def positive_sentiment(self, text: str) -> float:
        """
        Uses HuggingFace's cardiffnlp/twitter-roberta-base-sentiment model. A
        function that uses a sentiment classifier on `text`.

        Example:
            ```python
            from trulens.core import Feedback
            from trulens.providers.huggingface import Huggingface
            huggingface_provider = Huggingface()

            feedback = Feedback(huggingface_provider.positive_sentiment).on_output()
            ```

        Args:
            text (str): Text to evaluate.

        Returns:
            float: A value between 0 (negative sentiment) and 1 (positive sentiment).
        """
        max_length = 500
        truncated_text = text[:max_length]
        return self._positive_sentiment_endpoint(truncated_text)

        Uses HuggingFace's cardiffnlp/twitter-roberta-base-sentiment model. A
        function that uses a sentiment classifier on `text`.

        Example:
            ```python
            from trulens.core import Feedback
            from trulens.providers.huggingface import Huggingface
            huggingface_provider = Huggingface()

            feedback = Feedback(huggingface_provider.positive_sentiment).on_output()
            ```

        Args:
            text (str): Text to evaluate.

        Returns:
            float: A value between 0 (negative sentiment) and 1 (positive sentiment).
        
src/providers/huggingface/trulens/providers/huggingface/provider.py