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AI-Sweden
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SuperLim

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Swedish

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	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""The SuperGLUE benchmark."""

	import json
	import os
	import datasets
	import pandas as pd

	_CITATION = """\
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	"""

	_HOMEPAGE = ""

	_LICENSE = ""

	_SUPERLIM_CITATION = """\
	Yvonne Adesam, Aleksandrs Berdicevskis, Felix Morger (2020): SwedishGLUE – Towards a Swedish Test Set for Evaluating Natural Language Understanding Models BibTeX
	[1] Original Absabank:
	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
	[2] DaLAJ:
	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
	[3] Analogy:
	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
	[4] Swedish Test Set for SemEval 2020 Task 1:
	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
	[5] Winogender:
	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.
	[6] SuperSim:
	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

	"""

	_SUPERLIM_DESCRIPTION = """\
	SuperLim, A standardized suite for evaluation and analysis of Swedish natural language understanding systems.

	"""
	_DaLAJ_DESCRIPTION = """\
	Determine whether a sentence is correct Swedish or not.
	"""
	_DaLAJ_CITATION = """\
	[1] Original Absabank:
	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
	[2] DaLAJ:
	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
	"""

	_SweAna_DESCRIPTION = """\
	The Swedish analogy test set follows the format of the original Google version. However, it is bigger and balanced across the 2 major categories,
	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.
	There are 5 semantic subsections and 6 syntactic subsections. The dataset was constructed, partly using the samples in the English version,
	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\%)."""
	_SweAna_CITATION = """\
	[1] Original Absabank:
	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
	"""

	_SweDiag_DESCRIPTION = """\
	Färdig preliminär översättning av SuperGLUE diagnostik. Datan innehåller alla ursprungliga annoterade satspar från SuperGLUE tillsammans
	med deras svenska översättningar."""
	_SweDiag_CITATION = """\
	"""
	_SweFaq_DESCRIPTION = """\
	Vanliga frågor från svenska myndigheters webbsidor med svar i randomiserad ordning"""
	_SweFaq_CITATION = """\
	"""
	_SweFracas_DESCRIPTION = """\
	A textual inference/entailment problem set, derived from FraCas. The original English Fracas [1] was converted to html and edited by Bill MacCartney [2],
	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
	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.
	As a result, many translations are rather liberal and diverge noticeably from the English original."""
	_SweFracas_CITATION = """\
	"""
	_SwePar_DESCRIPTION = """\
	SweParaphrase is a subset of the automatically translated Swedish Semantic Textual Similarity dataset (Isbister and Sahlgren, 2020).
	It consists of 165 manually corrected Swedish sentence pairs paired with the original English sentences and their similarity scores
	ranging between 0 (no meaning overlap) and 5 (meaning equivalence). These scores were taken from the English data, they were assigned
	by Crowdsourcing through Mechanical Turk. Each sentence pair belongs to one genre (e.g. news, forums or captions).
	The task is to determine how similar two sentences are."""
	_SwePar_CITATION = """\
	"""
	_SweSat_DESCRIPTION = """\
	The dataset provides a gold standard for Swedish word synonymy/definition. The test items are collected from the Swedish Scholastic
	Aptitude Test (högskoleprovet), currently spanning the years 2006--2021 and 822 vocabulary test items. The task for the tested system
	is to determine which synonym or definition of five alternatives is correct for each test item.
	"""
	_SweSat_CITATION = """\
	"""

	_SweSim_DESCRIPTION = """\
	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."""

	_SweWgr_DESCRIPTION = """\
	The SweWinogender test set is diagnostic dataset to measure gender bias in coreference resolution. It is modelled after the English Winogender benchmark,
	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."""

	_SweWsc_DESCRIPTION = """\
	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)
	reasoning and knowledge, and not by syntactic constraints, lexical distributional information or discourse structuring patterns.
	The dataset contains 90 multiple choice with multiple correct answers test items."""

	_SweWic_DESCRIPTION = """\
	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)
	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
	system is whether these two occurrences represent instances of the same word sense. There are 500 same-sense pairs and 500 different-sense pairs."""

