Initial dataset commit.

README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- pl
+license:
+- cc-by-3.0
+multilinguality:
+- monolingual
+pretty_name: 'KPWr 1.27 & CEN'
+size_categories:
+- 18K
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+---
+
+# KPWr & CEN

kpwr_and_cen.py

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+# coding=utf-8
+"""KPWR version 1.27 & CEN merged dataset."""
+
+import csv
+import datasets
+
+_DESCRIPTION = "KPWR version 1.27 & CEN merged dataset."
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/clarin-knext/kpwr/resolve/main/data/train.iob",
+    "valid": "https://huggingface.co/datasets/clarin-knext/kpwr/resolve/main/data/valid.iob",
+    "test": "https://huggingface.co/datasets/clarin-knext/kpwr/resolve/main/data/test.iob",
+}
+
+_HOMEPAGE = "https://clarin-pl.eu/dspace/"
+
+_N82_TAGS = [
+    'nam_adj',
+    'nam_adj_city',
+    'nam_adj_country',
+    'nam_adj_person',
+    'nam_eve',
+    'nam_eve_human',
+    'nam_eve_human_cultural',
+    'nam_eve_human_holiday',
+    'nam_eve_human_sport',
+    'nam_fac_bridge',
+    'nam_fac_goe',
+    'nam_fac_goe_stop',
+    'nam_fac_park',
+    'nam_fac_road',
+    'nam_fac_square',
+    'nam_fac_system',
+    'nam_liv_animal',
+    'nam_liv_character',
+    'nam_liv_god',
+    'nam_liv_habitant',
+    'nam_liv_person',
+    'nam_loc',
+    'nam_loc_astronomical',
+    'nam_loc_country_region',
+    'nam_loc_gpe_admin1',
+    'nam_loc_gpe_admin2',
+    'nam_loc_gpe_admin3',
+    'nam_loc_gpe_city',
+    'nam_loc_gpe_conurbation',
+    'nam_loc_gpe_country',
+    'nam_loc_gpe_district',
+    'nam_loc_gpe_subdivision',
+    'nam_loc_historical_region',
+    'nam_loc_hydronym',
+    'nam_loc_hydronym_lake',
+    'nam_loc_hydronym_ocean',
+    'nam_loc_hydronym_river',
+    'nam_loc_hydronym_sea',
+    'nam_loc_land',
+    'nam_loc_land_continent',
+    'nam_loc_land_island',
+    'nam_loc_land_mountain',
+    'nam_loc_land_peak',
+    'nam_loc_land_region',
+    'nam_num_house',
+    'nam_num_phone',
+    'nam_org_company',
+    'nam_org_group',
+    'nam_org_group_band',
+    'nam_org_group_team',
+    'nam_org_institution',
+    'nam_org_nation',
+    'nam_org_organization',
+    'nam_org_organization_sub',
+    'nam_org_political_party',
+    'nam_oth',
+    'nam_oth_currency',
+    'nam_oth_data_format',
+    'nam_oth_license',
+    'nam_oth_position',
+    'nam_oth_tech',
+    'nam_oth_www',
+    'nam_pro',
+    'nam_pro_award',
+    'nam_pro_brand',
+    'nam_pro_media',
+    'nam_pro_media_periodic',
+    'nam_pro_media_radio',
+    'nam_pro_media_tv',
+    'nam_pro_media_web',
+    'nam_pro_model_car',
+    'nam_pro_software',
+    'nam_pro_software_game',
+    'nam_pro_title',
+    'nam_pro_title_album',
+    'nam_pro_title_article',
+    'nam_pro_title_book',
+    'nam_pro_title_document',
+    'nam_pro_title_song',
+    'nam_pro_title_treaty',
+    'nam_pro_title_tv',
+    'nam_pro_vehicle'
+]
+
+_NER_IOB_TAGS = ['O']
+
+for tag in _N82_TAGS:
+    _NER_IOB_TAGS.extend([f'B-{tag}', f'I-{tag}'])
+
+
+class KpwrDataset(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')),
+                    "mstags": datasets.Sequence(datasets.Value('string')),
+                    "ner": datasets.Sequence(datasets.features.ClassLabel(names=_NER_IOB_TAGS))
+                }
+            ),
+            homepage=_HOMEPAGE
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': downloaded_files['train']}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={'filepath': downloaded_files['valid']}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'filepath': downloaded_files['test']})
+        ]
+
+    def _generate_examples(self, filepath: str):
+        with open(filepath, 'r', encoding='utf-8') as fin:
+            reader = csv.reader(fin, delimiter='\t', quoting=csv.QUOTE_NONE)
+
+            tokens = []
+            lemmas = []
+            mstags = []
+            ner = []
+            gid = 0
+
+            for line in reader:
+                if not line:
+                    yield gid, {
+                        "tokens": tokens,
+                        "lemmas": lemmas,
+                        "mstags": mstags,
+                        "ner": ner
+                    }
+                    gid += 1
+                    tokens = []
+                    lemmas = []
+                    mstags = []
+                    ner = []
+                
+                elif len(line) == 1:  # ignore --DOCSTART lines
+                    continue
+
+                else:
+                    tokens.append(line[0])
+                    lemmas.append(line[1])
+                    mstags.append(line[2])
+                    ner.append(line[3])