Hub methods
Methods for using the Hugging Face Hub:
Push to hub
evaluate.push_to_hub
< source >( model_id: str task_type: str dataset_type: str dataset_name: str metric_type: str metric_name: str metric_value: float task_name: str = None dataset_config: str = None dataset_split: str = None dataset_revision: str = None dataset_args: typing.Dict[str, int] = None metric_config: str = None metric_args: typing.Dict[str, int] = None overwrite: bool = False )
Parameters
-
model_id (
str
) — Model id from https://hf.co/models. -
task_type (
str
) — Task id, refer to https://github.com/huggingface/evaluate/blob/main/src/evaluate/config.py#L154 for allowed values. -
dataset_type (
str
) — Dataset id from https://hf.co/datasets. -
dataset_name (
str
) — Pretty name for the dataset. -
metric_type (
str
) — Metric id from https://hf.co/metrics. -
metric_name (
str
) — Pretty name for the metric. -
metric_value (
float
) — Computed metric value. -
task_name (
str
, optional) — Pretty name for the task. -
dataset_config (
str
, optional) — Dataset configuration used in datasets.load_dataset(). See huggingface/datasets docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name -
dataset_split (
str
, optional) — Name of split used for metric computation. -
dataset_revision (
str
, optional) — Git hash for the specific version of the dataset. -
dataset_args (
dict[str, int]
, optional) — Additional arguments passed to datasets.load_dataset(). -
metric_config (
str
, optional) — Configuration for the metric (e.g. the GLUE metric has a configuration for each subset) -
metric_args (
dict[str, int]
, optional) — Arguments passed during Metric.compute(). -
overwrite (
bool
, optional, defaults to False) — If set to True an existing metric field can be overwritten, otherwise attempting to overwrite any existing fields will cause an error.
Pushes the result of a metric to the metadata of a model repository in the Hub.