Datasets:

timworks
/

massive-dataset

@@ -35,7 +35,59 @@ _CITATION = """
 }
 """
-_LANGUAGE_PAIRS = ['af-ZA', 'am-ET', 'ar-SA', 'az-AZ', 'bn-BD', 'cy-GB', 'da-DK', 'de-DE', 'el-GR', 'en-US', 'es-ES', 'fa-IR', 'fi-FI', 'fr-FR', 'he-IL', 'hi-IN', 'hu-HU', 'hy-AM', 'id-ID', 'is-IS', 'it-IT', 'ja-JP', 'jv-ID', 'ka-GE', 'km-KH', 'kn-IN', 'ko-KR', 'lv-LV', 'ml-IN', 'mn-MN', 'ms-MY', 'my-MM', 'nb-NO', 'nl-NL', 'pl-PL', 'pt-PT', 'ro-RO', 'ru-RU', 'sl-SL', 'sq-AL', 'sv-SE', 'sw-KE', 'ta-IN', 'te-IN', 'th-TH', 'tl-PH', 'tr-TR', 'ur-PK', 'vi-VN', 'zh-CN', 'zh-TW']
 _DESCRIPTION = """
@@ -47,30 +99,227 @@ the SLURP dataset, composed of general Intelligent Voice Assistant single-shot i
 _URL = "https://github.com/timworks/massive-raw/raw/main/massive-raw.tar.gz"
-_SCENARIOS = ['calendar', 'recommendation', 'social', 'general', 'news', 'cooking', 'iot', 'email', 'weather', 'alarm', 'transport', 'lists', 'takeaway', 'play', 'audio', 'music', 'qa', 'datetime']
-_INTENTS = ['audio_volume_other', 'play_music', 'iot_hue_lighton', 'general_greet', 'calendar_set', 'audio_volume_down', 'social_query', 'audio_volume_mute', 'iot_wemo_on', 'iot_hue_lightup', 'audio_volume_up', 'iot_coffee', 'takeaway_query', 'qa_maths', 'play_game', 'cooking_query', 'iot_hue_lightdim', 'iot_wemo_off', 'music_settings', 'weather_query', 'news_query', 'alarm_remove', 'social_post', 'recommendation_events', 'transport_taxi', 'takeaway_order', 'music_query', 'calendar_query', 'lists_query', 'qa_currency', 'recommendation_movies', 'general_joke', 'recommendation_locations', 'email_querycontact', 'lists_remove', 'play_audiobook', 'email_addcontact', 'lists_createoradd', 'play_radio', 'qa_stock', 'alarm_query', 'email_sendemail', 'general_quirky', 'music_likeness', 'cooking_recipe', 'email_query', 'datetime_query', 'transport_traffic', 'play_podcasts', 'iot_hue_lightchange', 'calendar_remove', 'transport_query', 'transport_ticket', 'qa_factoid', 'iot_cleaning', 'alarm_set', 'datetime_convert', 'iot_hue_lightoff', 'qa_definition', 'music_dislikeness', 'email_summarize']
-_TAGS = ['O', 'B-food_type', 'B-movie_type', 'B-person', 'B-change_amount', 'I-relation', 'I-game_name', 'B-date', 'B-movie_name', 'I-person', 'I-place_name', 'I-podcast_descriptor', 'I-audiobook_name', 'B-email_folder', 'B-coffee_type', 'B-app_name', 'I-time', 'I-coffee_type', 'B-transport_agency', 'B-podcast_descriptor', 'I-playlist_name', 'B-media_type', 'B-song_name', 'I-music_descriptor', 'I-song_name', 'B-event_name', 'I-timeofday', 'B-alarm_type', 'B-cooking_type', 'I-business_name', 'I-color_type', 'B-podcast_name', 'I-personal_info', 'B-weather_descriptor', 'I-list_name', 'B-transport_descriptor', 'I-game_type', 'I-date', 'B-place_name', 'B-color_type', 'B-game_name', 'I-artist_name', 'I-drink_type', 'B-business_name', 'B-timeofday', 'B-sport_type', 'I-player_setting', 'I-transport_agency', 'B-game_type', 'B-player_setting', 'I-music_album', 'I-event_name', 'I-general_frequency', 'I-podcast_name', 'I-cooking_type', 'I-radio_name', 'I-joke_type', 'I-meal_type', 'I-transport_type', 'B-joke_type', 'B-time', 'B-order_type', 'B-business_type', 'B-general_frequency', 'I-food_type', 'I-time_zone', 'B-currency_name', 'B-time_zone', 'B-ingredient', 'B-house_place', 'B-audiobook_name', 'I-ingredient', 'I-media_type', 'I-news_topic', 'B-music_genre', 'I-definition_word', 'B-list_name', 'B-playlist_name', 'B-email_address', 'I-currency_name', 'I-movie_name', 'I-device_type', 'I-weather_descriptor', 'B-audiobook_author', 'I-audiobook_author', 'I-app_name', 'I-order_type', 'I-transport_name', 'B-radio_name', 'I-business_type', 'B-definition_word', 'B-artist_name', 'I-movie_type', 'B-transport_name', 'I-email_folder', 'B-music_album', 'I-house_place', 'I-music_genre', 'B-drink_type', 'I-alarm_type', 'B-music_descriptor', 'B-news_topic', 'B-meal_type', 'I-transport_descriptor', 'I-email_address', 'I-change_amount', 'B-device_type', 'B-transport_type', 'B-relation', 'I-sport_type', 'B-personal_info']
 _ALL = "all"
 class MassiveIntents(datasets.GeneratorBasedBuilder):
     """MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"""
