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nerp.py

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+from pathlib import Path
+from typing import List
+
+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 (DEFAULT_NUSANTARA_VIEW_NAME,
+                                       DEFAULT_SOURCE_VIEW_NAME, Tasks)
+
+_DATASETNAME = "nerp"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LANGUAGES = ["ind"]
+_LOCAL = False
+_CITATION = """\
+@inproceedings{hoesen2018investigating,
+  title={Investigating bi-lstm and crf with pos tag embedding for indonesian named entity tagger},
+  author={Hoesen, Devin and Purwarianti, Ayu},
+  booktitle={2018 International Conference on Asian Language Processing (IALP)},
+  pages={35--38},
+  year={2018},
+  organization={IEEE}
+}
+"""
+
+_DESCRIPTION = """\
+The NERP dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites with five labels
+- PER (name of person)
+- LOC (name of location)
+- IND (name of product or brand)
+- EVT (name of the event)
+- FNB (name of food and beverage).
+NERP makes use of the IOB chunking format, just like the TermA dataset.
+"""
+
+_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
+
+_LICENSE = "Creative Common Attribution Share-Alike 4.0 International"
+
+_URLs = {
+    "train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/train_preprocess.txt",
+    "validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/valid_preprocess.txt",
+    "test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/test_preprocess_masked_label.txt",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class NerpDataset(datasets.GeneratorBasedBuilder):
+    """NERP is an NER tagging dataset contains about (train=6720,valid=840,test=840) sentences, with 11 classes."""
+
+    label_classes = ["B-PPL", "B-PLC", "B-EVT", "B-IND", "B-FNB", "I-PPL", "I-PLC", "I-EVT", "I-IND", "I-FNB", "O"]
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="nerp_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="NERP source schema",
+            schema="source",
+            subset_id="nerp",
+        ),
+        NusantaraConfig(
+            name="nerp_nusantara_seq_label",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="NERP Nusantara schema",
+            schema="nusantara_seq_label",
+            subset_id="nerp",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "nerp_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features({"index": datasets.Value("string"), "tokens": [datasets.Value("string")], "ner_tag": [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]:
+        train_tsv_path = Path(dl_manager.download_and_extract(_URLs["train"]))
+        validation_tsv_path = Path(dl_manager.download_and_extract(_URLs["validation"]))
+        test_tsv_path = Path(dl_manager.download_and_extract(_URLs["test"]))
+        data_files = {
+            "train": train_tsv_path,
+            "validation": validation_tsv_path,
+            "test": test_tsv_path,
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_files["validation"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_files["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path):
+        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"], "ner_tag": 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
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")