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URL = "https://huggingface.co/datasets/AI-Sweden/SuperLim/resolve/main/data/"
	_TASKS = {
	"dalaj": "DaLAJ",
	"sweana": "SweAna",
	"swediag": "SweDiag",
	"swefaq": "SweFaq",
	"swefracas": "SweFracas",
	"swepar": "SwePar",
	"swesat": "SweSat",
	"swesim_relatedness": "SweSim_relatedness",
	"swesim_similarity": "SweSim_similarity",
	"swewgr": "SweWgr",
	"swewic": "SweWic",
	"swewsc": "SweWsc"
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class SuperLim(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.1.0")

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="dalaj", version=VERSION, description=_DaLAJ_DESCRIPTION),
	datasets.BuilderConfig(name="sweana", version=VERSION, description=_SweAna_DESCRIPTION),
	datasets.BuilderConfig(name="swediag", version=VERSION, description=_SweDiag_DESCRIPTION),
	datasets.BuilderConfig(name="swefaq", version=VERSION, description=_SweFaq_DESCRIPTION),
	datasets.BuilderConfig(name="swefracas", version=VERSION, description=_SweFracas_DESCRIPTION),
	datasets.BuilderConfig(name="swepar", version=VERSION, description=_SwePar_DESCRIPTION),
	datasets.BuilderConfig(name="swesat", version=VERSION, description=_SweSat_DESCRIPTION),
	datasets.BuilderConfig(name="swesim_relatedness", version=VERSION, description=_SweSim_DESCRIPTION),
	datasets.BuilderConfig(name="swesim_similarity", version=VERSION, description=_SweSim_DESCRIPTION),
	datasets.BuilderConfig(name="swewgr", version=VERSION, description=_SweWgr_DESCRIPTION),
	datasets.BuilderConfig(name="swewic", version=VERSION, description=_SweWic_DESCRIPTION),
	datasets.BuilderConfig(name="swewsc", version=VERSION, description=_SweWsc_DESCRIPTION),
	]


	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	if self.config.name == "dalaj": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"original_sentence": datasets.Value("string"),
	"corrected_sentence": datasets.Value("string"),
	"error_indices": datasets.Value("string"),
	"corrected_indices": datasets.Value("string"),
	"error_corr_pair": datasets.Value("string"),
	"error_label": datasets.Value("string"),
	"l1": datasets.Value("string"),
	"approximate_level": datasets.Value("string"),
	# These are the features of your dataset like images, labels ...
	}
	)
	elif self.config.name == "sweana":
	features = datasets.Features(
	{
	"a": datasets.Value("string"),
	"b": datasets.Value("string"),
	"c": datasets.Value("string"),
	"d": datasets.Value("string"),
	"relation": datasets.Value("string"),
	}
	)
	elif self.config.name == "swediag":
	features = datasets.Features(
	{
	"premise": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.Value("string"),
	}
	)
	elif self.config.name == "swefaq":
	features = datasets.Features(
	{
	"question": datasets.Value("string"),
	"candidate_answer": datasets.Value("string"),
	"correct_answer": datasets.Value("string"),
	}
	)
	elif self.config.name == "swefracas":
	features = datasets.Features(
	{
	"answer": datasets.Value("string"),
	"question": datasets.Value("string"),
	"premiss_1": datasets.Value("string"),
	"premiss_2": datasets.Value("string"),
	"premiss_3": datasets.Value("string"),
	"premiss_4": datasets.Value("string"),
	"premiss_5": datasets.Value("string"),
	}
	)
	elif self.config.name == "swepar":
	features = datasets.Features(
	{
	"sentence_1": datasets.Value("string"),
	"sentence_2": datasets.Value("string"),
	"similarity_score": datasets.Value("string"),
	}
	)
	elif self.config.name == "swesat":
	features = datasets.Features(
	{
	"target_item": datasets.Value("string"),
	"answer_1": datasets.Value("string"),
	"answer_2": datasets.Value("string"),
	"answer_3": datasets.Value("string"),
	"answer_4": datasets.Value("string"),
	"answer_5": datasets.Value("string"),
	}
	)
	elif self.config.name == "swesim_relatedness":
	features = datasets.Features(
	{
	"word_1": datasets.Value("string"),
	"word_2": datasets.Value("string"),
	"relatedness": datasets.Value("string"),
	}
	)
	elif self.config.name == "swesim_similarity":
	features = datasets.Features(
	{
	"word_1": datasets.Value("string"),
	"word_2": datasets.Value("string"),
	"similarity": datasets.Value("string"),
	}
	)
	elif self.config.name == "swewgr":
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"challenge": datasets.Value("string"),
	"responses": datasets.Value("string"),
	}
	)
	elif self.config.name == "swewic":
	features = datasets.Features(
	{
	"sentence_1": datasets.Value("string"),
	"word_1": datasets.Value("string"),
	"sentence_2": datasets.Value("string"),
	"word_2": datasets.Value("string"),
	"same_sense": datasets.Value("string"),
	"start_1": datasets.Value("string"),
	"start_2": datasets.Value("string"),
	"end_1": datasets.Value("string"),
	"end_2": datasets.Value("string"),
	}
	)
	elif self.config.name == "swewsc":
	features = datasets.Features(
	{
	"passage": datasets.Value("string"),
	"challenge_text": datasets.Value("string"),
	"response_text": datasets.Value("string"),
	"challenge_begin": datasets.Value("string"),
	"challenge_end": datasets.Value("string"),
	"response_begin": datasets.Value("string"),
	"response_end": datasets.Value("string"),
	"label": datasets.Value("string")
	}
	)
	else: # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"option2": datasets.Value("string"),
	"second_domain_answer": datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	#urls = _URLS[self.config.name]

