def batch_geoparse(self, text_list):
        """
        Batch geoparsing function. Take in a list of text documents and return a list of lists
        of the geoparsed documents. The speed improvements come exclusively from using spaCy's `nlp.pipe`.

        Parameters
        ----------
        text_list : list of strs
            List of documents. The documents should not have been pre-processed by spaCy.

        Returns
        -------
        processed : list of list of dictionaries.
            The list is the same length as the input list of documents. Each element is a list of dicts, one for
            each geolocated entity.
        """
        if not self.threads:
            print("batch_geoparsed should be used with threaded searches. Please set `threads=True` when initializing the geoparser.")
        nlped_docs = list(nlp.pipe(text_list, as_tuples=False, n_threads=multiprocessing.cpu_count()))
        processed = []
        for i in tqdm(nlped_docs, disable=not self.progress):
            p = self.geoparse(i)
            processed.append(p)
        return processed
Batch geoparsing function. Take in a list of text documents and return a list of lists
        of the geoparsed documents. The speed improvements come exclusively from using spaCy's `nlp.pipe`.

        Parameters
        ----------
        text_list : list of strs
            List of documents. The documents should not have been pre-processed by spaCy.

        Returns
        -------
        processed : list of list of dictionaries.
            The list is the same length as the input list of documents. Each element is a list of dicts, one for
            each geolocated entity.