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indolem_ner_ugm.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from nusacrowd.utils import schemas
+from nusacrowd.utils.common_parser import load_conll_data
+
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{koto-etal-2020-indolem,
+    title = "{I}ndo{LEM} and {I}ndo{BERT}: A Benchmark Dataset and Pre-trained Language Model for {I}ndonesian {NLP}",
+    author = "Koto, Fajri  and
+      Rahimi, Afshin  and
+      Lau, Jey Han  and
+      Baldwin, Timothy",
+    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
+    month = dec,
+    year = "2020",
+    address = "Barcelona, Spain (Online)",
+    publisher = "International Committee on Computational Linguistics",
+    url = "https://aclanthology.org/2020.coling-main.66",
+    doi = "10.18653/v1/2020.coling-main.66",
+    pages = "757--770"
+}
+@phdthesis{fachri2014pengenalan,
+  title     = {Pengenalan Entitas Bernama Pada Teks Bahasa Indonesia Menggunakan Hidden Markov Model},
+  author    = {FACHRI, MUHAMMAD},
+  year      = {2014},
+  school    = {Universitas Gadjah Mada}
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "indolem_ner_ugm"
+
+_DESCRIPTION = """\
+NER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity.
+"""
+
+_HOMEPAGE = "https://indolem.github.io/"
+
+_LICENSE = "Creative Commons Attribution 4.0"
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/train.0{fold_number}.tsv",
+        "validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/dev.0{fold_number}.tsv",
+        "test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/test.0{fold_number}.tsv"
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+class IndolemNERUGM(datasets.GeneratorBasedBuilder):
+    """NER UGM comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity; and based on 5-fold cross validation"""
+
+    label_classes = ["B-PERSON", "B-LOCATION", "B-ORGANIZATION", "B-TIME", "B-QUANTITY", "I-PERSON", "I-LOCATION", "I-ORGANIZATION", "I-TIME", "I-QUANTITY", "O"]
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = ( 
+    [
+        NusantaraConfig(
+            name="indolem_ner_ugm_fold{fold_number}_source".format(fold_number=i),
+            version=_SOURCE_VERSION,
+            description="indolem_ner_ugm source schema",
+            schema="source",
+            subset_id="indolem_ner_ugm_fold{fold_number}".format(fold_number=i),
+        ) for i in range(5)
+    ] 
+    +   [
+            NusantaraConfig(
+                name="indolem_ner_ugm_fold{fold_number}_nusantara_seq_label".format(fold_number=i),
+                version=_NUSANTARA_VERSION,
+                description="indolem_ner_ugm Nusantara schema",
+                schema="nusantara_seq_label",
+                subset_id="indolem_ner_ugm_fold{fold_number}".format(fold_number=i),
+            ) for i in range(5)
+        ]
+    )
+
+    DEFAULT_CONFIG_NAME = "indolem_ner_ugm_fold0_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("string"),
+                    "tokens": [datasets.Value("string")],
+                    "tags": [datasets.Value("string")]
+                }
+            )
+
+        elif self.config.schema == "nusantara_seq_label":
+            features = schemas.seq_label_features(self.label_classes)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _get_fold_index(self):
+        try:
+            subset_id = self.config.subset_id
+            idx_fold = subset_id.index("_fold")
+            file_id = subset_id[(idx_fold + 5):]
+            return int(file_id)
+        except:
+            return 0
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        idx = self._get_fold_index()
+
+        urls = _URLS[_DATASETNAME]
+
+        for key in urls:
+            urls[key] = urls[key].format(fold_number=idx+1)
+
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        conll_dataset = load_conll_data(filepath)
+
+        if self.config.schema == "source":
+            for i, row in enumerate(conll_dataset):
+                ex = {
+                    "index": str(i),
+                    "tokens": row["sentence"],
+                    "tags": row["label"]
+                }
+                yield i, ex
+        elif self.config.schema == "nusantara_seq_label":
+            for i, row in enumerate(conll_dataset):
+                ex = {
+                    "id": str(i),
+                    "tokens": row["sentence"],
+                    "labels": row["label"]
+                }
+                yield i, ex