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
-            name = name,
-            version = datasets.Version("1.0.0"),
-            description = f"The MASSIVE corpora for {name}",
-        ) for name in _LANGUAGE_PAIRS
     ]
-    BUILDER_CONFIGS.append(datasets.BuilderConfig(
-        name = _ALL,
-        version = datasets.Version("1.0.0"),
-        description = f"The MASSIVE corpora for entire corpus",
-    ))
     DEFAULT_CONFIG_NAME = _ALL
@@ -88,23 +337,25 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
                     "annot_utt": datasets.Value("string"),
                     "tokens": datasets.Sequence(datasets.Value("string")),
                     "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names = _TAGS
-                        )
                     ),
                     "worker_id": datasets.Value("string"),
-                    "slot_method": datasets.Sequence({
-                        "slot": datasets.Value("string"),
-                        "method": datasets.Value("string"),
-                    }),
-                    "judgments": datasets.Sequence({
-                        "worker_id": datasets.Value("string"),
-                        "intent_score": datasets.Value("int8"), # [0, 1, 2]
-                        "slots_score": datasets.Value("int8"), # [0, 1, 2]
-                        "grammar_score": datasets.Value("int8"), # [0, 1, 2, 3, 4]
-                        "spelling_score": datasets.Value("int8"), # [0, 1, 2]
-                        "language_identification": datasets.Value("string"),
-                    }),
                 },
             ),
             supervised_keys=None,
@@ -114,7 +365,6 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
         )
     def _split_generators(self, dl_manager):
         archive = dl_manager.download(_URL)
         return [
@@ -124,7 +374,7 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
                     "files": dl_manager.iter_archive(archive),
                     "split": "train",
                     "lang": self.config.name,
-                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
@@ -132,7 +382,7 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
                     "files": dl_manager.iter_archive(archive),
                     "split": "dev",
                     "lang": self.config.name,
-                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
@@ -140,74 +390,74 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
                     "files": dl_manager.iter_archive(archive),
                     "split": "test",
                     "lang": self.config.name,
-                }
             ),
         ]
     def _getBioFormat(self, text):
         tags, tokens = [], []
         bio_mode = False
         cpt_bio = 0
         current_tag = None
-        split_iter = iter(text.split(" "))
-        for s in split_iter:
-            if s.startswith("["):
-                current_tag = s.strip("[")
-                bio_mode = True
-                cpt_bio += 1
-                next(split_iter)
-                continue
-            elif s.endswith("]"):
-                bio_mode = False
-                if cpt_bio == 1:
-                    prefix = "B-"
-                else:
-                    prefix = "I-"
-                token = prefix + current_tag
-                word = s.strip("]")
-                current_tag = None
-                cpt_bio = 0
-            else:
-                if bio_mode == True:
                     if cpt_bio == 1:
                         prefix = "B-"
                     else:
                         prefix = "I-"
                     token = prefix + current_tag
-                    word = s
-                    cpt_bio += 1
                 else:
-                    token = "O"
-                    word = s
-            tags.append(token)
-            tokens.append(word)
         return tokens, tags
     def _generate_examples(self, files, split, lang):
         key_ = 0
         if lang == "all":
