File size: 5,685 Bytes
ffd58f5 5c5b425 ffd58f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 |
# 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"),
"label": datasets.features.ClassLabel(names=_SCENARIOS),
"text": 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:
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
|