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netifier.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
+
+_CITATION = """\
+"""
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_DATASETNAME = "netifier"
+
+_DESCRIPTION = """\
+Netifier dataset is a collection of scraped posts on famous social media sites in Indonesia,
+such as Instagram, Twitter, and Kaskus aimed to do multi-label toxicity classification.
+The dataset consists of 7,773 texts. The author manually labelled ~7k samples into 4 categories:
+pornography, hate speech, racism, and radicalism.
+"""
+
+_HOMEPAGE = "https://github.com/ahmadizzan/netifier"
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+_URLS = {_DATASETNAME: {"train": "https://raw.githubusercontent.com/ahmadizzan/netifier/master/data/processed/train.csv", "test": "https://raw.githubusercontent.com/ahmadizzan/netifier/master/data/processed/test.csv"}}
+_SUPPORTED_TASKS = [Tasks.ASPECT_BASED_SENTIMENT_ANALYSIS]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class Netifier(datasets.GeneratorBasedBuilder):
+    """Netifier dataset is a collection of scraped posts on famous social media sites in Indonesia,
+    such as Instagram, Twitter, and Kaskus aimed to do multi-label toxicity classification."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="netifier_source",
+            version=SOURCE_VERSION,
+            description="Netifier source schema",
+            schema="source",
+            subset_id="netifier",
+        ),
+        SEACrowdConfig(
+            name="netifier_seacrowd_text_multi",
+            version=SEACROWD_VERSION,
+            description="Netifier Nusantara schema",
+            schema="seacrowd_text_multi",
+            subset_id="netifier",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "netifier_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "pornography": datasets.Value("bool"),
+                    "blasphemy_racism_discrimination": datasets.Value("bool"),
+                    "radicalism": datasets.Value("bool"),
+                    "defamation": datasets.Value("bool"),
+                }
+            )
+        elif self.config.schema == "seacrowd_text_multi":
+            features = schemas.text_multi_features([0, 1])
+
+        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]
+        train_data = Path(dl_manager.download(urls["train"]))
+        test_data = Path(dl_manager.download(urls["test"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_data,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_data,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        # Dataset does not have id, using row index as id
+        label_cols = ["pornography", "blasphemy_racism_discrimination", "radicalism", "defamation"]
+        df = pd.read_csv(filepath, encoding="ISO-8859-1").reset_index()
+        df.columns = ["id", "original_text", "text"] + label_cols
+
+        if self.config.schema == "source":
+            for row in df.itertuples():
+                ex = {
+                    "text": row.text,
+                }
+                for label in label_cols:
+                    ex[label] = getattr(row, label)
+                yield row.id, ex
+
+        elif self.config.schema == "seacrowd_text_multi":
+            for row in df.itertuples():
+                ex = {
+                    "id": str(row.id),
+                    "text": row.text,
+                    "labels": [label for label in row[4:]],
+                }
+                yield row.id, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")