add config builder file

superlim-2.py

@@ -0,0 +1,272 @@
+import datasets
+
+_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/sbx/superlim-2/tree/main/data/"
+_TASKS = {    
+    "absabank": "ABSAbank-lmm",
+    "dalaj": "DaLAJ",
+    "swesim_relatedness": "SuperSim_relatedness",
+    "swesim_similarity": "SuperSim_similarity",
+    "sweana": "SweAnalogy",
+    "swefaq": "SweFAQ",
+    "swepar": "SweParaphrase",
+    "swesat": "SweSAT-synonyms",
+    "swewic": "SweWIC"
+}
+
+
+class SuperLimConfig(datasets.BuilderConfig):
+    """BuilderConfig for SuperLim."""
+
+    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
+        """BuilderConfig for SuperLim.
+
+        Args:
+        features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+        data_url: `string`, url to download the zip file from.
+        citation: `string`, citation for the data set.
+        url: `string`, url for information about the data set.
+        label_classes: `list[string]`, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+        **kwargs: keyword arguments forwarded to super.
+        """
+        # Version history:
+        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
+        # 1.0.1: Change from the pre-release trial version of SuperLim (v1.9) to
+        #        the full release (v2.0).
+        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
+        # 0.0.2: Initial version.
+        super(SuperLimConfig, self).__init__(version=datasets.Version("2.0.0"), **kwargs)
+        self.features = features
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+class SuperLim(datasets.GeneratorBasedBuilder):
+    """The SuperLim benchmark."""
+
+    VERSION = datasets.Version("2.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="absabank", version=VERSION, description=_DaLAJ_DESCRIPTION),
+        datasets.BuilderConfig(name="dalaj", version=VERSION, description=_DaLAJ_DESCRIPTION),
+        datasets.BuilderConfig(name="swesim_relatedness", version=VERSION, description=_SweSim_DESCRIPTION),
+        datasets.BuilderConfig(name="swesim_similarity", version=VERSION, description=_SweSim_DESCRIPTION),        
+        datasets.BuilderConfig(name="sweana", version=VERSION, description=_SweAna_DESCRIPTION),
+        datasets.BuilderConfig(name="swefaq", version=VERSION, description=_SweFaq_DESCRIPTION),
+        datasets.BuilderConfig(name="swepar", version=VERSION, description=_SwePar_DESCRIPTION),
+        datasets.BuilderConfig(name="swesat", version=VERSION, description=_SweSat_DESCRIPTION),
+        datasets.BuilderConfig(name="swewic", version=VERSION, description=_SweWic_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 == 'absabank':
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value(dtype='float32')               
+                }
+            )
+        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 == "swefaq":
+            features = datasets.Features(
+                {
+                    "question": datasets.Value("string"),
+                    "candidate_answer": datasets.Value("string"),
+                    "correct_answer": 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 == "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"),
+                }
+            )
+        else:
+            raise ValueError(f"Subset {self.config.name} does not exist.")
+        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  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,
+        )