Closes SEACrowd#220 | Add Tagalog Profanity Dataloader (SEACrowd#240)

Closes SEACrowd#220

* Add Tagalog Profanity Dataloader

* Make feature types more consistent

* Apply suggestions from code review

* Fix errors based on changes

* Update seacrowd/sea_datasets/tgl_profanity/tgl_profanity.py

* Re-cast CLASS_LABELS to str

* Update seacrowd/sea_datasets/tgl_profanity/tgl_profanity.py

* Change supported task to abusive language detection

seacrowd/sea_datasets/tgl_profanity/tgl_profanity.py

@@ -0,0 +1,115 @@
+import csv
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from datasets.download.download_manager import DownloadManager
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@article{galinato-etal-2023-context,
+    title="Context-Based Profanity Detection and Censorship Using Bidirectional Encoder Representations from Transformers",
+    author="Galinato, Valfrid and Amores, Lawrence and Magsino, Gino Ben and Sumawang, David Rafael",
+    month="jan",
+    year="2023"
+    url="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4341604"
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["tgl"]
+_DATASETNAME = "tgl_profanity"
+_DESCRIPTION = """\
+This dataset contains 13.8k Tagalog sentences containing profane words, together
+with binary labels denoting whether or not the sentence conveys profanity /
+abuse / hate speech. The data was scraped from Twitter using a Python library
+called SNScrape and annotated manually by a panel of native Filipino speakers.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/mginoben/tagalog-profanity-dataset/"
+_LICENSE = Licenses.UNKNOWN.value
+_SUPPORTED_TASKS = [Tasks.ABUSIVE_LANGUAGE_PREDICTION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "1.0.0"
+_URLS = {
+    "train": "https://huggingface.co/datasets/mginoben/tagalog-profanity-dataset/resolve/main/train.csv",
+    "val": "https://huggingface.co/datasets/mginoben/tagalog-profanity-dataset/resolve/main/val.csv",
+}
+
+
+class TagalogProfanityDataset(datasets.GeneratorBasedBuilder):
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "text"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+    CLASS_LABELS = ["1", "0"]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("int64"),
+                }
+            )
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(label_names=self.CLASS_LABELS)
+        else:
+            raise ValueError(f"Invalid config name: {self.config.schema}")
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        data_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_files["val"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yield examples as (key, example) tuples"""
+        with open(filepath, encoding="utf-8") as f:
+            csv_reader = csv.reader(f, delimiter=",")
+            next(csv_reader, None)  # skip the headers
+            for idx, row in enumerate(csv_reader):
+                text, label = row
+                if self.config.schema == "source":
+                    example = {"text": text, "label": int(label)}
+                elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                    example = {"id": idx, "text": text, "label": int(label)}
+                yield idx, example