Add the capability to get the entire corpora using the 'all' subset.

MASSIVE.py

@@ -260,7 +260,7 @@ _SCENARIOS = ['social', 'transport', 'calendar', 'play', 'news', 'datetime', 're
 
 _INTENTS = ['datetime_query', 'iot_hue_lightchange', 'transport_ticket', 'takeaway_query', 'qa_stock', 'general_greet', 'recommendation_events', 'music_dislikeness', 'iot_wemo_off', 'cooking_recipe', 'qa_currency', 'transport_traffic', 'general_quirky', 'weather_query', 'audio_volume_up', 'email_addcontact', 'takeaway_order', 'email_querycontact', 'iot_hue_lightup', 'recommendation_locations', 'play_audiobook', 'lists_createoradd', 'news_query', 'alarm_query', 'iot_wemo_on', 'general_joke', 'qa_definition', 'social_query', 'music_settings', 'audio_volume_other', 'calendar_remove', 'iot_hue_lightdim', 'calendar_query', 'email_sendemail', 'iot_cleaning', 'audio_volume_down', 'play_radio', 'cooking_query', 'datetime_convert', 'qa_maths', 'iot_hue_lightoff', 'iot_hue_lighton', 'transport_query', 'music_likeness', 'email_query', 'play_music', 'audio_volume_mute', 'social_post', 'alarm_set', 'qa_factoid', 'calendar_set', 'play_game', 'alarm_remove', 'lists_remove', 'transport_taxi', 'recommendation_movies', 'iot_coffee', 'music_query', 'play_podcasts', 'lists_query']
 
-_TAGS = ['O', 'I-playlist_name', 'I-general_frequency', 'B-audiobook_name', 'I-player_setting', 'I-business_type', 'I-time', 'I-place_name', 'I-date', 'B-device_type', 'I-song_name', 'B-timeofday', 'B-movie_type', 'B-order_type', 'B-date', 'B-news_topic', 'I-music_descriptor', 'B-media_type', 'B-cooking_type', 'B-meal_type', 'I-movie_name', 'B-joke_type', 'I-media_type', 'B-list_name', 'I-podcast_descriptor', 'I-meal_type', 'I-transport_descriptor', 'I-transport_agency', 'B-player_setting', 'B-house_place', 'B-music_genre', 'I-timeofday', 'I-personal_info', 'I-definition_word', 'B-podcast_name', 'I-podcast_name', 'I-music_album', 'I-transport_type', 'B-business_name', 'B-transport_name', 'B-sport_type', 'I-house_place', 'I-movie_type', 'B-transport_descriptor', 'B-artist_name', 'I-email_folder', 'I-event_name', 'B-email_folder', 'I-cooking_type', 'B-music_album', 'I-coffee_type', 'I-alarm_type', 'B-game_type', 'I-audiobook_name', 'B-playlist_name', 'B-alarm_type', 'B-place_name', 'I-relation', 'B-drink_type', 'I-drink_type', 'I-business_name', 'I-artist_name', 'B-music_descriptor', 'B-change_amount', 'I-weather_descriptor', 'I-game_name', 'I-app_name', 'I-ingredient', 'B-song_name', 'B-weather_descriptor', 'I-email_address', 'B-time', 'B-color_type', 'B-food_type', 'I-person', 'B-transport_type', 'B-radio_name', 'I-change_amount', 'B-transport_agency', 'B-movie_name', 'B-general_frequency', 'B-event_name', 'I-joke_type', 'B-relation', 'B-coffee_type', 'I-order_type', 'B-email_address', 'B-app_name', 'B-podcast_descriptor', 'B-definition_word', 'I-list_name', 'B-audiobook_author', 'I-audiobook_author', 'I-sport_type', 'I-news_topic', 'B-personal_info', 'I-radio_name', 'B-person', 'I-color_type', 'I-food_type', 'I-device_type', 'B-time_zone', 'I-music_genre', 'B-business_type', 'B-currency_name', 'I-currency_name', 'B-ingredient', 'I-transport_name', 'B-game_name', 'I-time_zone']
+_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']
 
 class MASSIVE(datasets.GeneratorBasedBuilder):
     """MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"""
@@ -270,9 +270,15 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
             name = name,
             version = datasets.Version("1.0.0"),
             description = f"The MASSIVE corpora for {name}",
-        ) for name in _LANGUAGE_PAIRS
+        ) 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 = "en-US"
 
     def _info(self):
@@ -396,65 +402,76 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, filepath, split, lang):
 
