iter archive

test111.py

@@ -13,470 +13,157 @@ _DESCRIPTION = """\
         Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing
         the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
 """
-
-_CITATION = """\
-        @misc{fitzgerald2022massive,
-              title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
-              author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},
-              year={2022},
-              eprint={2204.08582},
-              archivePrefix={arXiv},
-              primaryClass={cs.CL}
-        }
-        \
-        @inproceedings{bastianelli-etal-2020-slurp,
-            title = "{SLURP}: A Spoken Language Understanding Resource Package",
-            author = "Bastianelli, Emanuele  and
-              Vanzo, Andrea  and
-              Swietojanski, Pawel  and
-              Rieser, Verena",
-            booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
-            month = nov,
-            year = "2020",
-            address = "Online",
-            publisher = "Association for Computational Linguistics",
-            url = "https://aclanthology.org/2020.emnlp-main.588",
-            doi = "10.18653/v1/2020.emnlp-main.588",
-            pages = "7252--7262",
-            abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp."
-        }
-"""
-
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-
 _URL = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz"
 
-_LANGUAGES = ['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']
-
-_SCENARIOS = ['social', 'transport', 'calendar', 'play', 'news', 'datetime', 'recommendation', 'email',
-              'iot', 'general', 'audio', 'lists', 'qa', 'cooking', 'takeaway', 'music', 'alarm', 'weather']
+_LANGUAGES = [
+    "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",
+]
+
+_SCENARIOS = [
+    "social",
+    "transport",
+    "calendar",
+    "play",
+    "news",
+    "datetime",
+    "recommendation",
+    "email",
+    "iot",
+    "general",
+    "audio",
+    "lists",
+    "qa",
+    "cooking",
+    "takeaway",
+    "music",
+    "alarm",
+    "weather",
+]
+
+_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",
+]
 
-_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']
 
 class MASSIVE(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 _LANGUAGES
+            name=name,
+            version=datasets.Version("1.0.0"),
+            description=f"The MASSIVE corpora for {name}",
+        )
+        for name in _LANGUAGES
     ]
 
     DEFAULT_CONFIG_NAME = "en-US"
@@ -494,29 +181,33 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
                     "utt": datasets.Value("string"),
                     "annot_utt": datasets.Value("string"),
                     "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"),
-                        "slots_score": datasets.Value("int8"),
-                        "grammar_score": datasets.Value("int8"),
-                        "spelling_score": datasets.Value("int8"),
-                        "language_identification": 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"),
+                            "slots_score": datasets.Value("int8"),
+                            "grammar_score": datasets.Value("int8"),
+                            "spelling_score": datasets.Value("int8"),
+                            "language_identification": datasets.Value("string"),
+                        }
+                    ),
                 },
             ),
             supervised_keys=None,
             homepage="https://github.com/alexa/massive",
-            citation=_CITATION,
-            license=_LICENSE,
+            citation="_CITATION",
+            license="_LICENSE",
         )
 
     def _split_generators(self, dl_manager):
 
-        path = dl_manager.download_and_extract(_URL)
+        path = dl_manager.iter_archive(_URL)
 
         return [
             datasets.SplitGenerator(
@@ -525,7 +216,7 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
                     "filepath": path,
                     "split": "train",
                     "lang": self.config.name,
-                }
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
@@ -533,7 +224,7 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
                     "filepath": path,
                     "split": "dev",
                     "lang": self.config.name,
-                }
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
@@ -541,7 +232,7 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
                     "filepath": path,
                     "split": "test",
                     "lang": self.config.name,
-                }
+                },
             ),
         ]
 
@@ -552,7 +243,7 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
         logger.info("⏳ Generating examples from = %s", filepath)
 
         # Read the file
-        f = open(filepath,"r")
+        f = open(filepath, "r")
         lines = f.read().split("\n")
         f.close()
 
@@ -571,7 +262,8 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
                     {
                         "slot": s["slot"],
                         "method": s["method"],
-                    } for s in data["slot_method"]
+                    }
+                    for s in data["slot_method"]
                 ]
             else:
                 slot_method = []
@@ -586,7 +278,8 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
                         "grammar_score": j["grammar_score"],
                         "spelling_score": j["spelling_score"],
                         "language_identification": j["language_identification"],
-                    } for j in data["judgments"]
+                    }
+                    for j in data["judgments"]
                 ]
             else:
                 judgments = []