             lang = _LANGUAGE_PAIRS.copy()
         else:
             lang = [lang]
         logger.info("⏳ Generating examples from = %s", ", ".join(lang))
         for path, f in files:
             l = path.split("1.1/data/")[-1].split(".jsonl")[0]
             if not lang:
                 break
             elif l in lang:
@@ -219,7 +469,6 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
             lines = f.read().decode(encoding="utf-8").split("\n")
             for line in lines:
                 data = json.loads(line)
                 if data["partition"] != split:
@@ -231,7 +480,8 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
                         {
                             "slot": s["slot"],
                             "method": s["method"],
-                        } for s in data["slot_method"]
                     ]
                 else:
                     slot_method = []
@@ -245,8 +495,11 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
                             "slots_score": j["slots_score"],
                             "grammar_score": j["grammar_score"],
                             "spelling_score": j["spelling_score"],
-                            "language_identification": j["language_identification"] if "language_identification" in j else "target",
-                        } for j in data["judgments"]
                     ]
                 else:
                     judgments = []
@@ -268,4 +521,4 @@ class MassiveIntents(datasets.GeneratorBasedBuilder):
                     "judgments": judgments,
                 }
-                key_ += 1

 }
 """
+_LANGUAGE_PAIRS = [
+    "af-ZA",
+    "am-ET",
+    "ar-SA",
+    "az-AZ",
+    "bn-BD",
+    "cy-GB",
+    "da-DK",
+    "de-DE",
+    "el-GR",
+    "en-US",
+    "es-ES",
+    "fa-IR",
+    "fi-FI",
+    "fr-FR",
+    "he-IL",
+    "hi-IN",
+    "hu-HU",
+    "hy-AM",
+    "id-ID",
+    "is-IS",
+    "it-IT",
+    "ja-JP",
+    "jv-ID",
+    "ka-GE",
+    "km-KH",
+    "kn-IN",
+    "ko-KR",
+    "lv-LV",
+    "ml-IN",
+    "mn-MN",
+    "ms-MY",
+    "my-MM",
+    "nb-NO",
+    "nl-NL",
+    "pl-PL",
+    "pt-PT",
+    "ro-RO",
+    "ru-RU",
+    "sl-SL",
+    "sq-AL",
+    "sv-SE",
+    "sw-KE",
+    "ta-IN",
+    "te-IN",
+    "th-TH",
+    "tl-PH",
+    "tr-TR",
+    "ur-PK",
+    "vi-VN",
+    "zh-CN",
+    "zh-TW",
+]
 _DESCRIPTION = """
 _URL = "https://github.com/timworks/massive-raw/raw/main/massive-raw.tar.gz"
+_SCENARIOS = [
+    "calendar",
+    "recommendation",
+    "social",
+    "general",
+    "news",
+    "cooking",
+    "iot",
+    "email",
+    "weather",
+    "alarm",
+    "transport",
+    "lists",
+    "takeaway",
+    "play",
+    "audio",
+    "music",
+    "qa",
+    "datetime",
+]
+_INTENTS = [
+    "audio_volume_other",
+    "play_music",
+    "iot_hue_lighton",
+    "general_greet",
+    "calendar_set",
+    "audio_volume_down",
+    "social_query",
+    "audio_volume_mute",
+    "iot_wemo_on",
+    "iot_hue_lightup",
+    "audio_volume_up",
+    "iot_coffee",
+    "takeaway_query",
+    "qa_maths",
+    "play_game",
+    "cooking_query",
+    "iot_hue_lightdim",
+    "iot_wemo_off",
+    "music_settings",
+    "weather_query",
+    "news_query",
+    "alarm_remove",
+    "social_post",
+    "recommendation_events",
+    "transport_taxi",
+    "takeaway_order",
+    "music_query",
+    "calendar_query",
+    "lists_query",
+    "qa_currency",
+    "recommendation_movies",
+    "general_joke",
+    "recommendation_locations",
+    "email_querycontact",
+    "lists_remove",
+    "play_audiobook",
+    "email_addcontact",
+    "lists_createoradd",
+    "play_radio",
+    "qa_stock",
+    "alarm_query",
+    "email_sendemail",
+    "general_quirky",
+    "music_likeness",
+    "cooking_recipe",
+    "email_query",
+    "datetime_query",
+    "transport_traffic",
+    "play_podcasts",
+    "iot_hue_lightchange",
+    "calendar_remove",
+    "transport_query",
+    "transport_ticket",
+    "qa_factoid",
+    "iot_cleaning",
+    "alarm_set",
+    "datetime_convert",
+    "iot_hue_lightoff",
+    "qa_definition",
