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

Args:
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.

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  
```
```