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uit_vsmec.py

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+# coding=utf-8
+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 Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{ho2020emotion,
+  title={Emotion recognition for vietnamese social media text},
+  author={Ho, Vong Anh and Nguyen, Duong Huynh-Cong and Nguyen, Danh Hoang and Pham, Linh Thi-Van and Nguyen, Duc-Vu and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy},
+  booktitle={Computational Linguistics: 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11--13, 2019, Revised Selected Papers 16},
+  pages={319--333},
+  year={2020},
+  organization={Springer}
+}
+"""
+
+_DATASETNAME = "uit_vsmec"
+
+_DESCRIPTION = """\
+This dataset consists of Vietnamese Facebook comments that were manually annotated for sentiment.
+There are seven possible emotion labels: enjoyment, sadness, fear, anger, disgust, surprise or other (for comments with no or neutral emotions).
+Two rounds of manual annotations were done to train annotators with tagging and editing guidelines.
+Annotation was performed until inter-annotator agreement reached at least 80%.
+"""
+
+_HOMEPAGE = "https://drive.google.com/drive/folders/1HooABJyrddVGzll7fgkJ6VzkG_XuWfRu"
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LANGUAGES = ["vie"]
+
+_LOCAL = False
+
+_URLS = {
+    "train": "https://docs.google.com/spreadsheets/export?id=10VYzfK7JLg-vfmqH0UmKX62z_uaXU-Hp&format=csv",
+    "valid": "https://docs.google.com/spreadsheets/export?id=1EsSFZ94fj2yTvFKO6EyxM0wBRcG0s1KE&format=csv",
+    "test": "https://docs.google.com/spreadsheets/export?id=1D16FCKKgJ0T6t2aSA3biWVwvD9fa4G9a&format=csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.EMOTION_CLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class UITVSMECDataset(datasets.GeneratorBasedBuilder):
+    """
+    This is the main class of SEACrowd dataloader for UIT-VSMEC, focusing on emotion/sentiment classification task.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_text",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+    LABEL_NAMES = ["Other", "Disgust", "Enjoyment", "Anger", "Surprise", "Sadness", "Fear"]
+    DEFAULT_CONFIG_NAME = "uit_vsmec_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({"Emotion": datasets.Value("string"), "Sentence": datasets.Value("string")})
+
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(self.LABEL_NAMES)
+
+        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_dict = dl_manager.download_and_extract(_URLS)
+        train_path, valid_path, test_path = path_dict["train"], path_dict["valid"], path_dict["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": valid_path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        df = pd.read_csv(filepath).reset_index()
+        if self.config.schema == "source":
+            for row in df.itertuples():
+                ex = {"Emotion": row.Emotion, "Sentence": row.Sentence}
+                yield row.index, ex
+
+        elif self.config.schema == "seacrowd_text":
+            for row in df.itertuples():
+                ex = {"id": str(row.index), "text": row.Sentence, "label": self.LABEL_NAMES.index(row.Emotion)}
+                yield row.index, ex