+    "music_dislikeness",
+    "email_summarize",
+]
+_TAGS = [
+    "O",
+    "B-food_type",
+    "B-movie_type",
+    "B-person",
+    "B-change_amount",
+    "I-relation",
+    "I-game_name",
+    "B-date",
+    "B-movie_name",
+    "I-person",
+    "I-place_name",
+    "I-podcast_descriptor",
+    "I-audiobook_name",
+    "B-email_folder",
+    "B-coffee_type",
+    "B-app_name",
+    "I-time",
+    "I-coffee_type",
+    "B-transport_agency",
+    "B-podcast_descriptor",
+    "I-playlist_name",
+    "B-media_type",
+    "B-song_name",
+    "I-music_descriptor",
+    "I-song_name",
+    "B-event_name",
+    "I-timeofday",
+    "B-alarm_type",
+    "B-cooking_type",
+    "I-business_name",
+    "I-color_type",
+    "B-podcast_name",
+    "I-personal_info",
+    "B-weather_descriptor",
+    "I-list_name",
+    "B-transport_descriptor",
+    "I-game_type",
+    "I-date",
+    "B-place_name",
+    "B-color_type",
+    "B-game_name",
+    "I-artist_name",
+    "I-drink_type",
+    "B-business_name",
+    "B-timeofday",
+    "B-sport_type",
+    "I-player_setting",
+    "I-transport_agency",
+    "B-game_type",
+    "B-player_setting",
+    "I-music_album",
+    "I-event_name",
+    "I-general_frequency",
+    "I-podcast_name",
+    "I-cooking_type",
+    "I-radio_name",
+    "I-joke_type",
+    "I-meal_type",
+    "I-transport_type",
+    "B-joke_type",
+    "B-time",
+    "B-order_type",
+    "B-business_type",
+    "B-general_frequency",
+    "I-food_type",
+    "I-time_zone",
+    "B-currency_name",
+    "B-time_zone",
+    "B-ingredient",
+    "B-house_place",
+    "B-audiobook_name",
+    "I-ingredient",
+    "I-media_type",
+    "I-news_topic",
+    "B-music_genre",
+    "I-definition_word",
+    "B-list_name",
+    "B-playlist_name",
+    "B-email_address",
+    "I-currency_name",
+    "I-movie_name",
+    "I-device_type",
+    "I-weather_descriptor",
+    "B-audiobook_author",
+    "I-audiobook_author",
+    "I-app_name",
+    "I-order_type",
+    "I-transport_name",
+    "B-radio_name",
+    "I-business_type",
+    "B-definition_word",
+    "B-artist_name",
+    "I-movie_type",
+    "B-transport_name",
+    "I-email_folder",
+    "B-music_album",
+    "I-house_place",
+    "I-music_genre",
+    "B-drink_type",
+    "I-alarm_type",
+    "B-music_descriptor",
+    "B-news_topic",
+    "B-meal_type",
+    "I-transport_descriptor",
+    "I-email_address",
+    "I-change_amount",
+    "B-device_type",
+    "B-transport_type",
+    "B-relation",
+    "I-sport_type",
+    "B-personal_info",
+]
 _ALL = "all"
 class MassiveIntents(datasets.GeneratorBasedBuilder):
     """MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"""
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
+            name=name,
+            version=datasets.Version("1.0.0"),
+            description=f"The MASSIVE corpora for {name}",
+        )
+        for name in _LANGUAGE_PAIRS
     ]
+    BUILDER_CONFIGS.append(
+        datasets.BuilderConfig(
+            name=_ALL,
+            version=datasets.Version("1.0.0"),
+            description=f"The MASSIVE corpora for entire corpus",
+        )
+    )
     DEFAULT_CONFIG_NAME = _ALL
                     "annot_utt": datasets.Value("string"),
                     "tokens": datasets.Sequence(datasets.Value("string")),
                     "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(names=_TAGS)
                     ),
                     "worker_id": datasets.Value("string"),
+                    "slot_method": datasets.Sequence(
+                        {
+                            "slot": datasets.Value("string"),
+                            "method": datasets.Value("string"),
+                        }
+                    ),
+                    "judgments": datasets.Sequence(
+                        {
+                            "worker_id": datasets.Value("string"),
+                            "intent_score": datasets.Value("int8"),  # [0, 1, 2]
+                            "slots_score": datasets.Value("int8"),  # [0, 1, 2]
+                            "grammar_score": datasets.Value("int8"),  # [0, 1, 2, 3, 4]
