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limesoda.py

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+import json
+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 = """\
+@INPROCEEDINGS{9678187,
+  author={Payoungkhamdee, Patomporn and Porkaew, Peerachet and Sinthunyathum, Atthasith and Songphum, Phattharaphon and Kawidam, Witsarut and Loha-Udom, Wichayut and Boonkwan, Prachya and Sutantayawalee, Vipas},
+  booktitle={2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)},
+  title={LimeSoda: Dataset for Fake News Detection in Healthcare Domain},
+  year={2021},
+  volume={},
+  number={},
+  pages={1-6},
+  doi={10.1109/iSAI-NLP54397.2021.9678187}}
+"""
+
+_DATASETNAME = "limesoda"
+
+_DESCRIPTION = """\
+Thai fake news dataset in the healthcare domain consisting of curate and manually annotated 7,191 documents
+(only 4,141 documents contain token labels and are used as a test set of the baseline models).
+Each document in the dataset is classified as fact, fake, or undefined.
+"""
+
+_HOMEPAGE = "https://github.com/byinth/LimeSoda"
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LANGUAGES = ["tha"]
+_LOCAL = False
+
+_URLS = {
+    "split": {
+        "train": "https://raw.githubusercontent.com/byinth/LimeSoda/main/dataset_train_wo_tokentags_v1/train_v1.jsonl",
+        "val": "https://raw.githubusercontent.com/byinth/LimeSoda/main/dataset_train_wo_tokentags_v1/val_v1.jsonl",
+        "test": "https://raw.githubusercontent.com/byinth/LimeSoda/main/dataset_train_wo_tokentags_v1/test_v1.jsonl",
+    },
+    "raw": "https://raw.githubusercontent.com/byinth/LimeSoda/main/LimeSoda/Limesoda.jsonl",
+}
+
+_SUPPORTED_TASKS = [Tasks.HOAX_NEWS_CLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class LimeSodaDataset(datasets.GeneratorBasedBuilder):
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description="limesoda source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_split_source",
+            version=SOURCE_VERSION,
+            description="limesoda source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_split",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_text",
+            version=SEACROWD_VERSION,
+            description="limesoda SEACrowd schema",
+            schema="seacrowd_text",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_split_seacrowd_text",
+            version=SEACROWD_VERSION,
+            description="limesoda: split SEACrowd schema",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}_split",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            if self.config.subset_id == "limesoda":
+                features = datasets.Features(
+                    {
+                        "id": datasets.Value("string"),
+                        "title": datasets.Value("string"),
+                        "detail": datasets.Sequence(datasets.Value("string")),
+                        "title_token_tags": datasets.Value("string"),
+                        "detail_token_tags": datasets.Sequence(datasets.Value("string")),
+                        "document_tag": datasets.Value("string"),
+                    }
+                )
+            else:
+                features = datasets.Features({"id": datasets.Value("string"), "text": datasets.Value("string"), "document_tag": datasets.Value("string")})
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(["Fact News", "Fake News", "Undefined"])
+
+        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."""
+        path_dict = dl_manager.download_and_extract(_URLS)
+        if self.config.subset_id == "limesoda":
+            raw_path = path_dict["raw"]
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": raw_path,
+                    },
+                ),
+            ]
+        elif self.config.subset_id == "limesoda_split":
+            train_path, val_path, test_path = path_dict["split"]["train"], path_dict["split"]["val"], path_dict["split"]["test"]
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": train_path,
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": test_path,
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": val_path,
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        with open(filepath, "r") as f:
+            entries = [json.loads(line) for line in f.readlines()]
+        if self.config.schema == "source":
+            if self.config.subset_id == "limesoda":
+                for i, row in enumerate(entries):
+                    ex = {"id": str(i), "title": row["Title"], "detail": row["Detail"], "title_token_tags": row["Title Token Tags"], "detail_token_tags": row["Detail Token Tags"], "document_tag": row["Document Tag"]}
+                    yield i, ex
+            else:
+                for i, row in enumerate(entries):
+                    ex = {"id": str(i), "text": row["Text"], "document_tag": row["Document Tag"]}
+                    yield i, ex
+        elif self.config.schema == "seacrowd_text":
+            for i, row in enumerate(entries):
+                ex = {
+                    "id": str(i),
+                    "text": row["Detail"] if self.config.subset_id == "limesoda" else row["Text"],
+                    "label": row["Document Tag"],
+                }
+                yield i, ex