| 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}") |
|
|
