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idner_news_2k.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.
+
+"""
+A dataset of Indonesian News for Named-Entity Recognition task.
+  This dataset re-annotated the dataset previously provided by Syaifudin & Nurwidyantoro (2016)
+  (https://github.com/yusufsyaifudin/Indonesia-ner) with a more standardized NER tags.
+  There are three subsets, namely train.txt, dev.txt, and test.txt.
+  Each file consists of three columns which are Tokens, PoS Tag, and NER Tag respectively.
+  The format is following CoNLL dataset. The NER tag use the IOB format.
+  The PoS tag using UDPipe (http://ufal.mff.cuni.cz/udpipe),
+  a pipeline for tokenization, tagging, lemmatization and dependency parsing
+  whose model is trained on UD Treebanks.
+"""
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{khairunnisa-etal-2020-towards,
+    title = "Towards a Standardized Dataset on {I}ndonesian Named Entity Recognition",
+    author = "Khairunnisa, Siti Oryza  and
+      Imankulova, Aizhan  and
+      Komachi, Mamoru",
+    editor = "Shmueli, Boaz  and
+      Huang, Yin Jou",
+    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
+      and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
+    month = dec,
+    year = "2020",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.aacl-srw.10",
+    pages = "64--71",
+    abstract = "In recent years, named entity recognition (NER) tasks in the Indonesian language
+    have undergone extensive development. There are only a few corpora for Indonesian NER;
+    hence, recent Indonesian NER studies have used diverse datasets. Although an open dataset is available,
+    it includes only approximately 2,000 sentences and contains inconsistent annotations,
+    thereby preventing accurate training of NER models without reliance on pre-trained models.
+    Therefore, we re-annotated the dataset and compared the two annotations{'} performance
+    using the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) approach.
+    Fixing the annotation yielded a more consistent result for the organization tag and improved the prediction score
+    by a large margin. Moreover, to take full advantage of pre-trained models, we compared different feature embeddings
+    to determine their impact on the NER task for the Indonesian language.",
+}
+"""
+
+_DATASETNAME = "idner_news_2k"
+
+_DESCRIPTION = """\
+A dataset of Indonesian News for Named-Entity Recognition task.
+  This dataset re-annotated the dataset previously provided by Syaifudin & Nurwidyantoro (2016)
+  (https://github.com/yusufsyaifudin/Indonesia-ner) with a more standardized NER tags.
+  There are three subsets, namely train.txt, dev.txt, and test.txt.
+  Each file consists of three columns which are Tokens, PoS Tag, and NER Tag respectively.
+  The format is following CoNLL dataset. The NER tag use the IOB format.
+  The PoS tag using UDPipe (http://ufal.mff.cuni.cz/udpipe),
+  a pipeline for tokenization, tagging, lemmatization and dependency parsing
+  whose model is trained on UD Treebanks.
+"""
+
+_HOMEPAGE = "https://github.com/khairunnisaor/idner-news-2k"
+
+_LANGUAGES = ["ind"]
+
+_LICENSE = Licenses.MIT.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://raw.githubusercontent.com/khairunnisaor/idner-news-2k/main/train.txt",
+        "dev": "https://raw.githubusercontent.com/khairunnisaor/idner-news-2k/main/dev.txt",
+        "test": "https://raw.githubusercontent.com/khairunnisaor/idner-news-2k/main/test.txt",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class IdNerNews2kDataset(datasets.GeneratorBasedBuilder):
+    """This dataset is designed for Named-Entity Recognition NLP task in Indonesian,
+    consisting of train, dev, and test files in CoNLL format. The NER tag in IOB format."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "seq_label"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        NAMED_ENTITIES = ["B-LOC", "I-LOC", "B-ORG", "I-ORG", "B-PER", "I-PER", "O"]
+        POS_TAGS = ["PROPN", "AUX", "NUM", "NOUN", "ADP", "PRON", "VERB", "ADV", "ADJ", "PUNCT", "DET", "PART", "SCONJ", "CCONJ", "SYM", "X"]
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(datasets.ClassLabel(names=POS_TAGS)),
+                    "ner_tags": datasets.Sequence(datasets.ClassLabel(names=NAMED_ENTITIES)),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.seq_label.features(NAMED_ENTITIES)
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+
+        urls = _URLS[_DATASETNAME]
+        train_path = dl_manager.download_and_extract(urls["train"])
+        dev_path = dl_manager.download_and_extract(urls["dev"])
+        test_path = dl_manager.download_and_extract(urls["test"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_path,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_path,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": dev_path,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        df = pd.read_csv(filepath, delimiter=" ", header=None, skip_blank_lines=False)
+        if self.config.schema == "source":
+            tokens, pos_tags, ner_tags = [], [], []
+
+            for idx, row in df.iterrows():
+                if pd.isnull(row[0]):
+                    if tokens:
+                        yield idx, {"id": idx, "tokens": tokens, "pos_tags": pos_tags, "ner_tags": ner_tags}
+                        tokens, pos_tags, ner_tags = [], [], []
+                else:
+                    tokens.append(row[0])
+                    pos_tags.append(row[1])
+                    ner_tags.append(row[2])
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            tokens, ner_tags = [], []
+
+            for idx, row in df.iterrows():
+                if pd.isnull(row[0]):
+                    if tokens:
+                        yield idx, {"id": idx, "tokens": tokens, "labels": ner_tags}
+                        tokens, ner_tags = [], []
+                else:
+                    tokens.append(row[0])
+                    ner_tags.append(row[2])
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")