Upload indowiki.py with huggingface_hub

indowiki.py

@@ -0,0 +1,198 @@
+# 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 pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses
+
+_CITATION = """\
+@INPROCEEDINGS{ramli2022indokepler,
+    author={Ramli, Inigo and Krisnadhi, Adila Alfa and Prasojo, Radityo Eko},
+    booktitle={2022 7th International Workshop on Big Data and Information Security (IWBIS)},
+    title={IndoKEPLER, IndoWiki, and IndoLAMA: A Knowledge-enhanced Language Model, Dataset, and Benchmark for the Indonesian Language},
+    year={2022},
+    volume={},
+    number={},
+    pages={19-26},
+    doi={10.1109/IWBIS56557.2022.9924844}}
+"""
+
+_DATASETNAME = "indowiki"
+_DESCRIPTION = """\
+IndoWiki is a knowledge-graph dataset taken from WikiData and aligned with Wikipedia Bahasa Indonesia as it's corpus.
+"""
+_HOMEPAGE = "https://github.com/IgoRamli/IndoWiki"
+_LANGUAGES = ["ind"]
+_LICENSE = Licenses.MIT.value
+_LOCAL = False
+
+_URLS = {
+    "inductive": {
+        "train": "https://drive.google.com/uc?export=download&id=1S3vNx9By5CWKGkObjtXaI6Jr4xri2Tz3",
+        "valid": "https://drive.google.com/uc?export=download&id=1cP-zDIxp9a-Bw9uYd40K9IN-4wg4dOgy",
+        "test": "https://drive.google.com/uc?export=download&id=1pLcoJgYmgQiN4Gv9tRcI26zM7-OgHcuZ",
+    },
+    "transductive": {
+        "train": "https://drive.google.com/uc?export=download&id=1KXDVwboo1h2yk_kAqv7IPYnHXCK6g-6X",
+        "valid": "https://drive.google.com/uc?export=download&id=1eRwpuRPYOnA-7FZ-YNZjRJ2DHuJsfUIE",
+        "test": "https://drive.google.com/uc?export=download&id=1cy9FwDMB_U-js8P8u4IWolvNeIFkQVDh",
+    },
+    "text": "https://drive.usercontent.google.com/download?id=1YC4P_IPSo1AsEwm5Z_4GBjDdwCbvokxX&export=download&authuser=0&confirm=t&uuid=36aa95f5-e1b6-43c1-a34f-754d14d8b473&at=APZUnTWD7fwarBs4ZVRy_QdKbDXi%3A1709478240158",
+}
+
+# none of the tasks in schema
+# dataset is used to learn knowledge embedding
+_SUPPORTED_TASKS = []
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class IndoWiki(datasets.GeneratorBasedBuilder):
+    """IndoWiki knowledge base dataset from https://github.com/IgoRamli/IndoWiki"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        # inductive setting
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_inductive_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        # transductive setting
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "ent1": datasets.Value("string"),
+                    "ent2": datasets.Value("string"),
+                    "ent1_text": datasets.Value("string"),
+                    "ent2_text": datasets.Value("string"),
+                    "relation": datasets.Value("string"),
+                }
+            )
+
+        else:
+            raise NotImplementedError()
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        if "inductive" in self.config.name:
+            setting = "inductive"
+            data_paths = {
+                "inductive": {
+                    "train": Path(dl_manager.download_and_extract(_URLS["inductive"]["train"])),
+                    "valid": Path(dl_manager.download_and_extract(_URLS["inductive"]["valid"])),
+                    "test": Path(dl_manager.download_and_extract(_URLS["inductive"]["test"])),
+                },
+                "text": Path(dl_manager.download_and_extract(_URLS["text"])),
+            }
+        else:
+            setting = "transductive"
+            data_paths = {
+                "transductive": {
+                    "train": Path(dl_manager.download_and_extract(_URLS["transductive"]["train"])),
+                    "valid": Path(dl_manager.download_and_extract(_URLS["transductive"]["valid"])),
+                    "test": Path(dl_manager.download_and_extract(_URLS["transductive"]["test"])),
+                },
+                "text": Path(dl_manager.download_and_extract(_URLS["text"])),
+            }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "triplets_filepath": data_paths[setting]["train"],
+                    "text_filepath": data_paths["text"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "triplets_filepath": data_paths[setting]["test"],
+                    "text_filepath": data_paths["text"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "triplets_filepath": data_paths[setting]["valid"],
+                    "text_filepath": data_paths["text"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, triplets_filepath: Path, text_filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        # read triplets file
+        with open(triplets_filepath, "r", encoding="utf-8") as triplets_file:
+            triplets_data = triplets_file.readlines()
+        triplets_data = [s.strip("\n").split("\t") for s in triplets_data]
+
+        # read text description file
+        with open(text_filepath, "r", encoding="utf-8") as text_file:
+            text_data = text_file.readlines()
+        # dictionary of entity: text description of entity
+        text_dict = {s.split("\t")[0]: s.split("\t")[1].strip("\n") for s in text_data}
+
+        num_sample = len(triplets_data)
+
+        for i in range(num_sample):
+            if self.config.schema == "source":
+                example = {
+                    "id": str(i),
+                    "ent1": triplets_data[i][0],
+                    "ent2": triplets_data[i][2],
+                    "ent1_text": text_dict[triplets_data[i][0]],
+                    "ent2_text": text_dict[triplets_data[i][2]],
+                    "relation": triplets_data[i][1],
+                }
+
+            yield i, example