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indo4b_plus.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)
+
+_DATASETNAME = "indo4b_plus"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LOCAL = False
+_LANGUAGES = ["ind", "sun", "jav"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_CITATION = """\
+    @inproceedings{cahyawijaya-etal-2021-indonlg,
+        title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation",
+        author = "Cahyawijaya, Samuel  and
+          Winata, Genta Indra  and
+          Wilie, Bryan  and
+          Vincentio, Karissa  and
+          Li, Xiaohong  and
+          Kuncoro, Adhiguna  and
+          Ruder, Sebastian  and
+          Lim, Zhi Yuan  and
+          Bahar, Syafri  and
+          Khodra, Masayu  and
+          Purwarianti, Ayu  and
+          Fung, Pascale",
+        booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+        month = nov,
+        year = "2021",
+        address = "Online and Punta Cana, Dominican Republic",
+        publisher = "Association for Computational Linguistics",
+        url = "https://aclanthology.org/2021.emnlp-main.699",
+        doi = "10.18653/v1/2021.emnlp-main.699",
+        pages = "8875--8898",
+        abstract = "Natural language generation (NLG) benchmarks provide an important avenue to measure progress 
+        and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource 
+        languages poses a challenging barrier for building NLG systems that work well for languages with limited 
+        amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG)
+        progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. 
+        Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important 
+        use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, 
+        and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, 
+        Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. 
+        We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth
+        the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes 
+        the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference 
+        at very low-resource languages like Javanese and Sundanese.",
+    }
+"""
+
+_DESCRIPTION = """\
+    Indo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus. 
+    Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local languages to the corpus, i.e., Sundanese and Javanese.
+    Indo4B-Plus adds 82,582,025 words (∼2.07%) of Sundanese sentences and 331,041,877 words (∼8.29%) of Javanese
+"""
+
+_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
+
+_LICENSE = "CC0"
+
+_LANGUAGES_MAP = {
+    "ind": "id",
+    "jav": "jv",
+    "sun": "su",
+}
+
+_URLS = {
+    "indo4b": "https://storage.googleapis.com/babert-pretraining/IndoNLG_finals/IndoNLG_ALL_new_dataset_preprocessed_uncased.txt.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+class Indo4BPlus(datasets.GeneratorBasedBuilder):
+    """Indo4B-Plus is a large-scale Indonesian self-supervised pre-training corpus consists
+    of around 4B words, covering three languages, i.e., Indonesian, Sundanese, and Javanese."""
+
+    DEFAULT_CONFIG_NAME = "indo4b_plus_source"
+
+    
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indo4b_plus_source",
+            version=_SOURCE_VERSION,
+            description="Indo4B-Plus source schema",
+            schema="source",
+            subset_id="indo4b_plus",
+        ),
+        NusantaraConfig(
+            name="indo4b_plus_nusantara_ssp",
+            version=_NUSANTARA_VERSION,
+            description="Indo4B-Plus Nusantara schema",
+            schema="nusantara_ssp",
+            subset_id="indo4b_plus",
+        ),
+    ]
+
+    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) + "/IndoNLG_ALL_new_dataset_preprocessed_uncased.txt"
+
+        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."""
+
+        with open(filepath, encoding="utf-8") as f:
+            if self.config.schema == "source":
+                for counter, row in enumerate(f):
+                    if row.strip() != "":
+                        yield (
+                            counter,
+                            {
+                                "id": str(counter),
+                                "text": row.strip(),
+                            },
+                        )
+            elif self.config.schema == "nusantara_ssp":
+                for counter, row in enumerate(f):
+                    if row.strip() != "":
+                        yield (
+                            counter,
+                            {
+                                "id": str(counter),
+                                "text": row.strip(),
+                            },
+                        )