many_emotions.py

import json
from typing import List

import datasets
from datasets import ClassLabel, Value, load_dataset

_LANGUAGES = ["en", "fr", "it", "es", "de"]

_SUB_CLASSES = [
    "anger",
    "fear",
    "joy",
    "love",
    "sadness",
    "surprise",
    "neutral",
]

_CLASS_NAMES = [
    "no emotion",
    "happiness",
    "admiration",
    "amusement",
    "anger",
    "annoyance",
    "approval",
    "caring",
    "confusion",
    "curiosity",
    "desire",
    "disappointment",
    "disapproval",
    "disgust",
    "embarrassment",
    "excitement",
    "fear",
    "gratitude",
    "grief",
    "joy",
    "love",
    "nervousness",
    "optimism",
    "pride",
    "realization",
    "relief",
    "remorse",
    "sadness",
    "surprise",
    "neutral",
]


class EmotionsDatasetConfig(datasets.BuilderConfig):
    def __init__(self, features, label_classes, **kwargs):
        super().__init__(**kwargs)
        self.features = features
        self.label_classes = label_classes


class EmotionsDataset(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        EmotionsDatasetConfig(
            name="raw",
            label_classes=_SUB_CLASSES,
            features=["text", "label", "dataset", "license"],
        ),
        EmotionsDatasetConfig(
            name="split",
            label_classes=_SUB_CLASSES,
            features=["text", "label", "dataset", "license", "language"],
        ),
    ]

    DEFAULT_CONFIG_NAME = "split"

    def _info(self):
        features = {
            "id": datasets.Value("string"),
            "text": Value(dtype="string", id=None),
            "label": ClassLabel(names=_SUB_CLASSES, id=None),
            "dataset": Value(dtype="string", id=None),
            "license": Value(dtype="string", id=None),
        }
        if self.config.name == "split":
            features.update({"language": ClassLabel(names=_LANGUAGES, id=None)})
        return datasets.DatasetInfo(features=datasets.Features(features))

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:
        splits = []
        if self.config.name == "raw":
            downloaded_files = dl_manager.download_and_extract(
                ["data/many_emotions.json.gz"]
            )
            for lang in _LANGUAGES:
                splits.append(
                    datasets.SplitGenerator(
                        name=lang,
                        gen_kwargs={
                            "filepaths": downloaded_files,
                            "language": lang,
                            "dataset": "raw",
                        },
                    )
                )
        else:
            for split in ["train", "validation", "test"]:
                downloaded_files = dl_manager.download_and_extract(
                    [f"data/split_dataset_{split}.jsonl.gz"]
                )
                splits.append(
                    datasets.SplitGenerator(
                        name=split,
                        gen_kwargs={"filepaths": downloaded_files, "dataset": "split"},
                    )
                )
        return splits

    def _generate_examples(self, filepaths, dataset, license=None, language=None):
        if dataset == "raw":
            for i, filepath in enumerate(filepaths):
                with open(filepath, encoding="utf-8") as f:
                    for idx, line in enumerate(f):
                        example = json.loads(line)
                        if language != "all":
                            example = {
                                "id": example["id"],
                                "text": example[
                                    "text" if language == "en" else language
                                ],
                                "label": example["label"],
                                "dataset": example["dataset"],
                                "license": example["license"],
                            }
                        label = _CLASS_NAMES[example["label"]]
                        if label == "no emotion":
                            label = "neutral"
                        elif label == "happiness":
                            label = "joy"
                        example.update({"label": label})
                        yield example["id"], example
        else:
            for i, filepath in enumerate(filepaths):
                with open(filepath, encoding="utf-8") as f:
                    for idx, line in enumerate(f):
                        example = json.loads(line)
                        yield example["id"], example


if __name__ == "__main__":
    dataset = load_dataset("ma2za/many_emotions", name="raw")
    print()