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diff --git a/tweet_topic_multilingual.py b/tweet_topic_multilingual.py
index 0b968b0d131a23b627edc26540b65906b9083b41..84c6f613a41936a36c1a77626680dc7b02161fbc 100644
--- a/tweet_topic_multilingual.py
+++ b/tweet_topic_multilingual.py
@@ -8,10 +8,96 @@ _VERSION = "0.0.3"
 _CITATION = """TBA"""
 _HOME_PAGE = "https://cardiffnlp.github.io"
 _NAME = "tweet_topic_multilingual"
-_ROOT_URL = f'https://huggingface.co/datasets/cardiffnlp/{_NAME}/resolve/main/dataset'
+_ROOT_URL = f"https://huggingface.co/datasets/cardiffnlp/{_NAME}/resolve/main/dataset"
 _LANGUAGES = ["en", "es", "ja", "gr"]
-_URL = {}
+_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"]}
@@ -54,25 +140,20 @@ class TweetTopicMultilingual(datasets.GeneratorBasedBuilder):
         _key = 0
         logger.info("generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f:
-            _list = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+            _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):
-        names = [
-            "arts_&_culture", "business_&_entrepreneurs", "celebrity_&_pop_culture", "diaries_&_daily_life", "family",
-            "fashion_&_style", "film_tv_&_video", "fitness_&_health", "food_&_dining", "gaming",
-            "learning_&_educational", "music", "news_&_social_concern", "other_hobbies", "relationships",
-            "science_&_technology", "sports", "travel_&_adventure", "youth_&_student_life"
-        ]
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
                     "id": datasets.Value("string"),
                     "text": datasets.Value("string"),
-                    "label": datasets.Sequence(datasets.features.ClassLabel(names=names)),
+                    "label_name_flatten": datasets.Value("string"),
+                    "label": datasets.Sequence(datasets.features.ClassLabel(names=_CLASS_MAPPING["en"])),
                     "label_name": datasets.Sequence(datasets.Value("string"))
                 }
             ),