# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """KPWR-NER tagging dataset.""" import csv from typing import List, Tuple, Dict, Generator import datasets _DESCRIPTION = """KPWR-NER tagging dataset.""" _URLS = { "train": "https://huggingface.co/datasets/clarin-pl/kpwr-ner/resolve/main/data/kpwr-ner-n82-train-tune.iob", "test": "https://huggingface.co/datasets/clarin-pl/kpwr-ner/resolve/main/data/kpwr-ner-n82-test.iob", } _HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/294" _NER_TAGS = [ "B-nam_adj", "B-nam_adj_city", "B-nam_adj_country", "B-nam_adj_person", "B-nam_eve", "B-nam_eve_human", "B-nam_eve_human_cultural", "B-nam_eve_human_holiday", "B-nam_eve_human_sport", "B-nam_fac_bridge", "B-nam_fac_goe", "B-nam_fac_goe_stop", "B-nam_fac_park", "B-nam_fac_road", "B-nam_fac_square", "B-nam_fac_system", "B-nam_liv_animal", "B-nam_liv_character", "B-nam_liv_god", "B-nam_liv_habitant", "B-nam_liv_person", "B-nam_loc", "B-nam_loc_astronomical", "B-nam_loc_country_region", "B-nam_loc_gpe_admin1", "B-nam_loc_gpe_admin2", "B-nam_loc_gpe_admin3", "B-nam_loc_gpe_city", "B-nam_loc_gpe_conurbation", "B-nam_loc_gpe_country", "B-nam_loc_gpe_district", "B-nam_loc_gpe_subdivision", "B-nam_loc_historical_region", "B-nam_loc_hydronym", "B-nam_loc_hydronym_lake", "B-nam_loc_hydronym_ocean", "B-nam_loc_hydronym_river", "B-nam_loc_hydronym_sea", "B-nam_loc_land", "B-nam_loc_land_continent", "B-nam_loc_land_island", "B-nam_loc_land_mountain", "B-nam_loc_land_peak", "B-nam_loc_land_region", "B-nam_num_house", "B-nam_num_phone", "B-nam_org_company", "B-nam_org_group", "B-nam_org_group_band", "B-nam_org_group_team", "B-nam_org_institution", "B-nam_org_nation", "B-nam_org_organization", "B-nam_org_organization_sub", "B-nam_org_political_party", "B-nam_oth", "B-nam_oth_currency", "B-nam_oth_data_format", "B-nam_oth_license", "B-nam_oth_position", "B-nam_oth_tech", "B-nam_oth_www", "B-nam_pro", "B-nam_pro_award", "B-nam_pro_brand", "B-nam_pro_media", "B-nam_pro_media_periodic", "B-nam_pro_media_radio", "B-nam_pro_media_tv", "B-nam_pro_media_web", "B-nam_pro_model_car", "B-nam_pro_software", "B-nam_pro_software_game", "B-nam_pro_title", "B-nam_pro_title_album", "B-nam_pro_title_article", "B-nam_pro_title_book", "B-nam_pro_title_document", "B-nam_pro_title_song", "B-nam_pro_title_treaty", "B-nam_pro_title_tv", "B-nam_pro_vehicle", "I-nam_adj_country", "I-nam_eve", "I-nam_eve_human", "I-nam_eve_human_cultural", "I-nam_eve_human_holiday", "I-nam_eve_human_sport", "I-nam_fac_bridge", "I-nam_fac_goe", "I-nam_fac_goe_stop", "I-nam_fac_park", "I-nam_fac_road", "I-nam_fac_square", "I-nam_fac_system", "I-nam_liv_animal", "I-nam_liv_character", "I-nam_liv_god", "I-nam_liv_person", "I-nam_loc", "I-nam_loc_astronomical", "I-nam_loc_country_region", "I-nam_loc_gpe_admin1", "I-nam_loc_gpe_admin2", "I-nam_loc_gpe_admin3", "I-nam_loc_gpe_city", "I-nam_loc_gpe_conurbation", "I-nam_loc_gpe_country", "I-nam_loc_gpe_district", "I-nam_loc_gpe_subdivision", "I-nam_loc_historical_region", "I-nam_loc_hydronym", "I-nam_loc_hydronym_lake", "I-nam_loc_hydronym_ocean", "I-nam_loc_hydronym_river", "I-nam_loc_hydronym_sea", "I-nam_loc_land", "I-nam_loc_land_continent", "I-nam_loc_land_island", "I-nam_loc_land_mountain", "I-nam_loc_land_peak", "I-nam_loc_land_region", "I-nam_num_house", "I-nam_num_phone", "I-nam_org_company", "I-nam_org_group", "I-nam_org_group_band", "I-nam_org_group_team", "I-nam_org_institution", "I-nam_org_nation", "I-nam_org_organization", "I-nam_org_organization_sub", "I-nam_org_political_party", "I-nam_oth", "I-nam_oth_currency", "I-nam_oth_data_format", "I-nam_oth_license", "I-nam_oth_position", "I-nam_oth_tech", "I-nam_oth_www", "I-nam_pro", "I-nam_pro_award", "I-nam_pro_brand", "I-nam_pro_media", "I-nam_pro_media_periodic", "I-nam_pro_media_radio", "I-nam_pro_media_tv", "I-nam_pro_media_web", "I-nam_pro_model_car", "I-nam_pro_software", "I-nam_pro_software_game", "I-nam_pro_title", "I-nam_pro_title_album", "I-nam_pro_title_article", "I-nam_pro_title_book", "I-nam_pro_title_document", "I-nam_pro_title_song", "I-nam_pro_title_treaty", "I-nam_pro_title_tv", "I-nam_pro_vehicle", "O", ] class KPWRNER(datasets.GeneratorBasedBuilder): def _info(self) -> datasets.DatasetInfo: return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string")), "lemmas": datasets.Sequence(datasets.Value("string")), "orth": datasets.Sequence(datasets.Value("string")), "ner": datasets.Sequence( datasets.features.ClassLabel( names=_NER_TAGS, num_classes=len(_NER_TAGS) ) ), } ), homepage=_HOMEPAGE, ) def _split_generators( self, dl_manager: datasets.DownloadManager ) -> List[datasets.SplitGenerator]: urls_to_download = _URLS downloaded_files = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}, ), ] def _generate_examples( self, filepath: str ) -> Generator[Tuple[int, Dict[str, str]], None, None]: with open(filepath, "r", encoding="utf-8") as f: reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) tokens = [] lemma = [] orth = [] ner = [] gid = 0 for line in reader: if not line: yield gid, { "tokens": tokens, "lemmas": lemma, "orth": orth, "ner": ner, } gid += 1 tokens = [] lemma = [] orth = [] ner = [] elif len(line) == 1: # ignore DOCS continue else: tokens.append(line[0]) lemma.append(line[1]) orth.append(line[2]) ner.append(line[3])