-        filepath = filepath + "/1.0/data/" + lang + ".jsonl"
+        key_ = 0
 
-        logger.info("⏳ Generating examples from = %s", filepath)
+        if lang == "all":
+            lang = _LANGUAGE_PAIRS
+        else:
+            lang = [lang]
         
-        # Read the file
-        f = open(filepath,"r")
-        lines = f.read().split("\n")
-        f.close()
+        logger.info("⏳ Generating examples from = %s", ", ".join(lang))
 
-        key_ = 0
+        for l in lang:
 
-        for line in lines:
+            path = filepath + "/1.0/data/" + l + ".jsonl"
 
-            data = json.loads(line)
+            print(f"->{l}<-")
+            
+            # Read the file
+            f = open(path ,"r")
+            lines = f.read().split("\n")
+            f.close()
 
-            if data["partition"] != split:
-                continue
+            for line in lines:
 
-            # Slot method
-            if "slot_method" in data:
-                slot_method = [
-                    {
-                        "slot": s["slot"],
-                        "method": s["method"],
-                    } for s in data["slot_method"]
-                ]
-            else:
-                slot_method = []
-
-            # Judgments
-            if "judgments" in data:
-                judgments = [
-                    {
-                        "worker_id": j["worker_id"],
-                        "intent_score": j["intent_score"],
-                        "slots_score": j["slots_score"],
-                        "grammar_score": j["grammar_score"],
-                        "spelling_score": j["spelling_score"],
-                        "language_identification": j["language_identification"],
-                    } for j in data["judgments"]
-                ]
-            else:
-                judgments = []
-
-            tokens, tags = self._getBioFormat(data["annot_utt"])
-
-            yield key_, {
-                "id": data["id"],
-                "locale": data["locale"],
-                "partition": data["partition"],
-                "scenario": data["scenario"],
-                "intent": data["intent"],
-                "utt": data["utt"],
-                "annot_utt": data["annot_utt"],
-                "tokens": tokens,
-                "ner_tags": tags,
-                "worker_id": data["worker_id"],
-                "slot_method": slot_method,
-                "judgments": judgments,
-            }
-
-            key_ += 1
+                data = json.loads(line)
+
+                # print(data["id"])
+
+                if data["partition"] != split:
+                    continue
+
+                # Slot method
+                if "slot_method" in data:
+                    slot_method = [
+                        {
+                            "slot": s["slot"],
+                            "method": s["method"],
+                        } for s in data["slot_method"]
+                    ]
+                else:
+                    slot_method = []
+
+                # Judgments
+                if "judgments" in data:
+                    judgments = [
+                        {
+                            "worker_id": j["worker_id"],
+                            "intent_score": j["intent_score"],
+                            "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 = []
+
+                tokens, tags = self._getBioFormat(data["annot_utt"])
+
+                yield key_, {
+                    "id": data["id"],
+                    "locale": data["locale"],
+                    "partition": data["partition"],
+                    "scenario": data["scenario"],
+                    "intent": data["intent"],
+                    "utt": data["utt"],
+                    "annot_utt": data["annot_utt"],
+                    "tokens": tokens,
+                    "ner_tags": tags,
+                    "worker_id": data["worker_id"],
+                    "slot_method": slot_method,
+                    "judgments": judgments,
+                }
+
+                key_ += 1

test_MASSIVE.py

@@ -5,7 +5,9 @@ set_caching_enabled(False)
 source = "MASSIVE.py"
 # source = "qanastek/MASSIVE"
 
-dataset = load_dataset(source, "fr-FR")
+dataset = load_dataset(source, "all")
+# dataset = load_dataset(source, "zh-CN")
+# dataset = load_dataset(source, "fr-FR")
 # dataset = load_dataset(source, "fr-FR", download_mode="force_redownload")
 # print(dataset)
 
@@ -17,3 +19,14 @@ print(dataset)
 # print(dataset[0])
 f = dataset["train"][0]
 print(f)
+
+# tags = []
+
+# for e in dataset["train"]:
+#     tags.extend(
+#         e["ner_tags"]
+#     )
+
+# print("#"*50)
+
+# print(list(set(tags)))