def pseudonymize_6(text: str, tagger: SequenceTagger) -> Tuple[str, str]:
    """
    Perform the pseudonymization action and return both the tagged version (see function "tag_entities") and the pseudonymized version
    Args:
        text (str): the input text to pseudonymize
        tagger (SequenceTagger): the flair model for NER
    Returns:
        Tuple[str, str]: the original text with tags, and the pseudonymized text
    """
    with sw.timer("root"):
        text_sentences = [Sentence(t.strip()) for t in text.split("\n") if t.strip()]
        with sw.timer("model_annotation"):
            # inplace function
            tagger.predict(
                sentences=text_sentences,
                mini_batch_size=32,
                embedding_storage_mode="none",
                verbose=True,
            )
        return tag_entities(sentences=text_sentences)