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indo4b.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.
+
+from posixpath import split
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME,
+                                       DEFAULT_SOURCE_VIEW_NAME, Tasks)
+import glob
+
+_DATASETNAME = "indo4b"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_CITATION = """\
+    @inproceedings{wilie-etal-2020-indonlu,
+        title = "{I}ndo{NLU}: Benchmark and Resources for Evaluating {I}ndonesian 
+            Natural Language Understanding",
+        author = "Wilie, Bryan  and
+          Vincentio, Karissa  and
+          Winata, Genta Indra  and
+          Cahyawijaya, Samuel  and
+          Li, Xiaohong  and
+          Lim, Zhi Yuan  and
+          Soleman, Sidik  and
+          Mahendra, Rahmad  and
+          Fung, Pascale  and
+          Bahar, Syafri  and
+          Purwarianti, Ayu",
+        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",
+        month = dec,
+        year = "2020",
+        address = "Suzhou, China",
+        publisher = "Association for Computational Linguistics",
+        url = "https://aclanthology.org/2020.aacl-main.85",
+        pages = "843--857",
+        abstract = "Although Indonesian is known to be the fourth most frequently used language 
+            over the internet, the research progress on this language in natural language processing (NLP) 
+            is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast 
+            resource for training, evaluation, and benchmarking on Indonesian natural language understanding 
+            (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to 
+            pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks 
+            lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian 
+            pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected 
+            from publicly available sources such as social media texts, blogs, news, and websites. 
+            We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, 
+            thus enabling everyone to benchmark their system performances.",
+    }
+"""
+
+_DESCRIPTION = """\
+    Indo4B is a large-scale Indonesian self-supervised pre-training corpus
+    consists of around 3.6B words, with around 250M sentences. The corpus
+    covers both formal and colloquial Indonesian sentences compiled from 
+    12 sources, of which two cover Indonesian colloquial language, eight
+    cover formal Indonesian language, and the rest have a mixed style of
+    both colloquial and formal.
+"""
+
+_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
+
+_LICENSE = "CC0"
+
+_LANGUAGES_MAP = {
+    "ind": "id",
+    "jav": "jv",
+    "sun": "su",
+}
+
+_URLS = {
+    "indo4b": "https://storage.googleapis.com/babert-pretraining/IndoNLU_finals/dataset/preprocessed/dataset_wot_uncased_blanklines.tar.xz",
+}
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+class Indo4B(datasets.GeneratorBasedBuilder):
+    """Indo4B is a large-scale Indonesian self-supervised pre-training corpus
+    consists of around 3.6B words, with around 250M sentences."""
+
+    DEFAULT_CONFIG_NAME = "indo4b_source"
+
+    
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indo4b_source",
+            version=_SOURCE_VERSION,
+            description="Indo4B source schema",
+            schema="source",
+            subset_id="indo4b",
+        ),
+        NusantaraConfig(
+            name="indo4b_nusantara_ssp",
+            version=_NUSANTARA_VERSION,
+            description="Indo4B Nusantara schema",
+            schema="nusantara_ssp",
+            subset_id="indo4b",
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "nusantara_ssp":
+            features = schemas.self_supervised_pretraining.features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        url = _URLS["indo4b"]
+        path = dl_manager.download_and_extract(url) + "/processed_uncased_blanklines"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": path,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        counter = 0
+        for txt_path in glob.glob(f'{filepath}/*.txt'):
+            with open(txt_path, encoding="utf-8") as f:
+                if self.config.schema == "source":
+                    for row in f:
+                        if row.strip() != "":
+                            yield (
+                                counter,
+                                {
+                                    "id": str(counter),
+                                    "text": row.strip(),
+                                },
+                            )
+                            counter += 1
+                elif self.config.schema == "nusantara_ssp":
+                    for row in f:
+                        if row.strip() != "":
+                            yield (
+                                counter,
+                                {
+                                    "id": str(counter),
+                                    "text": row.strip(),
+                                },
+                            )
+                            counter += 1