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* Implement dataloader for khpos * Remove unneeded comment * Implemented Test and Validation loading * Streamlining code
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# coding=utf-8 | ||
# Copyright 2022 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. | ||
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""" | ||
The khPOS Corpus (Khmer POS Corpus) is a 12,000 sentences (25,626 words) manually word segmented and POS tagged corpus | ||
developed for Khmer language NLP research and developments. We collected Khmer sentences from websites that include | ||
various area such as economics, news, politics. Moreover it is also contained some student list and voter list of | ||
national election committee of Cambodia. The average number of words per sentence in the whole corpus is 10.75. | ||
Here, some symbols such as "។" (Khmer sign Khan), "៖" (Khmer sign Camnuc pii kuuh), "-", "?", "[", "]" etc. also | ||
counted as words. The shortest sentence contained only 1 word and longest sentence contained 169 words. This dataset contains | ||
A validation set and a test set, each containing 1000 sentences. | ||
""" | ||
from pathlib import Path | ||
from typing import Dict, List, Tuple | ||
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import datasets | ||
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from seacrowd.utils import schemas | ||
from seacrowd.utils.configs import SEACrowdConfig | ||
from seacrowd.utils.constants import Tasks, Licenses | ||
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_CITATION = """\ | ||
@inproceedings{kyaw2017comparison, | ||
title={Comparison of Six POS Tagging Methods on 12K Sentences Khmer Language POS Tagged Corpus}, | ||
author={Ye Kyaw Thu and Vichet Chea and Yoshinori Sagisaka}, | ||
booktitle={Proceedings of the first Regional Conference on Optical character recognition and Natural language processing technologies for ASEAN languages (ONA 2017)}, | ||
year={2017}, | ||
month={December 7-8}, | ||
address={Phnom Penh, Cambodia} | ||
} | ||
""" | ||
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_DATASETNAME = "khpos" | ||
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_DESCRIPTION = """\ | ||
The khPOS Corpus (Khmer POS Corpus) is a 12,000 sentences (25,626 words) manually word segmented and POS tagged corpus | ||
developed for Khmer language NLP research and developments. We collected Khmer sentences from websites that include | ||
various area such as economics, news, politics. Moreover it is also contained some student list and voter list of | ||
national election committee of Cambodia. The average number of words per sentence in the whole corpus is 10.75. | ||
Here, some symbols such as "។" (Khmer sign Khan), "៖" (Khmer sign Camnuc pii kuuh), "-", "?", "[", "]" etc. also | ||
counted as words. The shortest sentence contained only 1 word and longest sentence contained 169 words. This dataset contains | ||
A validation set and a test set, each containing 1000 sentences. | ||
""" | ||
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_HOMEPAGE = "https://github.com/ye-kyaw-thu/khPOS/tree/master" | ||
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_LANGUAGES = ['khm'] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) | ||
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_LICENSE = Licenses.CC_BY_NC_SA_4_0.value | ||
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_LOCAL = False | ||
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_URLS = { | ||
_DATASETNAME: { | ||
'train': "https://raw.githubusercontent.com/ye-kyaw-thu/khPOS/master/corpus-draft-ver-1.0/data/after-replace/train.all2", | ||
'validation': "https://raw.githubusercontent.com/ye-kyaw-thu/khPOS/master/corpus-draft-ver-1.0/data/OPEN-TEST", | ||
'test': "https://raw.githubusercontent.com/ye-kyaw-thu/khPOS/master/corpus-draft-ver-1.0/data/CLOSE-TEST" | ||
} | ||
} | ||
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_SUPPORTED_TASKS = [Tasks.POS_TAGGING] | ||
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_SOURCE_VERSION = "1.0.0" | ||
