def make_country_features(self, doc, require_maj=False):
        """
        Create features for the country picking model. Function where all the individual
        feature maker functions are called and aggregated. (Formerly "process_text")

        Parameters
        -----------
        doc : str or spaCy doc

        Returns
        -------
        task_list : list of dicts
            Each entry has the word, surrounding text, span, and the country picking features.
            This output can be put into Prodigy for labeling almost as-is (the "features" key needs
            to be renamed "meta" or be deleted.)
        """
        if not hasattr(doc, "ents"):
            doc = nlp(doc)
        # initialize the place to store finalized tasks
        task_list = []

        # get document vector
        #doc_vec = self._feature_word_embedding(text)['country_1']

        # get explicit counts of country names
        ct_mention, ctm_count1, ct_mention2, ctm_count2 = self._feature_country_mentions(doc)

        #  pull out the place names, skipping empty ones, countries, and known
        #  junk from the skip list (like "Atlanic Ocean"
        ents = []
        for ent in doc.ents:
            if not ent.text.strip():
                continue
            if ent.label_ not in ["GPE", "LOC", "FAC"]:
                continue
            # don't include country names (make a parameter)
            if ent.text.strip() in self._skip_list:
                continue
            ents.append(ent)
        if not ents:
            return []
        # Look them up in geonames, either sequentially if no threading, or
        # in parallel if threads.
        if self.threads:
            pool = ThreadPool(len(ents))
            ent_text = [i.text for i in ents]
            ent_results = pool.map(self.simple_lookup, ent_text)
            pool.close()
            pool.join()
        else:
            ent_results = []
            for ent in ents:
                try:
                    result = self.query_geonames(ent.text)
                except ConnectionTimeout:
                    result = ""
                ent_results.append(result)

        for n, ent in enumerate(ents):
            result = ent_results[n]
            #skip_list.add(ent.text.strip())
            ent_label = ent.label_  # destroyed by trimming
            ent = self.clean_entity(ent)

            # vector for just the solo word
            vp = self._feature_word_embedding(ent)
            try:
                word_vec = vp['country_1']
                wv_confid = float(vp['confid_a'])
            except TypeError:
                # no idea why this comes up
                word_vec = ""
                wv_confid = "0"

            # look for explicit mentions of feature names
            class_mention, code_mention = self._feature_location_type_mention(ent)
            # build results-based features
            most_alt = self._feature_most_alternative(result)
            # TODO check if most_common feature really isn't that useful
            most_common = self._feature_most_common(result)
            most_pop = self._feature_most_population(result)
            first_back, second_back = self._feature_first_back(result)

            try:
                maj_vote = Counter([word_vec, most_alt,
                                    first_back, most_pop,
                                    ct_mention
                                    #doc_vec_sent, doc_vec
                                    ]).most_common()[0][0]
            except Exception as e:
                print("Problem taking majority vote: ", ent, e)
                maj_vote = ""

            if not maj_vote:
                maj_vote = ""
            # We only want all this junk for the labeling task. We just want to straight to features
            # and the model when in production.
            try:
                start = ent.start_char
                end = ent.end_char
                iso_label = maj_vote
                try:
                    text_label = self._inv_cts[iso_label]
                except KeyError:
                    text_label = ""
                task = {"text" : ent.text,
                        "label" : text_label,  # human-readable country name
                        "word" : ent.text,
                        "spans" : [{
                            "start" : start,
                            "end" : end,
                            }  # make sure to rename for Prodigy
                                ],
                        "features" : {
                                "maj_vote" : iso_label,
                                "word_vec" : word_vec,
                                "first_back" : first_back,
                                #"doc_vec" : doc_vec,
                                "most_alt" : most_alt,
                                "most_pop" : most_pop,
                                "ct_mention" : ct_mention,
                                "ctm_count1" : ctm_count1,
                                "ct_mention2" : ct_mention2,
                                "ctm_count2" : ctm_count2,
                                "wv_confid" : wv_confid,
                                "class_mention" : class_mention,  # inferred geonames class from mentions
                                "code_mention" : code_mention,
                                #"places_vec" : places_vec,
                                #"doc_vec_sent" : doc_vec_sent
                                }
                        }
                task_list.append(task)
            except Exception as e:
                print(ent.text,)
                print(e)
        return task_list
Create features for the country picking model. Function where all the individual
        feature maker functions are called and aggregated. (Formerly "process_text")

        Parameters
        -----------
        doc : str or spaCy doc

        Returns
        -------
        task_list : list of dicts
            Each entry has the word, surrounding text, span, and the country picking features.
            This output can be put into Prodigy for labeling almost as-is (the "features" key needs
            to be renamed "meta" or be deleted.)