# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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 """ BasqueGLUE: A Natural Language Understanding Benchmark for Basque """ import json import os import textwrap import datasets from datasets import DownloadManager _CITATION = """\ @InProceedings{urbizu2022basqueglue, author = {Urbizu, Gorka and San Vicente, Iñaki and Saralegi, Xabier and Agerri, Rodrigo and Soroa, Aitor}, title = {BasqueGLUE: A Natural Language Understanding Benchmark for Basque}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {1603--1612}, abstract = {Natural Language Understanding (NLU) technology has improved significantly over the last few years and multitask benchmarks such as GLUE are key to evaluate this improvement in a robust and general way. These benchmarks take into account a wide and diverse set of NLU tasks that require some form of language understanding, beyond the detection of superficial, textual clues. However, they are costly to develop and language-dependent, and therefore they are only available for a small number of languages. In this paper, we present BasqueGLUE, the first NLU benchmark for Basque, a less-resourced language, which has been elaborated from previously existing datasets and following similar criteria to those used for the construction of GLUE and SuperGLUE. We also report the evaluation of two state-of-the-art language models for Basque on BasqueGLUE, thus providing a strong baseline to compare upon. BasqueGLUE is freely available under an open license.}, url = {https://aclanthology.org/2022.lrec-1.172} } """ _DESCRIPTION = """\ We present BasqueGLUE, the first NLU benchmark for Basque, which has been elaborated from previously existing datasets and following similar criteria to those used for the construction of GLUE and SuperGLUE. BasqueGLUE is freely available under an open license. """ _HOMEPAGE = "https://github.com/orai-nlp/BasqueGLUE" URL = "https://raw.githubusercontent.com/orai-nlp/BasqueGLUE/main/" CONFIGS = [ "bec", "bhtc", "coref", "intent", "nerc_id", "nerc_od", "qnli", "slot", "vaxx", "wic" ] SPLITS = { "train": datasets.Split.TRAIN, "test": datasets.Split.TEST, "val": datasets.Split.VALIDATION } _URLS = { config: {split: URL + f"{config}/{split}.jsonl" for split in SPLITS.keys()} for config in CONFIGS } _URLS["wic"]["train"] = URL + "wic/train.zip" class BasqueGLUEConfig(datasets.BuilderConfig): """BuilderConfig for BasqueGLUE""" def __init__(self, text_features, label_column, citation, label_classes, int_features=None, is_tokens=False, **kwargs ): """ BuilderConfig for BasqueGLUE :param text_features: `list[string]`, the list of text columns :param int_features: `list[string]`, the list of int columns (optional) :param label_column: `string`, label column :param citation: `string`, citation for the data set :param label_classes: `list[string]`, the list of classes :param is_tokens: `bool`, indicates config is a token classification task :param kwargs: keyword arguments forwarded to super """ super(BasqueGLUEConfig, self).__init__(**kwargs) self.text_features = text_features self.int_features = int_features self.label_column = label_column self.label_classes = label_classes self.citation = citation self.is_tokens = is_tokens self.label_map = {label: idx for idx, label in enumerate(label_classes)} class BasqueGLUE(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ BasqueGLUEConfig( name='bec', description=textwrap.dedent( """\ The Basque Election Campaign 2016 Opinion Dataset (BEC2016eu) is a new dataset for the task of sentiment analysis, a sequence classification task, which contains tweets about the campaign for the Basque elections from 2016. The crawling was carried out during the election campaign period (2016/09/09-2016/09/23), by monitoring the main parties and their respective candidates. The tweets were manually annotated as positive, negative or neutral. """ ), text_features=['text'], label_column="label", label_classes=["N", "NEU", "P"], citation=textwrap.dedent(_CITATION) ), BasqueGLUEConfig( name='bhtc', description=textwrap.dedent( """\ The corpus contains 12,296 news headlines (brief article descriptions) from the Basque weekly newspaper [Argia](https://www.argia.eus). Topics are classified uniquely according to