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liputan6.py

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+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+from nusacrowd.utils import schemas
+import json
+
+_CITATION = """\
+@inproceedings{koto2020liputan6,
+  title={Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
+  author={Koto, Fajri and Lau, Jey Han and Baldwin, Timothy},
+  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},
+  pages={598--608},
+  year={2020}
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "liputan6"
+
+_DESCRIPTION = """
+A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs.
+"""
+
+_HOMEPAGE = "https://github.com/fajri91/sum_liputan6"
+
+_LICENSE = "CC-BY-SA 4.0"
+
+_URLS = {
+    _DATASETNAME: "https://storage.googleapis.com/babert-pretraining/IndoNLG_finals/downstream_task/downstream_task_datasets.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class Liputan6(datasets.GeneratorBasedBuilder):
+    """A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs."""
+
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+    
+    TYPE_LIST = ['canonical', 'xtreme']
+    BUILDER_CONFIGS = (
+        [
+            NusantaraConfig(
+                name="liputan6_{fold_name}_source".format(fold_name=i),
+                version=_SOURCE_VERSION,
+                description="liputan6 source schema",
+                schema="source",
+                subset_id="liputan6_{fold_name}".format(fold_name=i),
+            ) for i in TYPE_LIST
+        ]
+        +
+        [
+            NusantaraConfig(
+            name="liputan6_{fold_name}_nusantara_t2t".format(fold_name=i),
+            version=_NUSANTARA_VERSION,
+            description="liputan6 Nusantara schema",
+            schema="nusantara_t2t",
+            subset_id="liputan6_{fold_name}".format(fold_name=i),
+        ) for i in TYPE_LIST
+        ]
+    )
+    DEFAULT_CONFIG_NAME = "liputan6_canonical_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+               {
+                   "document": datasets.Value("string"),
+                   "id": datasets.Value("string"),
+                   "summary": datasets.Value("string")
+               }
+            )
+
+        elif self.config.schema == "nusantara_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _get_fold_name(self):
+        subset_id = self.config.subset_id
+        idx_fold = subset_id.index("_")
+        file_id = subset_id[(idx_fold + 1):]
+        return file_id
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        fold_name = self._get_fold_name()
+
+        urls = _URLS[_DATASETNAME]
+
+        data_dir = Path(dl_manager.download_and_extract(urls))
+
+        location = {
+            "train": "IndoNLG_downstream_tasks/liputan6/{fold_name}_train.json",
+            "test": "IndoNLG_downstream_tasks/liputan6/{fold_name}_test.json",
+            "dev": "IndoNLG_downstream_tasks/liputan6/{fold_name}_dev.json"
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, location["train"].format(fold_name=fold_name)),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, location["test"].format(fold_name=fold_name)),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, location["dev"].format(fold_name=fold_name)),
+                    "split": "dev",
+                },
+            ),
+        ]
+    
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+
+        if self.config.schema == "source":
+            
+            if "xtreme_train.json" in filepath:
+                with open(filepath) as f:
+                    lines = f.read().split("{")
+                    LEN = len(lines)
+                    for i, line in enumerate(lines):
+                        if 0 < i < LEN-1:
+                            idx = line.index("}")
+                            line = "{"+line[:idx+1]
+                            each_data = json.loads(line)
+                            ex = {
+                                "id": each_data["id"],
+                                "document": each_data['text'],
+                                "summary": each_data['label']
+                            }
+                            yield each_data["id"], ex
+
+            else:
+                with open(filepath) as f:
+                    data =  json.load(f)
+                    for i, each_data in enumerate(data):
+                        ex = {
+                            "id": each_data["id"],
+                            "document": each_data['text'],
+                            "summary": each_data['label']
+                        }
+                        yield each_data["id"], ex
+
+        elif self.config.schema == "nusantara_t2t":
+            if "xtreme_train.json" in filepath:
+                with open(filepath) as f:
+                    lines = f.read().split("{")
+                    LEN = len(lines)
+                    for i, line in enumerate(lines):
+                        if 0 < i < LEN-1:
+                            idx = line.index("}")
+                            line = "{"+line[:idx+1]
+                            each_data = json.loads(line)
+                            ex = {
+                                "id": each_data["id"],
+                                "text_1": each_data['text'],
+                                "text_2": each_data['label'],
+                                "text_1_name": "document",
+                                "text_2_name": "summary"
+                            }
+                            yield each_data["id"], ex
+
+            else:
+                with open(filepath) as f:
+                    data =  json.load(f)
+                    for i, each_data in enumerate(data):
+                        ex = {
+                            "id": each_data["id"],
+                            "text_1": each_data['text'],
+                            "text_2": each_data['label'],
+                            "text_1_name": "document",
+                            "text_2_name": "summary"
+                        }
+                        yield each_data["id"], ex
+            
+            
+