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import os |
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import datasets |
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import pandas as pd |
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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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_argumentation_sentences_DESCRIPTION = """\ |
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Argumentation sentences is a translated corpus for the task of identifying stance in relation to a topic. It consists of sentences labeled with pro, con or non in relation to one of six topics. |
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The original dataset can be found here https://github.com/trtm/AURC. The test set is manually corrected translations, the training set is machine translated. """ |
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_argumentation_sentences_DESCRIPTION_CITATION = """\ |
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""" |
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_URL = "https://huggingface.co/datasets/sbx/superlim-2/resolve/main/data/" |
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_TASKS = { |
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"absabank-imm": "absabank-imm", |
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"argumentation_sent":"argumentation-sentences", |
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"dalaj-ged": "dalaj-ged-superlim", |
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"swesim_relatedness": "supersim-superlim-relatedness", |
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"swesim_similarity": "supersim-superlim-similarity", |
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"sweana": "sweanalogy", |
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"swefaq": "swefaq", |
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"swepar": "sweparaphrase", |
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"swesat": "swesat-synonyms", |
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"swewic": "swewic", |
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"swefracas": "swefracas", |
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"swediagnostics": "swediagnostics" |
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} |
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class SuperLimConfig(datasets.BuilderConfig): |
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"""BuilderConfig for SuperLim.""" |
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def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs): |
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"""BuilderConfig for SuperLim. |
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Args: |
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features: `list[string]`, list of the features that will appear in the |
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feature dict. Should not include "label". |
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data_url: `string`, url to download the zip file from. |
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citation: `string`, citation for the data set. |
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url: `string`, url for information about the data set. |
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label_classes: `list[string]`, the list of classes for the label if the |
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label is present as a string. Non-string labels will be cast to either |
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'False' or 'True'. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(SuperLimConfig, self).__init__(version=datasets.Version("2.0.0"), **kwargs) |
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self.features = features |
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self.label_classes = label_classes |
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self.data_url = data_url |
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self.citation = citation |
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self.url = url |
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class SuperLim(datasets.GeneratorBasedBuilder): |
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"""The SuperLim benchmark.""" |
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VERSION = datasets.Version("2.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="absabank-imm", version=VERSION, description=_DaLAJ_DESCRIPTION), |
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datasets.BuilderConfig(name="dalaj-ged", version=VERSION, description=_DaLAJ_DESCRIPTION), |
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datasets.BuilderConfig(name="swesim_relatedness", version=VERSION, description=_SweSim_DESCRIPTION), |
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datasets.BuilderConfig(name="swesim_similarity", version=VERSION, description=_SweSim_DESCRIPTION), |
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datasets.BuilderConfig(name="sweana", version=VERSION, description=_SweAna_DESCRIPTION), |
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datasets.BuilderConfig(name="swefaq", version=VERSION, description=_SweFaq_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="swewic", version=VERSION, description=_SweWic_DESCRIPTION), |
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datasets.BuilderConfig(name="argumentation_sent", version=VERSION, description=_argumentation_sentences_DESCRIPTION), |
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datasets.BuilderConfig(name="swefracas", version=VERSION, description=_argumentation_sentences_DESCRIPTION), |
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datasets.BuilderConfig(name="swediagnostics", version=VERSION, description=_SweDiag_DESCRIPTION) |
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] |
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def _info(self): |
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if self.config.name == "dalaj-ged": |
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features = datasets.Features( |
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{ |
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"sentence": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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"meta": datasets.Value("string") |
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} |
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) |
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elif self.config.name == 'absabank-imm': |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"label": datasets.Value(dtype='float32') |
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} |
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) |
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elif self.config.name == "sweana": |
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features = datasets.Features( |
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{ |
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"pair1_element1": datasets.Value("string"), |
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"pair1_element2": datasets.Value("string"), |
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"pair2_element1": datasets.Value("string"), |
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"pair2_element2": datasets.Value("string"), |
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"category": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == "swefaq": |
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features = datasets.Features( |
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{ |
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"category_id": datasets.Value("string"), |
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"candidate_answers": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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"meta": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == "swepar": |
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features = datasets.Features( |
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{ |
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"genre": datasets.Value("string"), |
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"file": datasets.Value("string"), |
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"sentence_1": datasets.Value("string"), |
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"sentence_2": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == "swesat": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"item": datasets.Value("string"), |
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"candidate_answers": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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"meta": datasets.Value("string") |
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} |
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) |
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elif self.config.name == "swesim_relatedness": |
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features = datasets.Features( |
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{ |
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"word_1": datasets.Value("string"), |
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"word_2": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == "swesim_similarity": |
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features = datasets.Features( |
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{ |
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"word_1": datasets.Value("string"), |
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"word_2": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == "swewic": |
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features = datasets.Features( |
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{ |
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"idx": datasets.Value("string"), |
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"first": datasets.Value("string"), |
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"second": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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"meta": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == 'argumentation_sent': |
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features = datasets.Features( |
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{ |
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"topic": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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"sentence": datasets.Value("string") |
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} |
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) |
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elif self.config.name == 'swefracas': |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"original_id": datasets.Value("string"), |
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"attribute": datasets.Value("string"), |
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"value": datasets.Value("string") |
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} |
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) |
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elif self.config.name == 'swediagnostics': |
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features = datasets.Features( |
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{ |
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'lexical_semantics': datasets.Value("string"), |
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'predicate_argument_structure': datasets.Value("string"), |
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'logic': datasets.Value("string"), |
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'knowledge': datasets.Value("string"), |
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'domain': datasets.Value("string"), |
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'premise': datasets.Value("string"), |
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'hypothesis': datasets.Value("string"), |
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'label':datasets.Value("string") |
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} |
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) |
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elif self.config.name == 'swedn': |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"headline": datasets.Value("string"), |
