def for_natural_language_inference(
        cls: Type["FeedbackDataset"],
        labels: Optional[List[str]] = None,
        use_markdown: bool = False,
        guidelines: Optional[str] = None,
        metadata_properties: List[AllowedMetadataPropertyTypes] = None,
        vectors_settings: List[VectorSettings] = None,
    ) -> "FeedbackDataset":
        """
        You can use this method to create a basic dataset for natural language inference tasks.

        Args:
            labels: A list of labels for your dataset
            use_markdown: Set this parameter to True if you want to use markdown in your dataset
            guidelines: Contains the guidelines for the dataset
            metadata_properties: Contains the metadata properties that will be indexed and could be used to filter the dataset. Defaults to `None`.
            vectors_settings: Define the configuration of the vectors associated to the records that will be used to perform the vector search. Defaults to `None`.

        Returns:
            A `FeedbackDataset` object for natural language inference containing "premise" and "hypothesis" fields and a LabelQuestion named "label"
        """
        default_guidelines = "This is a natural language inference dataset that contains premises and hypotheses. Please choose the correct label for the given premise and hypothesis."
        if labels is None:
            labels = ["entailment", "neutral", "contradiction"]
        return cls(
            fields=[
                TextField(name="premise", use_markdown=use_markdown),
                TextField(name="hypothesis", use_markdown=use_markdown),
            ],
            questions=[LabelQuestion(name="label", labels=labels, description="Choose one of the labels.")],
            guidelines=default_guidelines if guidelines is None else guidelines,
            metadata_properties=metadata_properties,
            vectors_settings=vectors_settings,
        )

        You can use this method to create a basic dataset for natural language inference tasks.

        Args:
            labels: A list of labels for your dataset
            use_markdown: Set this parameter to True if you want to use markdown in your dataset
            guidelines: Contains the guidelines for the dataset
            metadata_properties: Contains the metadata properties that will be indexed and could be used to filter the dataset. Defaults to `None`.
            vectors_settings: Define the configuration of the vectors associated to the records that will be used to perform the vector search. Defaults to `None`.

        Returns:
            A `FeedbackDataset` object for natural language inference containing "premise" and "hypothesis" fields and a LabelQuestion named "label"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py