Delete loading script

emotion.py

@@ -1,88 +0,0 @@
-import json
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-@inproceedings{saravia-etal-2018-carer,
-    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
-    author = "Saravia, Elvis  and
-      Liu, Hsien-Chi Toby  and
-      Huang, Yen-Hao  and
-      Wu, Junlin  and
-      Chen, Yi-Shin",
-    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
-    month = oct # "-" # nov,
-    year = "2018",
-    address = "Brussels, Belgium",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/D18-1404",
-    doi = "10.18653/v1/D18-1404",
-    pages = "3687--3697",
-    abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
-}
-"""
-
-_DESCRIPTION = """\
-Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
-"""
-
-_HOMEPAGE = "https://github.com/dair-ai/emotion_dataset"
-
-_LICENSE = "The dataset should be used for educational and research purposes only"
-
-_URLS = {
-    "split": {
-        "train": "data/train.jsonl.gz",
-        "validation": "data/validation.jsonl.gz",
-        "test": "data/test.jsonl.gz",
-    },
-    "unsplit": {
-        "train": "data/data.jsonl.gz",
-    },
-}
-
-
-class Emotion(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="split", version=VERSION, description="Dataset split in train, validation and test"
-        ),
-        datasets.BuilderConfig(name="unsplit", version=VERSION, description="Unsplit dataset"),
-    ]
-    DEFAULT_CONFIG_NAME = "split"
-
-    def _info(self):
-        class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=class_names)}
-            ),
-            supervised_keys=("text", "label"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        paths = dl_manager.download_and_extract(_URLS[self.config.name])
-        if self.config.name == "split":
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]}),
-                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": paths["validation"]}),
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": paths["test"]}),
-            ]
-        else:
-            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]})]
-
-    def _generate_examples(self, filepath):
-        """Generate examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                example = json.loads(line)
-                yield idx, example