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"""The SuperGLUE benchmark.""" |
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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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""" |
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_DESCRIPTION = """\ |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "" |
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_SUPERLIM_CITATION = """\ |
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Yvonne Adesam, Aleksandrs Berdicevskis, Felix Morger (2020): SwedishGLUE – Towards a Swedish Test Set for Evaluating Natural Language Understanding Models BibTeX |
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[1] Original Absabank: |
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Jacobo Rouces, Lars Borin, Nina Tahmasebi (2020): Creating an Annotated Corpus for Aspect-Based Sentiment Analysis in Swedish, in Proceedings of the 5th conference in Digital Humanities in the Nordic Countries, Riga, Latvia, October 21-23, 2020. BibTeX |
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[2] DaLAJ: |
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Volodina, Elena, Yousuf Ali Mohammed, and Julia Klezl (2021). DaLAJ - a dataset for linguistic acceptability judgments for Swedish. In Proceedings of the 10th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2021). Linköping Electronic Conference Proceedings 177:3, s. 28-37. https://ep.liu.se/ecp/177/003/ecp2021177003.pdf |
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[3] Analogy: |
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Tosin Adewumi, Foteini Liwicki, Markus Liwicki. (2020). Corpora compared: The case of the Swedish Gigaword & Wikipedia corpora. In: Proceedings of the 8th SLTC, Gothenburg. arXiv preprint arXiv:2011.03281 |
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[4] Swedish Test Set for SemEval 2020 Task 1: |
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Unsupervised Lexical Semantic Change Detection: Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, Nina Tahmasebi (2020): SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection, in Proceedings of the Fourteenth Workshop on Semantic Evaluation (SemEval2020), Barcelona, Spain (Online), December 12, 2020. BibTeX |
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[5] Winogender: |
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Saga Hansson, Konstantinos Mavromatakis, Yvonne Adesam, Gerlof Bouma and Dana Dannélls (2021). The Swedish Winogender Dataset. In The 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021), Reykjavik. |
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[6] SuperSim: |
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Hengchen, Simon and Tahmasebi, Nina (2021). SuperSim: a test set for word similarity and relatedness in Swedish. In The 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021), Reykjavik. arXiv preprint arXiv:2014.05228 |
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""" |
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_SUPERLIM_DESCRIPTION = """\ |
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SuperLim, A standardized suite for evaluation and analysis of Swedish natural language understanding systems. |
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""" |
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_DaLAJ_DESCRIPTION = """\ |
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Determine whether a sentence is correct Swedish or not. |
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""" |
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_DaLAJ_CITATION = """\ |
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[1] Original Absabank: |
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Jacobo Rouces, Lars Borin, Nina Tahmasebi (2020): Creating an Annotated Corpus for Aspect-Based Sentiment Analysis in Swedish, in Proceedings of the 5th conference in Digital Humanities in the Nordic Countries, Riga, Latvia, October 21-23, 2020. BibTeX |
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[2] DaLAJ: |
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Volodina, Elena, Yousuf Ali Mohammed, and Julia Klezl (2021). DaLAJ - a dataset for linguistic acceptability judgments for Swedish. In Proceedings of the 10th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2021). Linköping Electronic Conference Proceedings 177:3, s. 28-37. https://ep.liu.se/ecp/177/003/ecp2021177003.pdf |
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""" |
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_SweAna_DESCRIPTION = """\ |
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The Swedish analogy test set follows the format of the original Google version. However, it is bigger and balanced across the 2 major categories, |
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having a total of 20,638 samples, made up of 10,381 semantic and 10,257 syntactic samples. It is also roughly balanced across the syntactic subsections. |
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There are 5 semantic subsections and 6 syntactic subsections. The dataset was constructed, partly using the samples in the English version, |
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with the help of tools dedicated to Swedish translation and it was proof-read for corrections by two native speakers (with a percentage agreement of 98.93\%).""" |
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_SweAna_CITATION = """\ |
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[1] Original Absabank: |
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Jacobo Rouces, Lars Borin, Nina Tahmasebi (2020): Creating an Annotated Corpus for Aspect-Based Sentiment Analysis in Swedish, in Proceedings of the 5th conference in Digital Humanities in the Nordic Countries, Riga, Latvia, October 21-23, 2020. BibTeX |
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""" |
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_SweDiag_DESCRIPTION = """\ |
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Färdig preliminär översättning av SuperGLUE diagnostik. Datan innehåller alla ursprungliga annoterade satspar från SuperGLUE tillsammans |
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med deras svenska översättningar.""" |
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_SweDiag_CITATION = """\ |
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""" |
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_SweFaq_DESCRIPTION = """\ |
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Vanliga frågor från svenska myndigheters webbsidor med svar i randomiserad ordning""" |
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_SweFaq_CITATION = """\ |
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""" |
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_SweFracas_DESCRIPTION = """\ |
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A textual inference/entailment problem set, derived from FraCas. The original English Fracas [1] was converted to html and edited by Bill MacCartney [2], |
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and then automatically translated to Swedish by Peter Ljunglöf and Magdalena Siverbo [3]. The current tabular form of the set was created by Aleksandrs Berdicevskis |
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by merging the Swedish and English versions and removing some of the problems. Finally, Lars Borin went through all the translations, correcting and Swedifying them manually. |
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As a result, many translations are rather liberal and diverge noticeably from the English original.""" |
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_SweFracas_CITATION = """\ |
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""" |
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_SwePar_DESCRIPTION = """\ |
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SweParaphrase is a subset of the automatically translated Swedish Semantic Textual Similarity dataset (Isbister and Sahlgren, 2020). |
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It consists of 165 manually corrected Swedish sentence pairs paired with the original English sentences and their similarity scores |
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ranging between 0 (no meaning overlap) and 5 (meaning equivalence). These scores were taken from the English data, they were assigned |
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by Crowdsourcing through Mechanical Turk. Each sentence pair belongs to one genre (e.g. news, forums or captions). |
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The task is to determine how similar two sentences are.""" |
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_SwePar_CITATION = """\ |
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""" |
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_SweSat_DESCRIPTION = """\ |
