Upload massive.py

massive.py

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+# coding=utf-8
+
+"""MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages"""
+
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+        MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations
+        for the Natural Language Understanding tasks of intent prediction and slot annotation.
+        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.
+"""
+_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",
+]
+
+_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
+    ]
+
+    DEFAULT_CONFIG_NAME = "en-US"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "locale": datasets.Value("string"),
+                    "partition": datasets.Value("string"),
+                    "scenario": datasets.features.ClassLabel(names=_SCENARIOS),
+                    "intent": datasets.features.ClassLabel(names=_INTENTS),
+                    "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"),
+                        }
+                    ),
+                },
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/alexa/massive",
+            citation="_CITATION",
+            license="_LICENSE",
+        )
+
+    def _split_generators(self, dl_manager):
+
+        #   path = dl_manager.download_and_extract(_URL)
+        archive_path = dl_manager.download(_URL)
+        files = dl_manager.iter_archive(archive_path)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": files,
+                    "split": "train",
+                    "lang": self.config.name,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": files,
+                    "split": "dev",
+                    "lang": self.config.name,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": files,
+                    "split": "test",
+                    "lang": self.config.name,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files, split, lang):
+
+        filepath = "1.0/data/" + lang + ".jsonl"
+
+        logger.info("⏳ Generating examples from = %s", filepath)
+
+        for path, f in files:
+            if path == filepath:
+                # Read the file
+                lines = f.readlines()
+
+                key_ = 0
+
+                for line in lines:
+
+                    data = json.loads(line)
+
+                    if data["partition"] != split:
+                        continue
+
+                    yield key_, {
+                        "id": data["id"],
+                        "label": data["scenario"],
+                        "text": data["utt"],
+                    }
+
+                    key_ += 1