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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks, Licenses
_CITATION = """\
@article{dao2021intent,
title={Intent Detection and Slot Filling for Vietnamese},
author={Mai Hoang Dao and Thinh Hung Truong and Dat Quoc Nguyen},
year={2021},
eprint={2104.02021},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DATASETNAME = "phoatis"
_DESCRIPTION = """\
This is first public intent detection and slot filling dataset for Vietnamese. The data contains 5871 English utterances from ATIS that are manually translated by professional translators into Vietnamese.
"""
_HOMEPAGE = "https://github.com/VinAIResearch/JointIDSF/"
_LICENSE = Licenses.UNKNOWN.value
_URLS = {
_DATASETNAME: {
"syllable": {
"syllable_train": [
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/train/seq.in",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/train/seq.out",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/train/label",
],
"syllable_dev": [
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/dev/seq.in",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/dev/seq.out",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/dev/label",
],
"syllable_test": [
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/test/seq.in",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/test/seq.out",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/test/label",
],
},
"word": {
"word_train": [
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/train/seq.in",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/train/seq.out",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/train/label",
],
"word_dev": [
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/dev/seq.in",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/dev/seq.out",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/dev/label",
],
"word_test": [
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/test/seq.in",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/test/seq.out",
"https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/test/label",
],
},
}
}
_LOCAL = False
_LANGUAGES = ["vie"]
_SUPPORTED_TASKS = [Tasks.INTENT_CLASSIFICATION, Tasks.SLOT_FILLING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
def config_constructor_intent_cls(schema: str, version: str, phoatis_subset: str = "syllable") -> SEACrowdConfig:
assert phoatis_subset == "syllable" or phoatis_subset == "word"
return SEACrowdConfig(
name="phoatis_intent_cls_{phoatis_subset}_{schema}".format(phoatis_subset=phoatis_subset.lower(), schema=schema),
version=version,
description="PhoATIS Intent Classification: {subset} {schema} schema".format(subset=phoatis_subset, schema=schema),
schema=schema,
subset_id=phoatis_subset,
)
def config_constructor_slot_filling(schema: str, version: str, phoatis_subset: str = "syllable") -> SEACrowdConfig:
assert phoatis_subset == "syllable" or phoatis_subset == "word"
return SEACrowdConfig(
name="phoatis_slot_filling_{phoatis_subset}_{schema}".format(phoatis_subset=phoatis_subset.lower(), schema=schema),
version=version,
description="PhoATIS Slot Filling: {subset} {schema} schema".format(subset=phoatis_subset, schema=schema),
schema=schema,
subset_id=phoatis_subset,
)
class PhoATIS(datasets.GeneratorBasedBuilder):
"""This is first public intent detection and slot filling dataset for Vietnamese. The data contains 5871 English utterances from ATIS that are manually translated by professional translators into Vietnamese."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
# BUILDER_CONFIGS = [config_constructor_intent_cls("source", _SOURCE_VERSION, subset) for subset in ["syllable", "word"]]
BUILDER_CONFIGS = []
BUILDER_CONFIGS.extend([config_constructor_intent_cls("seacrowd_text", _SEACROWD_VERSION, subset) for subset in ["syllable", "word"]])
# BUILDER_CONFIGS.extend([config_constructor_slot_filling("source", _SOURCE_VERSION, subset) for subset in ["syllable", "word"]])
BUILDER_CONFIGS.extend([config_constructor_slot_filling("seacrowd_seq_label", _SEACROWD_VERSION, subset) for subset in ["syllable", "word"]])
BUILDER_CONFIGS.extend(
[ # Default config
SEACrowdConfig(
name="phoatis_source",
version=SOURCE_VERSION,
description="PhoATIS source schema (Syllable version)",
schema="source",
subset_id="syllable",
),
SEACrowdConfig(
name="phoatis_intent_cls_seacrowd_text",
version=SEACROWD_VERSION,
description="PhoATIS Intent Classification SEACrowd schema (Syllable version)",
schema="seacrowd_text",
subset_id="syllable",
),
SEACrowdConfig(
name="phoatis_slot_filling_seacrowd_seq_label",
version=SEACROWD_VERSION,
description="PhoATIS Slot Filling SEACrowd schema (Syllable version)",
schema="seacrowd_seq_label",
subset_id="syllable",
),
]
)
DEFAULT_CONFIG_NAME = "phoatis_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"intent_label": datasets.Value("string"),
"slot_label": datasets.Sequence(datasets.Value("string")),
}
)
elif self.config.schema == "seacrowd_text":
with open("./seacrowd/sea_datasets/phoatis/intent_label.txt", "r+", encoding="utf8") as fw:
intent_label = fw.read()
intent_label = intent_label.split("\n")
features = schemas.text_features(intent_label)
elif self.config.schema == "seacrowd_seq_label":
with open("./seacrowd/sea_datasets/phoatis/slot_label.txt", "r+", encoding="utf8") as fw:
slot_label = fw.read()
slot_label = slot_label.split("\n")
features = schemas.seq_label_features(slot_label)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
schema = self.config.subset_id
urls = _URLS[_DATASETNAME][schema]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir[f"{schema}_train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir[f"{schema}_test"],
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir[f"{schema}_dev"],
"split": "dev",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
with open(filepath[0], "r+", encoding="utf8") as fw:
data_input = fw.read()
data_input = data_input.split("\n")
with open(filepath[1], "r+", encoding="utf8") as fw:
data_slot = fw.read()
data_slot = data_slot.split("\n")
with open(filepath[2], "r+", encoding="utf8") as fw:
data_intent = fw.read()
data_intent = data_intent.split("\n")
if self.config.schema == "source":
for idx, text in enumerate(data_input):
example = {}
example["id"] = str(idx)
example["text"] = text
example["intent_label"] = data_intent[idx]
example["slot_label"] = data_slot[idx].split()
yield example["id"], example
elif self.config.schema == "seacrowd_text":
for idx, text in enumerate(data_input):
example = {}
example["id"] = str(idx)
example["text"] = text
example["label"] = data_intent[idx]
yield example["id"], example
elif self.config.schema == "seacrowd_seq_label":
for idx, text in enumerate(data_input):
example = {}
example["id"] = str(idx)
example["tokens"] = text.split()
example["labels"] = data_slot[idx].split()
yield example["id"], example
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