+                            "spelling_score": datasets.Value("int8"),  # [0, 1, 2]
+                            "language_identification": datasets.Value("string"),
+                        }
+                    ),
                 },
             ),
             supervised_keys=None,
         )
     def _split_generators(self, dl_manager):
         archive = dl_manager.download(_URL)
         return [
                     "files": dl_manager.iter_archive(archive),
                     "split": "train",
                     "lang": self.config.name,
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                     "files": dl_manager.iter_archive(archive),
                     "split": "dev",
                     "lang": self.config.name,
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                     "files": dl_manager.iter_archive(archive),
                     "split": "test",
                     "lang": self.config.name,
+                },
             ),
         ]
     def _getBioFormat(self, text):
         tags, tokens = [], []
         bio_mode = False
         cpt_bio = 0
         current_tag = None
+        split_iter = iter(text.split(" "))
+        try:
+            for s in split_iter:
+                if s.startswith("["):
+                    current_tag = s.strip("[")
+                    bio_mode = True
+                    cpt_bio += 1
+                    next(split_iter)
+                    continue
+                elif s.endswith("]"):
+                    bio_mode = False
                     if cpt_bio == 1:
                         prefix = "B-"
                     else:
                         prefix = "I-"
                     token = prefix + current_tag
+                    word = s.strip("]")
+                    current_tag = None
+                    cpt_bio = 0
                 else:
+                    if bio_mode == True:
+                        if cpt_bio == 1:
+                            prefix = "B-"
+                        else:
+                            prefix = "I-"
+                        token = prefix + current_tag
+                        word = s
+                        cpt_bio += 1
+                    else:
+                        token = "O"
+                        word = s
+                tags.append(token)
+                tokens.append(word)
+        except Exception as e:
+            print(e)
+            print(text)
+            print(tags)
+            print(tokens)
         return tokens, tags
     def _generate_examples(self, files, split, lang):
         key_ = 0
         if lang == "all":
             lang = _LANGUAGE_PAIRS.copy()
         else:
             lang = [lang]
         logger.info("⏳ Generating examples from = %s", ", ".join(lang))
         for path, f in files:
             l = path.split("1.1/data/")[-1].split(".jsonl")[0]
             if not lang:
                 break
             elif l in lang:
             lines = f.read().decode(encoding="utf-8").split("\n")
             for line in lines:
                 data = json.loads(line)
                 if data["partition"] != split:
                         {
                             "slot": s["slot"],
                             "method": s["method"],
+                        }
+                        for s in data["slot_method"]
                     ]
                 else:
                     slot_method = []
                             "slots_score": j["slots_score"],
                             "grammar_score": j["grammar_score"],
                             "spelling_score": j["spelling_score"],
+                            "language_identification": j["language_identification"]
+                            if "language_identification" in j
+                            else "target",
+                        }
+                        for j in data["judgments"]
                     ]
                 else:
                     judgments = []
                     "judgments": judgments,
                 }
+                key_ += 1