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xed.py

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+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{ohman2020xed,
+  title={{XED}: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},
+  author={{\"O}hman, Emily and P{`a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},
+  booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
+  year={2020}
+}
+"""
+_DATASETNAME = "xed"
+
+_DESCRIPTION = """\
+This is the XED dataset. The dataset consists of emotion annotated movie subtitles
+from OPUS. We use Plutchik's 8 core emotions to annotate. The data is multilabel.
+The original annotations have been sourced for mainly English and Finnish, with the
+rest created using annotation projection to aligned subtitles in 41 additional languages,
+with 31 languages included in the final dataset (more than 950 lines of annotated subtitle
+lines). The dataset is an ongoing project with forthcoming additions such as machine translated datasets.
+"""
+
+_HOMEPAGE = "https://github.com/Helsinki-NLP/XED"
+
+_LANGUAGES = ["ind", "vie"]
+
+# This License is from the bottom of homepage's README not Unknown (as from Issues)
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LOCAL = False
+
+_URLS = {"ind": "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/Projections/id-projections.tsv", "vie": "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/Projections/vi-projections.tsv"}
+
+# Because of the multi-label attribute, I choose ASPECT_BASED_SENTIMENT_ANALYSIS than SENTIMENT_ANALYSIS
+_SUPPORTED_TASKS = [Tasks.ASPECT_BASED_SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class XEDDataset(datasets.GeneratorBasedBuilder):
+    """
+    This is the XED dataset. The dataset consists of emotion annotated movie subtitles
+    from OPUS. We use Plutchik's 8 core emotions to annotate. The data is multilabel.
+    The original annotations have been sourced for mainly English and Finnish, with the
+    rest created using annotation projection to aligned subtitles in 41 additional languages,
+    with 31 languages included in the final dataset (more than 950 lines of annotated subtitle
+    lines). The dataset is an ongoing project with forthcoming additions such as machine translated datasets.
+    """
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{LANG}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} {LANG} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{LANG}",
+        )
+        for LANG in _LANGUAGES
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{LANG}_seacrowd_text_multi",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} {LANG} SEACrowd schema",
+            schema="seacrowd_text_multi",
+            subset_id=f"{_DATASETNAME}_{LANG}",
+        )
+        for LANG in _LANGUAGES
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_ind_source"
+    _LABELS = ["Anger", "Anticipation", "Disgust", "Fear", "Joy", "Sadness", "Surprise", "Trust"]
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features({"Sentence": datasets.Value("string"), "Emotions": datasets.Sequence(feature=datasets.ClassLabel(names=self._LABELS))})
+
+        elif self.config.schema == "seacrowd_text_multi":
+            features = schemas.text_multi_features(self._LABELS)
+
+        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."""
+
+        language = self.config.name.split("_")[1]
+
+        if language in _LANGUAGES:
+            data_path = Path(dl_manager.download_and_extract(_URLS[language]))
+        else:
+            data_path = [Path(dl_manager.download_and_extract(_URLS[language])) for language in _LANGUAGES]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_path,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        emotions_mapping = {1: "Anger", 2: "Anticipation", 3: "Disgust", 4: "Fear", 5: "Joy", 6: "Sadness", 7: "Surprise", 8: "Trust"}
+
+        df = pd.read_csv(filepath, sep="\t", names=["Sentence", "Emotions"], index_col=None)
+        df["Emotions"] = df["Emotions"].apply(lambda x: list(map(int, x.split(", "))))
+        df["Emotions"] = df["Emotions"].apply(lambda x: [emotions_mapping[emotion] for emotion in x])
+
+        for index, row in df.iterrows():
+
+            if self.config.schema == "source":
+                example = row.to_dict()
+
+            elif self.config.schema == "seacrowd_text_multi":
+
+                example = {
+                    "id": str(index),
+                    "text": str(row["Sentence"]),
+                    "labels": row["Emotions"],
+                }
+
+            yield index, example