from typing import Dict import requests from huggingface_hub import dataset_info, model_info from huggingface_hub.repocard import metadata_update from .config import HF_HUB_ALLOWED_TASKS from .utils.logging import get_logger logger = get_logger(__name__) def push_to_hub( 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: Dict[str, int] = None, metric_config: str = None, metric_args: Dict[str, int] = None, overwrite: bool = False, ): r""" Pushes the result of a metric to the metadata of a model repository in the Hub. Args: model_id (`str`): Model id from https://hf.co/models. task_type (`str`): Task id, refer to the [Hub allowed tasks](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 [`~datasets.load_dataset`] for more info. 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 [`~evaluate.EvaluationModule.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. Example: ```python >>> push_to_hub( ... model_id="huggingface/gpt2-wikitext2", ... metric_value=0.5 ... metric_type="bleu", ... metric_name="BLEU", ... dataset_name="WikiText", ... dataset_type="wikitext", ... dataset_split="test", ... task_type="text-generation", ... task_name="Text Generation" ... ) ```""" if task_type not in HF_HUB_ALLOWED_TASKS: raise ValueError(f"Task type not supported. Task has to be one of {HF_HUB_ALLOWED_TASKS}") try: dataset_info(dataset_type) except requests.exceptions.HTTPError: logger.warning(f"Dataset {dataset_type} not found on the Hub at hf.co/datasets/{dataset_type}") try: model_info(model_id) except requests.exceptions.HTTPError: raise ValueError(f"Model {model_id} not found on the Hub at hf.co/{model_id}") result = { "task": { "type": task_type, }, "dataset": { "type": dataset_type, "name": dataset_name, }, "metrics": [ { "type": metric_type, "value": metric_value, }, ], } if dataset_config is not None: result["dataset"]["config"] = dataset_config if dataset_split is not None: result["dataset"]["split"] = dataset_split if dataset_revision is not None: result["dataset"]["revision"] = dataset_revision if dataset_args is not None: result["dataset"]["args"] = dataset_args if task_name is not None: result["task"]["name"] = task_name if metric_name is not None: result["metrics"][0]["name"] = metric_name if metric_config is not None: result["metrics"][0]["config"] = metric_config if metric_args is not None: result["metrics"][0]["args"] = metric_args metadata = {"model-index": [{"results": [result]}]} return metadata_update(repo_id=model_id, metadata=metadata, overwrite=overwrite)