twelve thematic categories. """ ), text_features=["text"], label_column="label", label_classes=["Ekonomia", "Euskal Herria", "Euskara", "Gizartea", "Historia", "Ingurumena", "Iritzia", "Komunikazioa", "Kultura", "Nazioartea", "Politika", "Zientzia"], citation=textwrap.dedent( """\ @inproceedings{agerri-etal-2020-give, title = "Give your Text Representation Models some Love: the Case for {B}asque", author = "Agerri, Rodrigo and San Vicente, I{\~n}aki and Campos, Jon Ander and Barrena, Ander and Saralegi, Xabier and Soroa, Aitor and Agirre, Eneko", booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.588", pages = "4781--4788", abstract = "Word embeddings and pre-trained language models allow to build rich representations of text and have enabled improvements across most NLP tasks. Unfortunately they are very expensive to train, and many small companies and research groups tend to use models that have been pre-trained and made available by third parties, rather than building their own. This is suboptimal as, for many languages, the models have been trained on smaller (or lower quality) corpora. In addition, monolingual pre-trained models for non-English languages are not always available. At best, models for those languages are included in multilingual versions, where each language shares the quota of substrings and parameters with the rest of the languages. This is particularly true for smaller languages such as Basque. In this paper we show that a number of monolingual models (FastText word embeddings, FLAIR and BERT language models) trained with larger Basque corpora produce much better results than publicly available versions in downstream NLP tasks, including topic classification, sentiment classification, PoS tagging and NER. This work sets a new state-of-the-art in those tasks for Basque. All benchmarks and models used in this work are publicly available.", language = "English", ISBN = "979-10-95546-34-4", } """ ) ), BasqueGLUEConfig( name='coref', description=textwrap.dedent( """\ EPEC-KORREF-Bin is a dataset derived from EPEC-KORREF (Soraluze et al. 2012), a corpus of Basque news documents with manually annotated mentions and coreference chains, which we have been converted into a binary classification task. In this task, the model has to predict whether two mentions from a text, which can be pronouns, nouns or noun phrases, are referring to the same entity. """ ), text_features=["text", 'span1_text', "span2_text"], label_column="label", label_classes=["false", 'true'], int_features=["span1_index", "span2_index"], citation=textwrap.dedent(_CITATION) ), BasqueGLUEConfig( name='intent', description=textwrap.dedent( """\ This dataset contains utterance texts and intent annotations drawn from the manually-annotated Facebook Multilingual Task Oriented Dataset (FMTOD) (Schuster et al. 2019). Basque translated data was drawn from the datasets created for Building a Task-oriented Dialog System for languages with no training data: the Case for Basque (de Lacalle et al. 2020). The examples are annotated with one of 12 different intent classes corresponding to alarm, reminder or weather related actions. """ ), text_features=["text"], label_column="label", label_classes=["alarm/cancel_alarm", "alarm/modify_alarm", "alarm/set_alarm", "alarm/show_alarms", "alarm/snooze_alarm", "alarm/time_left_on_alarm", "reminder/cancel_reminder", "reminder/set_reminder", "reminder/show_reminders", "weather/checkSunrise", "weather/checkSunset", "weather/find"], citation=textwrap.dedent( """\ @inproceedings{lopez-de-lacalle-etal-2020-building, title = "Building a Task-oriented Dialog System for Languages with no Training Data: the Case for {B}asque", author = "L{\'o}pez de Lacalle, Maddalen and Saralegi, Xabier and San Vicente, I{\~n}aki", booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.340", pages = "2796--2802", abstract = "This paper presents an approach for developing a task-oriented dialog system for less-resourced languages in scenarios where training data is not available. Both intent classification and slot filling are tackled. We project the existing annotations in rich-resource languages by means of Neural Machine Translation (NMT) and posterior word alignments. We then compare training on the projected monolingual