def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
        """Embed a list of n documents/words into an n-dimensional
        matrix of embeddings.

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        """
        dataset = MyDataset(documents)

        embeddings = []
        for document, features in tqdm(
            zip(documents, self.embedding_model(dataset, truncation=True, padding=True)),
            total=len(dataset),
            disable=not verbose,
        ):
            embeddings.append(self._embed(document, features))

        return np.array(embeddings)
Embed a list of n documents/words into an n-dimensional
        matrix of embeddings.

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        
bertopic/backend/_hftransformers.py