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_SEACROWD_VERSION = "1.0.0" | ||
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class KhPOS(datasets.GeneratorBasedBuilder): | ||
"""\ | ||
This datasets contain 12000 sentences (25626 words) for the Khmer language. | ||
There are 24 POS tags and their description can be found at https://github.com/ye-kyaw-thu/khPOS/tree/master. | ||
The used Khmer Tokenizer can be found in the above github repository as well. This dataset contains | ||
A validation set and a test set, each containing 1000 sentences. | ||
""" | ||
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | ||
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) | ||
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BUILDER_CONFIGS = [ | ||
SEACrowdConfig( | ||
name="khpos_source", | ||
version=SOURCE_VERSION, | ||
description="khpos source schema", | ||
schema="source", | ||
subset_id="khpos", | ||
), | ||
SEACrowdConfig( | ||
name="khpos_seacrowd_seq_label", | ||
version=SEACROWD_VERSION, | ||
description="khpos SEACrowd schema", | ||
schema="seacrowd_seq_label", | ||
subset_id="khpos", | ||
), | ||
] | ||
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DEFAULT_CONFIG_NAME = "khpos_source" | ||
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def _info(self) -> datasets.DatasetInfo: | ||
if self.config.schema == "source": | ||
features = datasets.Features({ | ||
"id" : datasets.Value("string"), | ||
"tokens" : datasets.Sequence(datasets.Value("string")), | ||
#pos_tags follows order from corpus-draft-ver-1.0/data/after-replace/train.all2.tag.freq | ||
"pos_tags": datasets.Sequence(datasets.features.ClassLabel( | ||
names = [ | ||
'AB', 'AUX', 'CC', 'CD', | ||
'DBL', 'DT', 'ETC', 'IN', | ||
'JJ', 'KAN', 'M', 'NN', | ||
'PA', 'PN', 'PRO', 'QT', | ||
'RB', 'RPN', 'SYM', 'UH', | ||
'VB', 'VB_JJ', 'VCOM' | ||
] | ||
)) | ||
}) | ||
elif self.config.schema == "seacrowd_seq_label": | ||
features = schemas.seq_label.features([ | ||
'AB', 'AUX', 'CC', 'CD', | ||
'DBL', 'DT', 'ETC', 'IN', | ||
'JJ', 'KAN', 'M', 'NN', | ||
'PA', 'PN', 'PRO', 'QT', | ||
'RB', 'RPN', 'SYM', 'UH', | ||
'VB', 'VB_JJ', 'VCOM' | ||
]) | ||
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return datasets.DatasetInfo( | ||
description=_DESCRIPTION, | ||
features=features, | ||
homepage=_HOMEPAGE, | ||
license=_LICENSE, | ||
citation=_CITATION, | ||
) | ||
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | ||
"""Returns SplitGenerators.""" | ||
urls = _URLS[_DATASETNAME]['train'] | ||
path = dl_manager.download_and_extract(urls) | ||
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dev_url = _URLS[_DATASETNAME]['validation'] | ||
dev_path = dl_manager.download_and_extract(dev_url) | ||
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test_url = _URLS[_DATASETNAME]['test'] | ||
test_path = dl_manager.download_and_extract(test_url) | ||
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return [ | ||
datasets.SplitGenerator( | ||
name=datasets.Split.TRAIN, | ||
gen_kwargs={ | ||
"filepath": path, | ||
"split": "train", | ||
}, | ||
), | ||
datasets.SplitGenerator( | ||
name=datasets.Split.VALIDATION, | ||
gen_kwargs={ | ||
"filepath": dev_path, | ||
"split": "dev", | ||
}, | ||
), | ||
datasets.SplitGenerator( | ||
name=datasets.Split.TEST, | ||
gen_kwargs={ | ||
"filepath": test_path, | ||
"split": "test", | ||
}, | ||
), | ||
] | ||
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: | ||
"""Yields examples as (key, example) tuples.""" | ||
with open(filepath, encoding="utf-8") as file: | ||
counter = 0 | ||
for line in file: | ||
if line.strip() != "": | ||
groups = line.split(" ") | ||
tokens = [] | ||
pos_tags = [] | ||
for group in groups: | ||
token, pos_tag = group.split("/") | ||
tokens.append(token) | ||
pos_tags.append(pos_tag) | ||
if self.config.schema == "source": | ||
yield ( | ||
counter, | ||
{ | ||
"id" : str(counter), | ||
"tokens" : tokens, | ||
"pos_tags": pos_tags | ||
} | ||
) | ||
counter += 1 | ||
elif self.config.schema == "seacrowd_seq_label": | ||
yield ( | ||
counter, | ||
{ | ||
"id" : str(counter), | ||
"tokens": tokens, | ||
"labels": pos_tags | ||
} | ||
) | ||
counter += 1 |