Datasets:
Tasks:
Other
Modalities:
Text
Sub-tasks:
part-of-speech
Languages:
Polish
Size:
10K - 100K
Tags:
structure-prediction
License:
File size: 4,811 Bytes
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# 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
"""NKJP-POS tagging dataset."""
import csv
from typing import List, Tuple, Dict, Generator
import datasets
_DESCRIPTION = """NKJP-POS tagging dataset."""
_URLS = {
"train": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/train.tsv",
"validation": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/valid.tsv",
"test": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/test.tsv",
}
_HOMEPAGE = "http://clip.ipipan.waw.pl/NationalCorpusOfPolish"
_POS_TAGS = [
'adj',
'adja',
'adjc',
'adjp',
'adv',
'aglt',
'bedzie',
'brev',
'burk',
'comp',
'conj',
'depr',
'fin',
'ger',
'imps',
'impt',
'inf',
'interj',
'interp',
'num',
'numcol',
'pact',
'pant',
'pcon',
'ppas',
'ppron12',
'ppron3',
'praet',
'pred',
'prep',
'qub',
'siebie',
'subst',
'winien',
'xxx'
]
class NKJPPOS(datasets.GeneratorBasedBuilder):
def _info(self) -> datasets.DatasetInfo:
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"morph": datasets.Sequence(datasets.Value("string")),
"lemmas": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(datasets.features.ClassLabel(
names=_POS_TAGS,
num_classes=len(_POS_TAGS)
)),
"full_pos_tags": datasets.Sequence(
datasets.Value("string")),
"nps": datasets.Sequence(datasets.Value("string")),
}
),
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.VALIDATION,
gen_kwargs={"filepath": downloaded_files["validation"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": downloaded_files["test"]},
),
]
@staticmethod
def _parse_tag(
tag: str
) -> Tuple[str, str]:
full_tag = tag
pos_tag = tag.split(':')[0]
return pos_tag, full_tag
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 = []
morph = []
tags = []
full_tags = []
lemma = []
nps = []
gid = 0
for line in reader:
if not line:
yield gid, {
'tokens': tokens,
'morph': morph,
'pos_tags': tags,
'full_pos_tags': full_tags,
'lemmas': lemma,
'nps': nps
}
gid += 1
tokens = []
morph = []
tags = []
full_tags = []
lemma = []
nps = []
else:
tokens.append(line[0])
morph.append(line[1])
lemma.append(line[3])
nps.append(line[4])
tag, full_tag = self._parse_tag(line[2])
tags.append(tag)
full_tags.append(full_tag)
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