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