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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, List, Tuple
import datasets
from seacrowd.sea_datasets.mtop_intent_classification.labels import (
DOMAIN_LABELS, INTENT_LABELS)
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """\
@inproceedings{li-etal-2021-mtop,
author = {Li, Haoran and Arora, Abhinav and Chen, Shuochi and Gupta, Anchit and Gupta, Sonal and Mehdad, Yashar},
title = {MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
publisher = {Association for Computational Linguistics},
year = {2021},
url = {https://aclanthology.org/2021.eacl-main.257},
doi = {10.18653/v1/2021.eacl-main.257},
pages = {2950-2962},
}
"""
_LOCAL = False
_LANGUAGES = ["tha"]
_DATASETNAME = "mtop_intent_classification"
_DESCRIPTION = """
This dataset contains annotated utterances from 6 languages, including Thai,
for semantic parsing. Queries corresponding to the chosen domains are crowdsourced.
Two subsets are included in this dataset: 'domain' (eg. 'news', 'people', 'weather')
and 'intent' (eg. 'GET_MESSAGE', 'STOP_MUSIC', 'END_CALL')
"""
_HOMEPAGE = "https://huggingface.co/mteb"
_LICENSE = Licenses.CC_BY_SA_4_0.value # Found in original dataset (not HF) linked in paper
_URL = "https://huggingface.co/datasets/mteb/"
_SUPPORTED_TASKS = [Tasks.INTENT_CLASSIFICATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class MTOPIntentClassificationDataset(datasets.GeneratorBasedBuilder):
"""Dataset of Thai sentences and their domains or intents."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
SUBSETS = ["domain", "intent"]
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema for {subset} subset",
schema="source",
subset_id=f"{_DATASETNAME}_{subset}",
)
for subset in SUBSETS
] + [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_seacrowd_text",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
schema="seacrowd_text",
subset_id=f"{_DATASETNAME}_{subset}",
)
for subset in SUBSETS
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_domain_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("int64"),
"text": datasets.Value("string"),
"label": datasets.Value("int32"),
"label_text": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_text":
if self.config.subset_id == "domain":
labels = DOMAIN_LABELS
elif self.config.subset_id == "intent":
labels = INTENT_LABELS
else:
raise ValueError(f"Received unexpected schema name {self.config.name}")
features = schemas.text_features(label_names=labels)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
# dl_manager not used since dataloader uses HF `load_dataset`
return [datasets.SplitGenerator(name=split, gen_kwargs={"split": split._name}) for split in (datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST)]
def _load_hf_data_from_remote(self, split: str) -> datasets.DatasetDict:
"""Load dataset from HuggingFace."""
if self.config.subset_id not in ("domain", "intent"):
raise ValueError(f"Received unexpected schema name {self.config.name}")
HF_REMOTE_REF = "/".join(_URL.split("/")[-2:]) + f"mtop_{self.config.subset_id}"
_hf_dataset_source = datasets.load_dataset(HF_REMOTE_REF, "th", split=split)
return _hf_dataset_source
def _generate_examples(self, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
data = self._load_hf_data_from_remote(split=split)
for index, row in enumerate(data):
if self.config.schema == "source":
example = row
elif self.config.schema == "seacrowd_text":
example = {"id": str(index), "text": row["text"], "label": row["label_text"]}
yield index, example
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