Upload malaysia_ai_hansard.py with huggingface_hub

malaysia_ai_hansard.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.
+
+"""
+The Malaysia AI Hansard Scrape dataset contains 142,766 PDFs from the Malaysian Parliament website.
+"""
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@article{malaysua_ai_hansard,
+  author    = {{Malaysia-AI}},
+  title     = {Crawl Malaysian Hansard},
+  year      = {2023},  % Change to the relevant year if known
+  url       = {https://huggingface.co/datasets/malaysia-ai/crawl-malaysian-hansard}
+
+}
+"""
+
+_DATASETNAME = "malaysia_ai_hansard"
+
+_DESCRIPTION = """\
+The Malaysia AI Hansard Scrape dataset contains 142,766 PDFs from the Malaysian Parliament website.
+(https://www.parlimen.gov.my/hansard-dewan-rakyat.html?uweb=dr).
+It includes a JSON file for each document with the text labeled "original", page numbers "no_page" and "actual_no_page", the document's "date", and the "url" of the original PDF.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/malaysia-ai/crawl-malaysian-hansard"
+
+_LANGUAGES = ["zlm"]
+
+_LICENSE = Licenses.APACHE_2_0.value
+
+_LOCAL = False
+
+
+_URLS = {
+    _DATASETNAME: "https://huggingface.co/datasets/malaysia-ai/crawl-malaysian-hansard",
+}
+
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class MalaysiaAIHansardDataset(datasets.GeneratorBasedBuilder):
+    """Malaysia AI Hansard Scrape dataset contains 142,766 PDFs from the Malaysian Parliament website."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="malaysia_ai_hansard_source",
+            version=SOURCE_VERSION,
+            description="malaysia_ai_hansard source schema",
+            schema="source",
+            subset_id="malaysia_ai_hansard",
+        ),
+        SEACrowdConfig(
+            name="malaysia_ai_hansard_seacrowd_ssp",
+            version=SEACROWD_VERSION,
+            description="malaysia_ai_hansard SEACrowd schema",
+            schema="seacrowd_ssp",
+            subset_id="malaysia_ai_hansard",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "malaysia_ai_hansard_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        # Create the source schema; this schema will keep all keys/information/labels as close to the original dataset as possible.
+
+        # You can arbitrarily nest lists and dictionaries.
+        # For iterables, use lists over tuples or `datasets.Sequence`
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "original": datasets.Value("string"),
+                    "cleaned": datasets.Value("string"),
+                    "no_page": datasets.Value("string"),
+                    "actual_no_page": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_ssp":
+            features = schemas.ssp_features
+
+        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."""
+
+        urls = _URLS[_DATASETNAME]
+        # data_dir = dl_manager.download_and_extract(urls)
+        # dl_manager not used since dataloader uses HF 'load_dataset'
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": urls,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        data = datasets.load_dataset("/".join(filepath.split("/")[-2:]), split="train")
+
+        for key, sample in enumerate(data):
+            if self.config.schema == "source":
+                yield key, {
+                    "original": sample["original"],
+                    "cleaned": sample["cleaned"],
+                    "no_page": sample["no_page"],
+                    "actual_no_page": sample["actual_no_page"],
+                    "date": sample["date"],
+                    "url": sample["url"],
+                }
+
+            elif self.config.schema == "seacrowd_ssp":
+                yield key, {
+                    "id": key,
+                    "text": sample["cleaned"],
+                }