Closes #63 | Create dataloader for MongabayConservation

seacrowd/sea_datasets/mongabay/README.md

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+### PROVIDED DATA
+- "mongabay-tag-classification"
+- "mongabay-sentiment-classification"
+
+### DATA CALLING EXAMPLE
+
+- seacrowd format
+
+    - mongabay-tag-classification
+        ```
+        from datasets import load_dataset
+
+        data = load_dataset("seacrowd/sea_datasets/mongabay/mongabay.py", name="mongabay-tag-classification_seacrowd_t2t")
+
+        >>> data["train"][0]
+        {'id': '0', 'text_1': 'Pandemi, Momentum bagi Negara Serius Lindungi Hak Masyarakat Adat | ...', 'text_2': '[0.1111111119389534, 0.0, 0.0, 0.0, 0.1111111119389534, 0.0, 0.0, 0.0, 0.0, 0.1111111119389534, 0.1111111119389534, 0.0, 0.1111111119389534, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1111111119389534, 0.0, 0.1111111119389534, 0.0, 0.1111111119389534, 0.0, 0.0, 0.1111111119389534, 0.0, 0.0, 0.0]', 'text_1_name': 'text', 'text_2_name': 'weak_label'}
+        ```
+
+    - mongabay-sentiment-classification
+        ```
+        from datasets import load_dataset
+
+        data = load_dataset("seacrowd/sea_datasets/mongabay/mongabay.py", name="mongabay-sentiment-classification_seacrowd_t2t")
+
+        >>> data["train"][0]
+        {'id': '0', 'text_1': 'Pandemi, Momentum bagi Negara Serius Lindungi Hak Masyarakat Adat | ...', 'text_2': '[1.0, 1.4414156535025313e-09, 1.320]', 'text_1_name': 'text', 'text_2_name': 'weak_label'}
+        ```
+
+- source format
+  - mongabay-tag-classification
+    ```
+    from datasets import load_dataset
+
+    data = load_dataset("seacrowd/sea_datasets/mongabay/mongabay.py", name="mongabay-tag-classification_source")
+
+    data['train'][0]
+    {'text': 'Pandemi, Momentum bagi Negara Serius Lindungi Hak Masyarakat Adat | ...', 'label': '[0.1111111119389534, 0.0, 0.0, 0.0, 0.1111111119389534, 0.0, 0.0, 0.0, 0.0, 0.1111111119389534, 0.1111111119389534, 0.0, 0.1111111119389534, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1111111119389534, 0.0, 0.1111111119389534, 0.0, 0.1111111119389534, 0.0, 0.0, 0.1111111119389534, 0.0, 0.0, 0.0]'}
+    ```
+  - mongabay-seniment-classification
+    ```
+    from datasets import load_dataset
+
+    data = load_dataset("seacrowd/sea_datasets/mongabay/mongabay.py", name="mongabay-sentiment-classification_source")
+    {'text': 'Pandemi, Momentum bagi Negara Serius Lindungi Hak Masyarakat Adat | ...', 'tags': "['Aparatur Sipil Negara' 'masyarakat desa' 'konflik' 'perusahaan' 'tambang']", 'label': '[1.0, 1.4414156535025313e-09, 1.3204033422198336e-09]'}
+    ```

seacrowd/sea_datasets/mongabay/mongabay.py

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+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 Tasks
+
+_CITATION = """\
+@misc{fransiska2023utilizing,
+      title={Utilizing Weak Supervision To Generate Indonesian Conservation Dataset},
+      author={Mega Fransiska and Diah Pitaloka and Saripudin and Satrio Putra and Lintang Sutawika},
+      year={2023},
+      eprint={2310.11258},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "mongabay"
+
+_DESCRIPTION = """\
+Conservation dataset that was collected from mongabay.co.id contains
+ topic-classification task (multi-label format) and sentiment classification.
+ The dataset consists of 31 important topics that are commonly found in
+ Indonesian conservation articles or general news, and each article can
+ belong to more than one topic. After gathering topics for each article,
+ each article will be classified into one of author's sentiments
+ (positive, neutral, negative) based on related topics.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "The Unlicense (unlicense)"
+
+_URLS = {"mongabay-tag-classification": "https://huggingface.co/datasets/Datasaur/Mongabay-tags-classification", "mongabay-sentiment-classification": "https://huggingface.co/datasets/Datasaur/Mongabay-sentiment-classification"}
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+_SUPPORTED_TASKS = [Tasks.PARAPHRASING]
+
+_LANGUAGES = ["ind"]
+
+
+class Mongabay(datasets.GeneratorBasedBuilder):
+    """mongabay is a dataset sourced from mongabay.co.id's Indonesian articles from 2012-2023. Each article is chunked to maximum 512 tokens to ease experiment process"""
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="mongabay-tag-classification_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="mongabay-tag-classification source schema",
+            schema="source",
+            subset_id="mongabay-tag-classification",
+        ),
+        SEACrowdConfig(
+            name="mongabay-tag-classification_seacrowd_t2t",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description="mongabay-tag-classification SEACrowd schema",
+            schema="seacrowd_t2t",
+            subset_id="mongabay-tag-classification",
+        ),
+        SEACrowdConfig(
+            name="mongabay-sentiment-classification_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="mongabay-sentiment-classification source schema",
+            schema="source",
+            subset_id="mongabay-sentiment-classification",
+        ),
+        SEACrowdConfig(
+            name="mongabay-sentiment-classification_seacrowd_t2t",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description="mongabay-sentiment-classification SEACrowd schema",
+            schema="seacrowd_t2t",
+            subset_id="mongabay-sentiment-classification",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            if "mongabay-sentiment-classification" in self.config.name:
+                features = datasets.Features(
+                    {
+                        "text": datasets.Value("string"),
+                        "tags": datasets.Value("string"),
+                        "label": datasets.Value("string"),
+                    }
+                )
+            elif "mongabay-tag-classification" in self.config.name:
+                features = datasets.Features(
+                    {
+                        "text": datasets.Value("string"),
+                        "label": datasets.Value("string"),
+                    }
+                )
+        elif self.config.schema == "seacrowd_t2t":
+            features = schemas.text2text_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]:
+        name = self.config.name.replace("_" + self.config.schema, "")
+        url = _URLS[name]
+        filename = "/".join(url.split("/")[-2:])
+
+        output = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filename": filename,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filename": filename,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filename": filename,
+                    "split": "test",
+                },
+            ),
+        ]
+
+        return output
+
+    def _generate_examples(self, filename: Path, split: str) -> Tuple[int, Dict]:
+        """Yield examples as tuples of idx, (text, tags[optional], and label)"""
+
+        try:
+            dataset = datasets.load_dataset(filename)[split]
+
+            if self.config.schema == "source":
+                for idx, row in enumerate(dataset):
+                    yield idx, row
+
+            elif self.config.schema == "seacrowd_t2t":
+                for idx, row in enumerate(dataset):
+                    sample = {"id": str(idx), "text_1": row["text"], "text_2": row["label"], "text_1_name": "text", "text_2_name": "weak_label"}
+                    yield idx, sample
+        except datasets.exceptions.DatasetGenerationError as e:
+            print(e)