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indolem_nerui.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{8275098,
+  author={Gultom, Yohanes and Wibowo, Wahyu Catur},
+  booktitle={2017 International Workshop on Big Data and Information Security (IWBIS)},
+  title={Automatic open domain information extraction from Indonesian text},
+  year={2017},
+  volume={},
+  number={},
+  pages={23-30},
+  doi={10.1109/IWBIS.2017.8275098}}
+
+@article{DBLP:journals/corr/abs-2011-00677,
+  author    = {Fajri Koto and
+               Afshin Rahimi and
+               Jey Han Lau and
+               Timothy Baldwin},
+  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
+               Model for Indonesian {NLP}},
+  journal   = {CoRR},
+  volume    = {abs/2011.00677},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2011.00677},
+  eprinttype = {arXiv},
+  eprint    = {2011.00677},
+  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "indolem_nerui"
+
+_DESCRIPTION = """\
+NER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.
+The corpus has three named entity classes: location, organisation, and person with training/dev/test distribution: 1,530/170/42 and based on 5-fold cross validation.
+"""
+
+_HOMEPAGE = "https://indolem.github.io/"
+
+_LICENSE = "Creative Commons Attribution 4.0"
+
+_URLS = {
+    _DATASETNAME: [
+        {
+            "train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.01.tsv",
+            "validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.01.tsv",
+            "test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.01.tsv",
+        },
+        {
+            "train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.02.tsv",
+            "validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.02.tsv",
+            "test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.02.tsv",
+        },
+        {
+            "train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.03.tsv",
+            "validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.03.tsv",
+            "test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.03.tsv",
+        },
+        {
+            "train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.04.tsv",
+            "validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.04.tsv",
+            "test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.04.tsv",
+        },
+        {
+            "train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.05.tsv",
+            "validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.05.tsv",
+            "test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.05.tsv",
+        },
+    ]
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class IndolemNERUIDataset(datasets.GeneratorBasedBuilder):
+    """NER UI contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia. The corpus has three named entity classes: location, organisation, and person; and based on 5-fold cross validation."""
+
+    label_classes = [
+        "O",
+        "B-LOCATION",
+        "B-ORGANIZATION",
+        "B-PERSON",
+        "I-LOCATION",
+        "I-ORGANIZATION",
+        "I-PERSON",
+    ]
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name=f"indolem_nerui_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="Indolem NER UI source schema",
+            schema="source",
+            subset_id=f"indolem_nerui",
+        ),
+        NusantaraConfig(
+            name=f"indolem_nerui_nusantara_seq_label",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="Indolem NER UI Nusantara schema",
+            schema="nusantara_seq_label",
+            subset_id=f"indolem_nerui",
+        )
+    ] + [
+        NusantaraConfig(
+            name=f"indolem_nerui_fold{i}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="Indolem NER UI source schema",
+            schema="source",
+            subset_id=f"indolem_nerui_fold{i}",
+        )
+        for i in range(5)
+    ] + [
+        NusantaraConfig(
+            name=f"indolem_nerui_fold{i}_nusantara_seq_label",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="Indolem NER UI Nusantara schema",
+            schema="nusantara_seq_label",
+            subset_id=f"indolem_nerui_fold{i}",
+        )
+        for i in range(5)
+    ]
+
+    DEFAULT_CONFIG_NAME = "indolem_nerui_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 _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        idx = self._get_fold_index()
+        urls = _URLS[_DATASETNAME][idx]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                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
+
+    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:
+            # get default: fold0 (index 0)
+            return 0