)rCrrrr)rs"rrrs>Y%Y%CY%tJ?OY%TXY]^acf^fYgThY%rc\\4pVF$pW#P,PV4K& .pVP 4EF'pV^,pRRVP RVP /RRVPRVPRVPRVPRVPRVP/R VUu.uFYpRVPR VPRVPRVP RVP"R VP$R VP&/NK[ up/pVP(e0R VP(/p VP*eVP*V R&WR&VPV4EK* RVRV/.p \-V 4#uupi)a@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. Args: 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. 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}]}]}] ``` rrrrrrrrrr^rBrrrr)rrjr"rvaluesrr=r;rrrrr>r<r)r?r@rArBr*r6r5r) rrtask_and_ds_types_maprmodel_index_datar sample_resultrdatarrs && rrrsJ:ET9J# ;;<CCKP$(//1  // //  22 22-6644M:: 22  & &FF..V00F..f22F..!6#6#6&  4  # # /}//F((4!.!:!:v#N%I2T J ' K  $$= sAF cV^8dQhR\\\,,R\\\,,/#)rrrrf)rs"rrrs,(49-(492ErcZVfV#.pVFpW!9gK VPV4K V#rT)r)r unique_tagstags& rrrs7 | K  !   s # r)rZ collectionsr dataclassesrtypingrrrrr r huggingface_hub.utilsr r get_loggerrDrrrOrrrrrrrrr3rrrs #!::4   H % TbTb Tbn P"P" P"f"~CH~CBMJhMJ`N#HN#beP Y%xr