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thai_depression.py

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+import json
+from pathlib import Path
+from typing import List
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import DEFAULT_SEACROWD_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Licenses, Tasks
+
+_DATASETNAME = "thai_depression"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
+
+_LANGUAGES = ["tha"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+_CITATION = """\
+@inproceedings{hamalainen-etal-2021-detecting,
+    title = "Detecting Depression in Thai Blog Posts: a Dataset and a Baseline",
+    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
+      Patpong, Pattama  and
+      Alnajjar, Khalid  and
+      Partanen, Niko  and
+      Rueter, Jack},
+    editor = "Xu, Wei  and
+      Ritter, Alan  and
+      Baldwin, Tim  and
+      Rahimi, Afshin",
+    booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
+    month = nov,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.wnut-1.3",
+    doi = "10.18653/v1/2021.wnut-1.3",
+    pages = "20--25",
+    abstract = "We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs.
+    We experiment with two different LSTM based models and two different BERT based models. We achieve a 77.53%% accuracy with a Thai BERT model in detecting depression.
+    This establishes a good baseline for future researcher on the same corpus. Furthermore, we identify a need for Thai embeddings that have been trained on a more varied corpus than Wikipedia.
+    Our corpus, code and trained models have been released openly on Zenodo.",
+}
+"""
+
+_DESCRIPTION = """\
+We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs.
+We experiment with two different LSTM based models and two different BERT based models. We achieve a 77.53%% accuracy with a Thai BERT model in detecting depression.
+This establishes a good baseline for future researcher on the same corpus. Furthermore, we identify a need for Thai embeddings that have been trained on a more varied corpus than Wikipedia.
+Our corpus, code and trained models have been released openly on Zenodo.
+"""
+
+_HOMEPAGE = "https://zenodo.org/records/4734552"
+
+_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
+
+_URLs = "https://zenodo.org/records/4734552/files/data.zip?download=1"
+
+_SUPPORTED_TASKS = [Tasks.EMOTION_CLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ThaiDepressionDataset(datasets.GeneratorBasedBuilder):
+    """Thai depression detection dataset."""
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} seacrowd schema",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(["depression", "no_depression"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        path = Path(dl_manager.download_and_extract(_URLs))
+        data_files = {
+            "train": path / "splits/train.json",
+            "test": path / "splits/test.json",
+            "valid": path / "splits/valid.json",
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_files["valid"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_files["test"]},
+            ),
+        ]
+
+    def _parse_and_label(self, file_path):
+        with open(file_path, "r", encoding="utf-8") as file:
+            data = json.load(file)
+
+        parsed_data = []
+        for item in data:
+            parsed_data.append({"text": item[0], "label": item[1]})
+
+        return parsed_data
+
+    def _generate_examples(self, filepath: Path):
+        print("Reading ", filepath)
+        for id, row in enumerate(self._parse_and_label(filepath)):
+            if self.config.schema == "source":
+                yield id, {"text": row["text"], "label": row["label"]}
+            elif self.config.schema == "seacrowd_text":
+                yield id, {"id": str(id), "text": row["text"], "label": row["label"]}
+            else:
+                raise ValueError(f"Invalid config: {self.config.name}")