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"summary": datasets.Value("string"), |
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"article": datasets.Value("string"), |
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"article_category": datasets.Value("string") |
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} |
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) |
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else: |
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raise ValueError(f"Subset {self.config.name} does not exist.") |
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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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file_format = 'jsonl' |
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splits = [] |
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DATA_FOLDER = 'supersim-superlim' if self.config.name.startswith('swesim') else _TASKS[self.config.name] |
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data_dir_test = dl_manager.download_and_extract(os.path.join(_URL,DATA_FOLDER,f"{_TASKS[self.config.name]}_test.{file_format}")) |
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split_test = datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": data_dir_test, |
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"split": "test" |
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}, |
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) |
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splits.append(split_test) |
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if self.config.name in ("absabank-imm", "dalaj-ged", "swefaq", "swewic", "swedn", "swepar"): |
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data_dir_dev = dl_manager.download_and_extract(os.path.join(_URL,DATA_FOLDER,f"{_TASKS[self.config.name]}_dev.{file_format}")) |
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split_dev = datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": data_dir_dev, |
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"split": "dev", |
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}, |
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) |
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splits.append(split_dev) |
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if self.config.name in ("absabank-imm", "dalaj-ged", "swefaq", "swewic", "argumentation_sent", "swedn", "swepar"): |
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data_dir_train = dl_manager.download_and_extract(os.path.join(_URL,DATA_FOLDER,f"{_TASKS[self.config.name]}_train.{file_format}")) |
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split_train = datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": data_dir_train, |
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"split": "train", |
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}, |
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) |
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splits.append(split_train) |
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return splits |
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def _generate_examples(self, filepath, split): |
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df = pd.read_json(filepath, lines=True) |
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for key, row in df.iterrows(): |
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if self.config.name == "dalaj-ged": |
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yield key, { |
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"sentence": row["sentence"], |
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"label": row["label"], |
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"meta": row["meta"], |
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} |
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elif self.config.name == "absabank-imm": |
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yield key, { |
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"id": row['id'], |
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"text": row["text"], |
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"label": row["label"], |
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} |
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elif self.config.name == "sweana": |
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yield key, { |
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"pair1_element1": row["pair1_element1"], |
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"pair1_element2": row["pair1_element2"], |
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"pair2_element1": row["pair2_element1"], |
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"pair2_element2": row["pair2_element2"], |
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"category": row["category"], |
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} |
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elif self.config.name == "swefaq": |
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yield key, { |
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"category_id": row['category_id'], |
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"question": row["question"], |
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"candidate_answers": row['candidate_answers'], |
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"label": row["label"], |
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"meta": row['meta'], |
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} |
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elif self.config.name == "swepar": |
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yield key, { |
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"genre": row["genre"], |
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"file": row["file"], |
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"sentence_1": row["sentence_1"], |
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"sentence_2": row["sentence_2"], |
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"label": row["label"], |
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} |
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elif self.config.name == "swesat": |
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yield key, { |
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"id": row["id"], |
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"item": row["item"], |
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"candidate_answers": row["candidate_answers"], |
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"label": row["label"], |
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"meta": row["meta"], |
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} |
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elif self.config.name == "swesim_relatedness": |
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yield key, { |
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"word_1": row["word_1"], |
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"word_2": row["word_2"], |
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"label": row["label"], |
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} |
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elif self.config.name == "swesim_similarity": |
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yield key, { |
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"word_1": row["word_1"], |
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"word_2": row["word_2"], |
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"label": row["label"], |
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} |
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elif self.config.name == "swewgr": |
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yield key, { |
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"text": row["text"], |
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"challenge": row["challenge"], |
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"responses": row["responses"], |
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} |
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elif self.config.name == "swewic": |
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yield key, { |
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"idx": row["idx"], |
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"first": row["first"], |
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"second": row["second"], |
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"label": row["label"], |
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"meta": row["meta"], |
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} |
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elif self.config.name == "swewsc": |
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yield key, { |
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"passage": row["passage"], |
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"challenge_text": row["challenge_text"], |
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"response_text": row["response_text"], |
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"challenge_begin":row["challenge_begin"], |
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"challenge_end":row["challenge_end"], |
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"response_begin":row["response_begin"], |
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"response_end":row["response_end"], |
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"label":row["label"], |
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} |
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|
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elif self.config.name == "argumentation_sent": |
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yield key, { |
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"topic": row["topic"], |
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"label": row["label"], |
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"sentence" : row["sentence"], |
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} |
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|
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elif self.config.name == "swediagnostics": |
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yield key, { |
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'lexical_semantics': row['lexical_semantics'], |
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'predicate_argument_structure': row['predicate_argument_structure'], |
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'logic': row['logic'], |
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'knowledge': row['knowledge'], |
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'domain': row['domain'], |
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'premise': row['premise'], |
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'hypothesis': row['hypothesis'], |
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'label': row['label'] |
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} |
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elif self.config.name == "swefracas": |
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yield key, { |
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'original_id': row['original_id'], |
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'attribute': row['attribute'], |
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'value': row['value'] |
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} |
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elif self.config.name == "swedn": |
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yield key, { |
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'id': row['id'], |
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'headline': row['headline'], |
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'summary': row['summary'], |
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'article': row['article'], |
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'article_category': row['article_category'] |
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} |
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else: |
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raise ValueError(f"Subset {self.config.name} does not exist") |