First version of dataset scripts. Datafiles and tagset list.

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'
+size_categories:
+- 18K
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+---
+
+# CEN

cen.py

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+# coding=utf-8
+"""CEN dataset."""
+
+import csv
+import datasets
+
+_DESCRIPTION = "CEN dataset."
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/clarin-knext/cen/resolve/main/data/train.iob",
+    "valid": "https://huggingface.co/datasets/clarin-knext/cen/resolve/main/data/valid.iob",
+    "test": "https://huggingface.co/datasets/clarin-knext/cen/resolve/main/data/test.iob",
+}
+
+_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/6"
+
+with open('data/n82_tagset.txt', 'r') as fin:
+    _N82_TAGS = fin.read().split('\n')
+
+_NER_IOB_TAGS = ['O']
+
+for tag in _N82_TAGS:
+    _NER_IOB_TAGS.extend([f'B-{tag}', f'I-{tag}'])
+
+
+class CenDataset(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])

data/n82_tagset.txt

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+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