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The dataset provides a gold standard for Swedish word synonymy/definition. The test items are collected from the Swedish Scholastic |
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Aptitude Test (högskoleprovet), currently spanning the years 2006--2021 and 822 vocabulary test items. The task for the tested system |
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is to determine which synonym or definition of five alternatives is correct for each test item. |
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""" |
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_SweSat_CITATION = """\ |
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""" |
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_SweSim_DESCRIPTION = """\ |
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SuperSim is a large-scale similarity and relatedness test set for Swedish built with expert human judgments. The test set is composed of 1360 word-pairs independently judged for both relatedness and similarity by five annotators.""" |
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_SweWgr_DESCRIPTION = """\ |
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The SweWinogender test set is diagnostic dataset to measure gender bias in coreference resolution. It is modelled after the English Winogender benchmark, |
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and is released with reference statistics on the distribution of men and women between occupations and the association between gender and occupation in modern corpus material.""" |
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_SweWsc_DESCRIPTION = """\ |
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SweWinograd is a pronoun resolution test set, containing constructed items in the style of Winograd schema’s. The interpretation of the target pronouns is determined by (common sense) |
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reasoning and knowledge, and not by syntactic constraints, lexical distributional information or discourse structuring patterns. |
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The dataset contains 90 multiple choice with multiple correct answers test items.""" |
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_SweWic_DESCRIPTION = """\ |
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The Swedish Word-in-Context dataset provides a benchmark for evaluating distributional models of word meaning, in particular context-sensitive/dynamic models. Constructed following the principles of the (English) |
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Word-in-Context dataset, SweWiC consists of 1000 sentence pairs, where each sentence in a pair contains an occurence of a potentially ambiguous focus word specific to that pair. The question posed to the tested |
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system is whether these two occurrences represent instances of the same word sense. There are 500 same-sense pairs and 500 different-sense pairs.""" |
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_URL = "https://huggingface.co/datasets/Ai-Sweden/SuperLim/resolve/main/data/" |
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_TASKS = { |
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"dalaj": "DaLAJ", |
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"sweana": "SweAna", |
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"swediag": "SweDiag", |
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"swefaq": "SweFaq", |
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"swefracas": "SweFracas", |
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"swepar": "SwePar", |
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"swesat": "SweSat", |
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"swesim": "SweSim", |
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"swewgr": "SweWgr", |
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"swewic": "SweWic", |
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"swewsc": "SweWsc" |
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} |
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class SuperLim(datasets.GeneratorBasedBuilder): |
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"""TODO: Short description of my dataset.""" |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="dalaj", version=VERSION, description=_DaLAJ_DESCRIPTION), |
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datasets.BuilderConfig(name="sweana", version=VERSION, description=_SweAna_DESCRIPTION), |
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datasets.BuilderConfig(name="swediag", version=VERSION, description=_SweDiag_DESCRIPTION), |
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datasets.BuilderConfig(name="swefaq", version=VERSION, description=_SweFaq_DESCRIPTION), |
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datasets.BuilderConfig(name="swefracas", version=VERSION, description=_SweFracas_DESCRIPTION), |
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datasets.BuilderConfig(name="swepar", version=VERSION, description=_SwePar_DESCRIPTION), |
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datasets.BuilderConfig(name="swesat", version=VERSION, description=_SweSat_DESCRIPTION), |
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datasets.BuilderConfig(name="swesim", version=VERSION, description=_SweSim_DESCRIPTION), |
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datasets.BuilderConfig(name="swewgr", version=VERSION, description=_SweWgr_DESCRIPTION), |
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datasets.BuilderConfig(name="swewic", version=VERSION, description=_SweWic_DESCRIPTION), |
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datasets.BuilderConfig(name="swewsc", version=VERSION, description=_SweWsc_DESCRIPTION), |
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] |
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DEFAULT_CONFIG_NAME = "swewic" |
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def _info(self): |
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if self.config.name == "dalaj": |
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features = datasets.Features( |
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{ |
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"original sentence": datasets.Value("string"), |
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"corrected sentence": datasets.Value("string") |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"sentence": datasets.Value("string"), |
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"option2": datasets.Value("string"), |
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"second_domain_answer": datasets.Value("string") |
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} |
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) |
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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): |
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data_dir = dl_manager.download_and_extract(os.path.join(_URL,self.config.data_url,self.config.task_name)) |
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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": os.path.join(data_dir, "train.csv"), |
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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": os.path.join(data_dir, "test.csv"), |
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"split": "test" |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "dev.csv"), |
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"split": "dev", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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with open(filepath, encoding="utf-8") as f: |
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for key, row in enumerate(f): |
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data = json.loads(row) |
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if self.config.name == "dalaj": |
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yield key, { |
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"original sentence": data["original sentence"], |
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"correct sentence": data["corrected sentence"], |
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} |
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else: |
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yield key, { |
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"sentence": data["sentence"], |
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"option2": data["option2"], |
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"second_domain_answer": "" if split == "test" else data["second_domain_answer"], |
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} |
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