from pathlib import Path
from typing import List
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
_CITATION = """\
@inproceedings{van-der-goot-etal-2020-cross,
title={From Masked-Language Modeling to Translation: Non-{E}nglish Auxiliary Tasks Improve Zero-shot Spoken Language Understanding},
author={van der Goot, Rob and Sharaf, Ibrahim and Imankulova, Aizhan and {\"U}st{\"u}n, Ahmet and Stepanovic, Marija and Ramponi, Alan and Khairunnisa, Siti Oryza and Komachi, Mamoru and Plank, Barbara},
booktitle = "Proceedings of the 2021 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics"
}
"""
_DATASETNAME = "xsid"
_DESCRIPTION = """\
XSID is a new benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect.
"""
_HOMEPAGE = "https://bitbucket.org/robvanderg/xsid/src/master/"
_LANGUAGES = ["ind"]
_LICENSE = "CC-BY-SA 4.0"
_LOCAL = False
_URLS = {
_DATASETNAME: "https://bitbucket.org/robvanderg/xsid/get/04ce1e6c8c28.zip",
}
_SUPPORTED_TASKS = [Tasks.INTENT_CLASSIFICATION, Tasks.POS_TAGGING]
_SOURCE_VERSION = "0.3.0"
_SEACROWD_VERSION = "2024.06.20"
INTENT_LIST = [
"AddToPlaylist",
"BookRestaurant",
"PlayMusic",
"RateBook",
"SearchCreativeWork",
"SearchScreeningEvent",
"alarm/cancel_alarm",
"alarm/modify_alarm",
"alarm/set_alarm",
"alarm/show_alarms",
"alarm/snooze_alarm",
"alarm/time_left_on_alarm",
"reminder/cancel_reminder",
"reminder/set_reminder",
"reminder/show_reminders",
"weather/checkSunrise",
"weather/checkSunset",
"weather/find"
]
TAG_LIST = [
"B-album",
"B-artist",
"B-best_rating",
"B-condition_description",
"B-condition_temperature",
"B-cuisine",
"B-datetime",
"B-ecurring_datetime",
"B-entity_name",
"B-facility",
"B-genre",
"B-location",
"B-movie_name",
"B-movie_type",
"B-music_item",
"B-object_location_type",
"B-object_name",
"B-object_part_of_series_type",
"B-object_select",
"B-object_type",
"B-party_size_description",
"B-party_size_number",
"B-playlist",
"B-rating_unit",
"B-rating_value",
"B-recurring_datetime",
"B-reference",
"B-reminder/todo",
"B-restaurant_name",
"B-restaurant_type",
"B-served_dish",
"B-service",
"B-sort",
"B-track",
"B-weather/attribute",
"I-album",
"I-artist",
"I-best_rating",
"I-condition_description",
"I-condition_temperature",
"I-cuisine",
"I-datetime",
"I-ecurring_datetime",
"I-entity_name",
"I-facility",
"I-genre",
"I-location",
"I-movie_name",
"I-movie_type",
"I-music_item",
"I-object_location_type",
"I-object_name",
"I-object_part_of_series_type",
"I-object_select",
"I-object_type",
"I-party_size_description",
"I-party_size_number",
"I-playlist",
"I-rating_unit",
"I-rating_value",
"I-recurring_datetime",
"I-reference",
"I-reminder/todo",
"I-restaurant_name",
"I-restaurant_type",
"I-served_dish",
"I-service",
"I-sort",
"I-track",
"I-weather/attribute",
"O",
"Orecurring_datetime"
]
class XSID(datasets.GeneratorBasedBuilder):
"""xSID datasets contains datasets to detect the intent from the text"""
BUILDER_CONFIGS = [
SEACrowdConfig(
name="xsid_source",
version=datasets.Version(_SOURCE_VERSION),
description="xSID source schema",
schema="source",
subset_id="xsid",
),
SEACrowdConfig(
name="xsid_seacrowd_text",
version=datasets.Version(_SEACROWD_VERSION),
description="xSID Nusantara intent classification schema",
schema="seacrowd_text",
subset_id="xsid",
),
SEACrowdConfig(
name="xsid_seacrowd_seq_label",
version=datasets.Version(_SEACROWD_VERSION),
description="xSID Nusantara pos tagging schema",
schema="seacrowd_seq_label",
subset_id="xsid",
),
]
DEFAULT_CONFIG_NAME = "xsid_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"text-en": datasets.Value("string"),
"intent": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
}
)
elif self.config.schema == "seacrowd_text":
features = schemas.text_features(label_names=INTENT_LIST)
elif self.config.schema == "seacrowd_seq_label":
features = schemas.seq_label_features(label_names=TAG_LIST)
else:
raise ValueError(f"Invalid config schema: {self.config.schema}")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls = _URLS[_DATASETNAME]
base_path = Path(dl_manager.download_and_extract(urls)) / "robvanderg-xsid-04ce1e6c8c28" / "data" / "xSID-0.3"
data_files = {
"train": base_path / "id.projectedTrain.conll",
"test": base_path / "id.test.conll",
"validation": base_path / "id.valid.conll"
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_files["test"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_files["validation"]},
),
]
def _generate_examples(self, filepath: Path):
print('filepath', filepath)
if self.config.name == "xsid_source":
with open(filepath, "r") as file:
data = file.read().strip("\n").split("\n\n")
i = 0
for sample in data:
id = ""
tokens = []
for row_sample in sample.split("\n"):
s = row_sample.split(": ")
if s[0] == "# id":
id = s[1]
elif s[0] == "# text-en":
text_en = s[1]
elif s[0] == "# text":
text = s[1]
elif s[0] == "# intent":
intent = s[1]
else:
tokens.append(s[0])
if id == "":
id = i
i = i + 1
ex = {
"id": id,
"text": text,
"text-en": text_en,
"intent": intent,
"tokens": tokens
}
yield id, ex
elif self.config.name == "xsid_seacrowd_text":
with open(filepath, "r") as file:
data = file.read().strip("\n").split("\n\n")
i = 0
for sample in data:
id = ""
for row_sample in sample.split("\n"):
s = row_sample.split(": ")
if s[0] == "# id":
id = s[1]
elif s[0] == "# text":
text = s[1]
elif s[0] == "# intent":
intent = s[1]
if id == "":
id = i
i = i + 1
ex = {
"id": id,
"text": text,
"label": intent
}
yield id, ex
elif self.config.name == "xsid_seacrowd_seq_label":
with open(filepath, "r") as file:
data = file.read().strip("\n").split("\n\n")
i = 0
for sample in data:
id = ""
tokens = []
labels = []
for row_sample in sample.split("\n"):
s = row_sample.split(": ")
if s[0] == "# id":
id = s[1]
elif len(s) == 1:
tokens.append(s[0].split("\t")[1])
labels.append(s[0].split("\t")[3])
if id == "":
id = i
i = i + 1
ex = {
"id": id,
"tokens": tokens,
"labels": labels
}
yield id, ex
else:
raise ValueError(f"Invalid config: {self.config.name}")