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id_multilabel_hs.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{ibrohim-budi-2019-multi,
+    title = "Multi-label Hate Speech and Abusive Language Detection in {I}ndonesian {T}witter",
+    author = "Ibrohim, Muhammad Okky  and
+      Budi, Indra",
+    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
+    month = aug,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W19-3506",
+    doi = "10.18653/v1/W19-3506",
+    pages = "46--57",
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "id_multilabel_hs"
+
+_DESCRIPTION = """\
+The ID_MULTILABEL_HS dataset is collection of 13,169 tweets in Indonesian language,
+designed for hate speech detection NLP task. This dataset is combination from previous research and newly crawled data from Twitter.
+This is a multilabel dataset with label details as follows:
+-HS : hate speech label;
+-Abusive : abusive language label;
+-HS_Individual : hate speech targeted to an individual;
+-HS_Group : hate speech targeted to a group;
+-HS_Religion : hate speech related to religion/creed;
+-HS_Race : hate speech related to race/ethnicity;
+-HS_Physical : hate speech related to physical/disability;
+-HS_Gender : hate speech related to gender/sexual orientation;
+-HS_Gender : hate related to other invective/slander;
+-HS_Weak : weak hate speech;
+-HS_Moderate : moderate hate speech;
+-HS_Strong : strong hate speech.
+"""
+
+_HOMEPAGE = "https://aclanthology.org/W19-3506/"
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+_URLS = {
+    _DATASETNAME: "https://raw.githubusercontent.com/okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection/master/re_dataset.csv",
+}
+_SUPPORTED_TASKS = [Tasks.ASPECT_BASED_SENTIMENT_ANALYSIS]
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class IdAbusive(datasets.GeneratorBasedBuilder):
+    """The ID_MULTILABEL_HS dataset is multi-label hate speech and abusive language detection in Indonesian tweets"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="id_multilabel_hs_source",
+            version=SOURCE_VERSION,
+            description="ID Multilabel HS source schema",
+            schema="source",
+            subset_id="id_multilabel_hs",
+        ),
+        NusantaraConfig(
+            name="id_multilabel_hs_nusantara_text_multi",
+            version=NUSANTARA_VERSION,
+            description="ID Multilabel HS Nusantara schema",
+            schema="nusantara_text_multi",
+            subset_id="id_multilabel_hs",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "id_multilabel_hs_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({
+                "tweet": datasets.Value("string"), 
+                "HS": datasets.Value("bool"),
+                "Abusive": datasets.Value("bool"), 
+                "HS_Individual": datasets.Value("bool"), 
+                "HS_Group": datasets.Value("bool"), 
+                "HS_Religion": datasets.Value("bool"), 
+                "HS_Race": datasets.Value("bool"), 
+                "HS_Physical": datasets.Value("bool"), 
+                "HS_Gender": datasets.Value("bool"), 
+                "HS_Other": datasets.Value("bool"), 
+                "HS_Weak": datasets.Value("bool"), 
+                "HS_Moderate": datasets.Value("bool"), 
+                "HS_Strong": datasets.Value("bool"),
+                })
+        elif self.config.schema == "nusantara_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."""
+        # Dataset does not have predetermined split, putting all as TRAIN
+        urls = _URLS[_DATASETNAME]
+        base_dir = Path(dl_manager.download_and_extract(urls))
+        data_files = {"train": base_dir}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_files["train"],
+                    "split": "train",
+                },
+            ),
+        ]
+
+    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 = ["HS", "Abusive", "HS_Individual", "HS_Group", "HS_Religion", "HS_Race", "HS_Physical", "HS_Gender", "HS_Other", "HS_Weak", "HS_Moderate", "HS_Strong"]
+        df = pd.read_csv(filepath, encoding="ISO-8859-1").reset_index()
+        df.columns = ["id", "tweet"] + label_cols
+
+        if self.config.schema == "source":
+            for row in df.itertuples():
+                ex = {
+                    "tweet": row.tweet,
+                }
+                for label in label_cols:
+                    ex[label] = getattr(row, label)
+                yield row.id, ex
+
+        elif self.config.schema == "nusantara_text_multi":
+            for row in df.itertuples():
+                ex = {
+                    "id": str(row.id),
+                    "text": row.tweet,
+                    "labels": [label for label in row[3:]],
+                }
+                yield row.id, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")