Create tweet_topic_multilingual.py

tweet_topic_multilingual.py

@@ -0,0 +1,173 @@
+""" TweetTopicMultilingual Dataset """
+import json
+from typing import List
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """[TweetTopicMultilingual](TBA)"""
+_VERSION = "0.0.91"
+_CITATION = """TBA"""
+_HOME_PAGE = "https://cardiffnlp.github.io"
+_NAME = "tweet_topic_multilingual"
+_ROOT_URL = f"https://huggingface.co/datasets/cardiffnlp/{_NAME}/resolve/main/dataset"
+_LANGUAGES = ["en", "es", "ja", "gr"]
+_CLASS_MAPPING = {
+    "en": [
+        "Arts & Culture",
+        "Business & Entrepreneurs",
+        "Celebrity & Pop Culture",
+        "Diaries & Daily Life",
+        "Family",
+        "Fashion & Style",
+        "Film, TV & Video",
+        "Fitness & Health",
+        "Food & Dining",
+        "Learning & Educational",
+        "News & Social Concern",
+        "Relationships",
+        "Science & Technology",
+        "Youth & Student Life",
+        "Music",
+        "Gaming",
+        "Sports",
+        "Travel & Adventure",
+        "Other Hobbies"
+    ],
+    "gr": [
+        "Τέχνες & Πολιτισμός",
+        "Επιχειρήσεις & Επιχειρηματίες",
+        "Διασημότητες & Ποπ κουλτούρα",
+        "Ημερολόγια & Καθημερινή ζωή",
+        "Οικογένεια",
+        "Μόδα & Στυλ",
+        "Ταινίες, τηλεόραση & βίντεο",
+        "Γυμναστική & Υεία",
+        "Φαγητό & Δείπνο",
+        "Μάθηση & Εκπαίδευση",
+        "Ειδήσεις & Κοινωνία",
+        "Σχέσεις",
+        "Επιστήμη & Τεχνολογία",
+        "Νεανική & Φοιτητική ζωή",
+        "Μουσική",
+        "Παιχνίδια",
+        "Αθλητισμός",
+        "Ταξίδια & Περιπέτεια",
+        "Άλλα χόμπι"
+    ],
+    "es": [
+        "Arte y cultura",
+        "Negocios y emprendedores",
+        "Celebridades y cultura pop",
+        "Diarios y vida diaria",
+        "Familia",
+        "Moda y estilo",
+        "Cine, televisión y video",
+        "Estado físico y salud",
+        "Comida y comedor",
+        "Aprendizaje y educación",
+        "Noticias e interés social",
+        "Relaciones",
+        "Ciencia y Tecnología",
+        "Juventud y Vida Estudiantil",
+        "Música",
+        "Juegos",
+        "Deportes",
+        "Viajes y aventuras",
+        "Otros pasatiempos"
+    ],
+    "ja": [
+        "アート&カルチャー",
+        "ビジネス",
+        "芸能",
+        "日常",
+        "家族",
+        "ファッション",
+        "映画&ラジオ",
+        "フィットネス&健康",
+        "料理",
+        "教育関連",
+        "社会",
+        "人間関係",
+        "サイエンス",
+        "学校",
+        "音楽",
+        "ゲーム",
+        "スポーツ",
+        "旅行",
+        "その他"
+    ]
+}
+
+_URL = {}
+# plain split
+for lan in _LANGUAGES:
+    _URL[lan] = {split: [f"{_ROOT_URL}/{lan}/{lan}_{split}.jsonl"] for split in ["train", "test", "validation"]}
+_URL["en_2022"] = {split: [f"{_ROOT_URL}/en_2022/{split}.jsonl"] for split in ["train", "validation"]}
+_URL["mix"] = {
+    split: [f"{_ROOT_URL}/{lan}/{lan}_{split}.jsonl" for lan in _LANGUAGES] for split in ["train", "validation"]
+}
+_URL["mix_2022"] = {
+    split: [f"{_ROOT_URL}/{lan}/{lan}_{split}.jsonl" for lan in _LANGUAGES] + [f"{_ROOT_URL}/en_2022/{split}.jsonl"]
+    for split in ["train", "validation"]
+}
+# cross validation
+for lan in _LANGUAGES:
+    _URL.update({
+        f"{lan}_cross_validation_{n}": {
+            split: [f"{_ROOT_URL}/{lan}/cross_validation/{lan}_{split}_{n}.jsonl"]
+            for split in ["train", "test", "validation"]
+        } for n in range(5)
+    })
+
+
+class Config(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Config, self).__init__(**kwargs)
+
+
+class TweetTopicMultilingual(datasets.GeneratorBasedBuilder):
+    """Dataset."""
+
+    BUILDER_CONFIGS = [
+        Config(name=i, version=datasets.Version(_VERSION), description=_DESCRIPTION) for i in _URL.keys()
+    ]
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URL[self.config.name])
+        splits = _URL[self.config.name].keys()
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_file[i]}) for i in splits]
+
+    def _generate_examples(self, filepath: List[str]):
+        _key = 0
+        for _file in filepath:
+            logger.info("generating examples from = %s", _file)
+            with open(_file, encoding="utf-8") as f:
+                _list = [json.loads(i) for i in f.read().split("\n") if len(i) > 0]
+                for i in _list:
+                    yield _key, i
+                    _key += 1
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "label_name_flatten": datasets.Value("string"),
+                    "label": datasets.Sequence(datasets.features.ClassLabel(names=_CLASS_MAPPING["en"])),
+                    "label_name": datasets.Sequence(datasets.Value("string"))
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOME_PAGE,
+            citation=_CITATION,
+        )