| 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}") | |