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import csv |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import SCHEMA_TO_FEATURES, Licenses, Tasks |
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_CITATION = """\ |
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@inproceedings{lin2022fewshot, |
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author = {Xi Victoria Lin and |
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Todor Mihaylov and |
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Mikel Artetxe and |
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Tianlu Wang and |
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Shuohui Chen and |
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Daniel Simig and |
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Myle Ott and |
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Naman Goyal and |
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Shruti Bhosale and |
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Jingfei Du and |
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Ramakanth Pasunuru and |
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Sam Shleifer and |
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Punit Singh Koura and |
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Vishrav Chaudhary and |
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Brian O'Horo and |
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Jeff Wang and |
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Luke Zettlemoyer and |
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Zornitsa Kozareva and |
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Mona T. Diab and |
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Veselin Stoyanov and |
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Xian Li}, |
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editor = {Yoav Goldberg and |
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Zornitsa Kozareva and |
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Yue Zhang}, |
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title = {Few-shot Learning with Multilingual Generative Language Models}, |
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booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural |
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Language Processing, {EMNLP} 2022, Abu Dhabi, United Arab Emirates, |
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December 7-11, 2022}, |
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pages = {9019--9052}, |
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publisher = {Association for Computational Linguistics}, |
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year = {2022}, |
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url = {https://doi.org/10.18653/v1/2022.emnlp-main.616}, |
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doi = {10.18653/V1/2022.EMNLP-MAIN.616}, |
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} |
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""" |
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_DATASETNAME = "xstorycloze" |
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_DESCRIPTION = """\ |
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XStoryCloze consists of the professionally translated version of the English StoryCloze |
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dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by |
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Meta AI. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/juletxara/xstory_cloze" |
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_LANGUAGES = ["ind", "mya"] |
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_LICENSE = Licenses.CC_BY_SA_4_0.value |
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_LOCAL = False |
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_BASE_URL = "https://huggingface.co/datasets/juletxara/xstory_cloze/resolve/main/spring2016.val.{lang}.tsv.split_20_80_{split}.tsv" |
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_SUPPORTED_TASKS = [Tasks.COMMONSENSE_REASONING] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class XStoryClozeDataset(datasets.GeneratorBasedBuilder): |
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"""XStoryCloze subset for Indonesian and Burmese language.""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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SEACROWD_SUBSET = ["id", "my"] |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{subset}_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"{_DATASETNAME} {subset} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}_{subset}", |
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) |
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for subset in SEACROWD_SUBSET |
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] + [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{subset}_seacrowd_qa", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"{_DATASETNAME} {subset} SEACrowd schema", |
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schema="seacrowd_qa", |
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subset_id=f"{_DATASETNAME}_{subset}", |
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) |
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for subset in SEACROWD_SUBSET |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{SEACROWD_SUBSET[0]}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"story_id": datasets.Value("string"), |
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"input_sentence_1": datasets.Value("string"), |
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"input_sentence_2": datasets.Value("string"), |
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"input_sentence_3": datasets.Value("string"), |
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"input_sentence_4": datasets.Value("string"), |
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"sentence_quiz1": datasets.Value("string"), |
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"sentence_quiz2": datasets.Value("string"), |
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"answer_right_ending": datasets.Value("int32"), |
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} |
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) |
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elif self.config.schema == "seacrowd_qa": |
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features = SCHEMA_TO_FEATURES["QA"] |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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lang = self.config.name.split("_")[1] |
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filepaths = dl_manager.download_and_extract( |
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{ |
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"train": _BASE_URL.format(lang=lang, split="train"), |
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"test": _BASE_URL.format(lang=lang, split="eval"), |
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} |
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) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": filepaths["train"], |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": filepaths["test"], |
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"split": "test", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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with open(filepath, encoding="utf-8") as f: |
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data = csv.reader(f, quotechar='"', delimiter="\t", quoting=csv.QUOTE_ALL, skipinitialspace=True) |
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_ = next(data) |
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if self.config.schema == "source": |
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for id, row in enumerate(data): |
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yield id, { |
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"story_id": row[0], |
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"input_sentence_1": row[1], |
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"input_sentence_2": row[2], |
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"input_sentence_3": row[3], |
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"input_sentence_4": row[4], |
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"sentence_quiz1": row[5], |
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"sentence_quiz2": row[6], |
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"answer_right_ending": int(row[7]), |
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} |
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elif self.config.schema == "seacrowd_qa": |
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for id, row in enumerate(data): |
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question = " ".join(row[1:5]) |
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choices = [row[5], row[6]] |
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yield id, { |
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"id": str(id), |
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"question_id": row[0], |
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"document_id": None, |
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"question": question, |
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"type": "multiple_choice", |
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"choices": choices, |
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"context": None, |
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"answer": [choices[int(row[7]) - 1]], |
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"meta": {}, |
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} |
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