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Datasets:
classla
/
janes_tag

Tasks:
Other
Modalities:
Text
Sub-tasks:
lemmatization
part-of-speech
Languages:
Sinhala
Size:
1K - 10K
Tags:
structure-prediction
normalization
tokenization
Libraries:
Datasets
License:
Dataset card Data Studio Files
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janes_tag / janes_tag.py
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
import os
import datasets
_CITATION = ''
_DESCRIPTION = """The dataset contains 6273 training samples, 762 validation samples and 749 test samples.
Each sample represents a sentence and includes the following features: sentence ID ('sent_id'),
list of tokens ('tokens'), list of normalised word forms ('norms'), list of lemmas ('lemmas'),
list of Multext-East tags ('xpos_tags), list of morphological features ('feats'),
and list of UPOS tags ('upos_tags'), which are encoded as class labels.
"""
_HOMEPAGE = ''
_LICENSE = ''
_URL = 'https://huggingface.co/datasets/classla/janes_tag/raw/main/data.zip'
_TRAINING_FILE = 'train_all.conllup'
_DEV_FILE = 'dev_all.conllup'
_TEST_FILE = 'test_all.conllup'
_DATA_DIR = 'data'
class JanesTag(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version('1.0.0')
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name='janes_tag',
version=VERSION,
description=''
)
]
def _info(self):
features = datasets.Features(
{
'sent_id': datasets.Value('string'),
'tokens': datasets.Sequence(datasets.Value('string')),
'norms': datasets.Sequence(datasets.Value('string')),
'lemmas': datasets.Sequence(datasets.Value('string')),
'xpos_tags': datasets.Sequence(datasets.Value('string')),
'feats': datasets.Sequence(datasets.Value('string')),
'upos_tags': datasets.Sequence(
datasets.features.ClassLabel(
names=[
'SCONJ VERB', 'NOUN', 'NOUN NOUN', 'CCONJ SCONJ', 'ADV X', 'ADJ', 'NOUN NUM', 'ADP VERB',
'CCONJ', 'SCONJ AUX', 'VERB', 'PRON PRON', 'CCONJ PART', 'ADV ADJ', 'PRON AUX', 'AUX AUX',
'VERB ADP', 'DET ADJ', 'ADJ NOUN', 'PART PART', 'ADV AUX', 'NOUN ADV', 'PART CCONJ',
'DET NOUN', 'CCONJ CCONJ', 'ADV', 'NUM', 'AUX NUM', 'ADV DET', 'ADV ADV', 'PRON VERB',
'ADP PRON', 'DET AUX', 'VERB ADV', 'PROPN PROPN', 'NOUN PROPN', 'ADJ ADP', 'PART AUX',
'PROPN NOUN', 'PROPN ADV', 'ADP NOUN', 'NUM ADV', 'NOUN ADJ', 'SCONJ', 'PART NOUN',
'ADV NUM', 'VERB PRON', 'PART ADJ', 'AUX', 'ADP NUM', 'PRON', 'ADP ADJ', 'INTJ', 'ADV VERB',
'NOUN SYM', 'PART', 'ADV PART', 'DET VERB', 'SCONJ PART', 'ADV SCONJ', 'NOUN CCONJ',
'NUM DET', 'ADP X', 'INTJ X', 'NOUN VERB', 'PUNCT', 'ADP', 'ADV CCONJ', 'NOUN DET',
'X NOUN', 'DET', 'PROPN X', 'SYM', 'PROPN NUM', 'PART VERB', 'SYM INTJ', 'ADP ADV',
'X PROPN', 'X X', 'PROPN', 'ADP DET', 'X', 'AUX ADV', 'NUM NOUN', 'INTJ NOUN', 'AUX PRON',
'PART ADV', 'PRON ADP', 'INTJ INTJ', 'VERB NOUN', 'NOUN AUX'
]
)
)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = os.path.join(dl_manager.download_and_extract(_URL), _DATA_DIR)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={
'filepath': os.path.join(data_dir, _TRAINING_FILE),
'split': 'train'}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={
'filepath': os.path.join(data_dir, _DEV_FILE),
'split': 'dev'}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={
'filepath': os.path.join(data_dir, _TEST_FILE),
'split': 'test'}
),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding='utf-8') as f:
sent_id = ''
tokens = []
norms = []
lemmas = []
xpos_tags = []
feats = []
upos_tags = []
data_id = 0
for line in f:
if line and line != '\n' and not line.startswith('# global.columns') and not line.startswith('# text'):
if line.startswith('# sent_id'):
if tokens:
yield data_id, {
'sent_id': sent_id,
'tokens': tokens,
'norms': norms,
'lemmas': lemmas,
'xpos_tags': xpos_tags,
'feats': feats,
'upos_tags': upos_tags
}
tokens = []
norms = []
lemmas = []
xpos_tags = []
feats = []
upos_tags = []
data_id += 1
sent_id = line.split(' = ')[1].strip()
else:
splits = line.split('\t')
tokens.append(splits[1].strip())
norms.append(splits[2].strip())
lemmas.append(splits[3].strip())
upos_tags.append(splits[4].strip())
xpos_tags.append(splits[5].strip())
feats.append(splits[6].strip())
yield data_id, {
'sent_id': sent_id,
'tokens': tokens,
'norms': norms,
'lemmas': lemmas,
'xpos_tags': xpos_tags,
'feats': feats,
'upos_tags': upos_tags
}