Datasets:
Tasks:
Other
Modalities:
Text
Sub-tasks:
part-of-speech
Languages:
Polish
Size:
10K - 100K
Tags:
structure-prediction
License:
File size: 3,662 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 json
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.jsonl",
"test": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/test.jsonl",
}
_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):
VERSION = datasets.Version("1.1.0")
def _info(self) -> datasets.DatasetInfo:
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=list(_POS_TAGS), num_classes=len(_POS_TAGS)
)
),
}
),
homepage=_HOMEPAGE,
version=self.VERSION,
)
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"]},
),
]
@staticmethod
def _clean_line(data_line: Dict):
new_tokens = []
new_pos_tags = []
for token, pos_tag in zip(data_line["tokens"], data_line["pos_tags"]):
if pos_tag in _POS_TAGS:
new_tokens.append(token)
new_pos_tags.append(pos_tag)
data_line["tokens"] = new_tokens
data_line["pos_tags"] = new_pos_tags
assert len(data_line["tokens"]) == len(data_line["pos_tags"])
return data_line
def _generate_examples(
self, filepath: str
) -> Generator[Tuple[str, Dict[str, str]], None, None]:
with open(filepath, "r", encoding="utf-8") as f:
for line in f:
data_line = self._clean_line(json.loads(line))
yield data_line["id"], data_line
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