	if self.config.name == "dalaj":
	data_dir_test = dl_manager.download_and_extract(os.path.join(_URL,_TASKS[self.config.name],"test.csv"))
	data_dir_train = dl_manager.download_and_extract(os.path.join(_URL,_TASKS[self.config.name],"train.csv"))
	data_dir_dev = dl_manager.download_and_extract(os.path.join(_URL,_TASKS[self.config.name],"dev.csv"))

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir_train,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir_test,
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir_dev,
	"split": "dev",
	},
	),
	]
	else:
	data_dir_test = dl_manager.download_and_extract(os.path.join(_URL, _TASKS[self.config.name], "test.csv"))
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir_test,
	"split": "test"
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	df = pd.read_csv(filepath)
	for key, row in df.iterrows():
	if self.config.name == "dalaj":
	# Yields examples as (key, example) tuples
	yield key, {
	"original_sentence": row["original sentence"],
	"corrected_sentence": row["corrected sentence"],
	"error_indices": row["error indices"],
	"corrected_indices": row["corrected indices"],
	"error_corr_pair": row["error-corr pair"],
	"error_label": row["error label"],
	"l1": row["l1"],
	"approximate_level": row["approximate level"],
	}
	elif self.config.name == "sweana":
	yield key, {
	"a": row["A"],
	"b": row["B"],
	"c": row["C"],
	"d": row["D"],
	"relation": row["relation"],
	}
	elif self.config.name == "swediag":
	yield key, {
	"premise": row["Premise_SE"],
	"hypothesis": row["Hypothesis_SE"],
	"label": row["Label"],
	}
	elif self.config.name == "swefaq":
	yield key, {
	"question": row["question"],
	"candidate_answer": row["candidate_answer"],
	"correct_answer": row["correct_answer"],
	}
	elif self.config.name == "swefracas":
	yield key, {
	"answer": row["svar"],
	"question": row["fråga"],
	"premiss_1": row["premiss_1"],
	"premiss_2": row["premiss_2"],
	"premiss_3": row["premiss_3"],
	"premiss_4": row["premiss_4"],
	"premiss_5": row["premiss_5"],
	}
	elif self.config.name == "swepar":
	yield key, {
	"sentence_1": row["sentence_1"],
	"sentence_2": row["sentence_2"],
	"similarity_score": row["similarity_score"],
	}
	elif self.config.name == "swesat":
	yield key, {
	"target_item": row["target_item"],
	"answer_1": row["answer_1"],
	"answer_2": row["answer_2"],
	"answer_3": row["answer_3"],
	"answer_4": row["answer_4"],
	"answer_5": row["answer_5"],
	}
	elif self.config.name == "swesim_relatedness":
	yield key, {
	"word_1": row["Word 1 "],
	"word_2": row[" Word 2 "],
	"relatedness": row[" Average "],
	}
	elif self.config.name == "swesim_similarity":
	yield key, {
	"word_1": row["Word 1 "],
	"word_2": row[" Word 2 "],
	"similarity": row[" Average "],
	}
	elif self.config.name == "swewgr":
	yield key, {
	"text": row["text"],
	"challenge": row["challenge"],
	"responses": row["responses"],
	}
	elif self.config.name == "swewic":
	yield key, {
	"sentence_1": row["sentence1"],
	"word_1": row["word1"],
	"sentence_2": row["sentence2"],
	"word_2": row["word2"],
	"same_sense": row["same_sense"],
	"start_1": row["start1"],
	"end_1": row["end1"],
	"start_2": row["start2"],
	"end_2": row["end2"],
	}
	elif self.config.name == "swewsc":
	yield key, {
	"passage": row["passage"],
	"challenge_text": row["challenge_text"],
	"response_text": row["response_text"],
	"challenge_begin":row["challenge_begin"],
	"challenge_end":row["challenge_end"],
	"response_begin":row["response_begin"],
	"response_end":row["response_end"],
	"label":row["label"]
	}
	else:
	yield key, {
	"sentence": data["sentence"],
	"option2": data["option2"],
	"second_domain_answer": "" if split == "test" else data["second_domain_answer"],
	}