|
|
|
|
|
"""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": "af-ZA", |
|
"am": "am-ET", |
|
"ar": "ar-SA", |
|
"az": "az-AZ", |
|
"bn": "bn-BD", |
|
"cy": "cy-GB", |
|
"da": "da-DK", |
|
"de": "de-DE", |
|
"el": "el-GR", |
|
"en": "en-US", |
|
"es": "es-ES", |
|
"fa": "fa-IR", |
|
"fi": "fi-FI", |
|
"fr": "fr-FR", |
|
"he": "he-IL", |
|
"hi": "hi-IN", |
|
"hu": "hu-HU", |
|
"hy": "hy-AM", |
|
"id": "id-ID", |
|
"is": "is-IS", |
|
"it": "it-IT", |
|
"ja": "ja-JP", |
|
"jv": "jv-ID", |
|
"ka": "ka-GE", |
|
"km": "km-KH", |
|
"kn": "kn-IN", |
|
"ko": "ko-KR", |
|
"lv": "lv-LV", |
|
"ml": "ml-IN", |
|
"mn": "mn-MN", |
|
"ms": "ms-MY", |
|
"my": "my-MM", |
|
"nb": "nb-NO", |
|
"nl": "nl-NL", |
|
"pl": "pl-PL", |
|
"pt": "pt-PT", |
|
"ro": "ro-RO", |
|
"ru": "ru-RU", |
|
"sl": "sl-SL", |
|
"sq": "sq-AL", |
|
"sv": "sv-SE", |
|
"sw": "sw-KE", |
|
"ta": "ta-IN", |
|
"te": "te-IN", |
|
"th": "th-TH", |
|
"tl": "tl-PH", |
|
"tr": "tr-TR", |
|
"ur": "ur-PK", |
|
"vi": "vi-VN", |
|
"zh-CN": "zh-CN", |
|
"zh-TW": "zh-TW", |
|
} |
|
|
|
_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.keys() |
|
] |
|
|
|
DEFAULT_CONFIG_NAME = "en" |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
"id": datasets.Value("string"), |
|
"label": datasets.features.ClassLabel(names=_INTENTS), |
|
"label_text": datasets.Value("string"), |
|
"text": datasets.Value("string"), |
|
}, |
|
), |
|
supervised_keys=None, |
|
homepage="https://github.com/alexa/massive", |
|
citation="_CITATION", |
|
license="_LICENSE", |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
|
|
|
|
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/" + _LANGUAGES[lang] + ".jsonl" |
|
logger.info("⏳ Generating examples from = %s", filepath) |
|
for path, f in files: |
|
if path == filepath: |
|
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["intent"], |
|
"label_text": data["intent"], |
|
"text": data["utt"], |
|
} |
|
key_ += 1 |
|
|
