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khpos.py

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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.
+
+"""
+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
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+_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}
+}
+"""
+
+_DATASETNAME = "khpos"
+
+_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.
+"""
+
+_HOMEPAGE = "https://github.com/ye-kyaw-thu/khPOS/tree/master"
+
+_LANGUAGES = ['khm']  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value 
+
+_LOCAL = False
+
+_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"
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.POS_TAGGING]  
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+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.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    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",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "khpos_source"
+
+    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'
+                    ])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]['train']
+        path = dl_manager.download_and_extract(urls)
+
+        dev_url  = _URLS[_DATASETNAME]['validation']
+        dev_path = dl_manager.download_and_extract(dev_url)
+
+        test_url  = _URLS[_DATASETNAME]['test']
+        test_path = dl_manager.download_and_extract(test_url)
+
+        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",
+                },
+            ),
+        ]
+
+    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