| 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 = [] |
| BUILDER_CONFIGS.extend([config_constructor_intent_cls("seacrowd_text", _SEACROWD_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( |
| [ |
| 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 |
|
|
