def for_text_classification(
        cls: Type["FeedbackDataset"],
        labels: List[str],
        multi_label: bool = False,
        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 text classification tasks.

        Args:
            labels: A list of labels for your dataset
            multi_label: Set this parameter to True if you want to add multiple labels to 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 text classification containing "text" field and LabelQuestion or MultiLabelQuestion named "label"
        """
        default_guidelines = "This is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection."

        description = "Classify the text by selecting the correct label from the given list of labels."
        return cls(
            fields=[TextField(name="text", use_markdown=use_markdown)],
            questions=[
                LabelQuestion(
                    name="label",
                    labels=labels,
                    description=description,
                )
                if not multi_label
                else MultiLabelQuestion(
                    name="label",
                    labels=labels,
                    description=description,
                )
            ],
            guidelines=guidelines
            if guidelines is not None
            else default_guidelines
            if multi_label
            else default_guidelines.replace("one or more labels", "one label"),
            metadata_properties=metadata_properties,
            vectors_settings=vectors_settings,
        )

        You can use this method to create a basic dataset for text classification tasks.

        Args:
            labels: A list of labels for your dataset
            multi_label: Set this parameter to True if you want to add multiple labels to 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 text classification containing "text" field and LabelQuestion or MultiLabelQuestion named "label"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py