data with direct model transfer alternatives. Intent Classifiers and slot filling sequence taggers are implemented using a BiLSTM architecture or by fine-tuning BERT transformer models. Models learnt exclusively from Basque projected data provide better accuracies for slot filling. Combining Basque projected train data with rich-resource languages data outperforms consistently models trained solely on projected data for intent classification. At any rate, we achieve competitive performance in both tasks, with accuracies of 81{\%} for intent classification and 77{\%} for slot filling.", language = "English", ISBN = "979-10-95546-34-4", } """ ) ), BasqueGLUEConfig( name='nerc_id', description=textwrap.dedent( """\ This dataset contains sentences from the news domain with manually annotated named entities. The data is the merge of EIEC (a dataset of a collection of news wire articles from Euskaldunon Egunkaria newspaper, (Alegria et al. 2004)), and newly annotated data from naiz.eus. The data is annotated following the BIO annotation scheme over four categories: person, organization, location, and miscellaneous. """ ), is_tokens=True, text_features=["tokens"], label_column="tags", label_classes=["O", "B-PER", "I-PER", "B-LOC", "I-LOC", "B-ORG", "I-ORG", "B-MISC", "I-MISC"], citation=textwrap.dedent(_CITATION) ), BasqueGLUEConfig( name='nerc_od', description=textwrap.dedent( """\ This dataset contains sentences with manually annotated named entities. The training data is the merge of EIEC (a dataset of a collection of news wire articles from Euskaldunon Egunkaria newspaper, (Alegria et al. 2004)), and newly annotated data from naiz.eus. The data is annotated following the BIO annotation scheme over four categories: person, organization, location, and miscellaneous. For validation and test sets, sentences from Wikipedia were annotated following the same annotation guidelines. """ ), is_tokens=True, text_features=["tokens"], label_column="tags", label_classes=["O", "B-PER", "I-PER", "B-LOC", "I-LOC", "B-ORG", "I-ORG", "B-MISC", "I-MISC"], citation=textwrap.dedent(_CITATION) ), BasqueGLUEConfig( name='qnli', description=textwrap.dedent( """\ This task includes the QA dataset ElkarHizketak (Otegi et al. 2020), a low resource conversational Question Answering (QA) dataset for Basque created by native speaker volunteers. The dataset is built on top of Wikipedia sections about popular people and organizations, and it contains around 400 dialogues and 1600 question and answer pairs. The task was adapted into a sentence-pair binary classification task, following the design of QNLI for English (Wang et al. 2019). Each question and answer pair are given a label indicating whether the answer is entailed by the question. """ ), text_features=["question", "sentence"], label_column="label", label_classes=["entailment", "not_entailment"], citation=textwrap.dedent(_CITATION) ), BasqueGLUEConfig( name='slot', description=textwrap.dedent( """\ This dataset contains utterance texts and sequence intent argument annotations designed for slot filling tasks, drawn from the manually-annotated Facebook Multilingual Task Oriented Dataset (FMTOD) (Schuster et al. 2019). Basque translated data was drawn from the datasets created for Building a Task-oriented Dialog System for languages with no training data: the Case for Basque (de Lacalle et al. 2020). The task is a sequence labelling task similar to NERC, following BIO annotation scheme over 11 categories. """ ), is_tokens=True, text_features=["tokens"], label_column="tags", label_classes=["O", "B-datetime", "B-location", "B-negation", "B-alarm/alarm_modifier", "B-alarm/recurring_period", "B-reminder/noun", "B-reminder/todo", "B-reminder/reference", "B-reminder/recurring_period", "B-weather/attribute", "B-weather/noun", "I-datetime", "I-location", "I-negation", "I-alarm/alarm_modifier", "I-alarm/recurring_period", "I-reminder/noun", "I-reminder/todo", "I-reminder/reference", "I-reminder/recurring_period", "I-weather/attribute", "I-weather/noun"], citation=textwrap.dedent( """\ @inproceedings{lopez-de-lacalle-etal-2020-building, title = "Building a Task-oriented Dialog System for Languages with no Training Data: the Case for {B}asque", author = "L{\'o}pez de Lacalle, Maddalen and Saralegi, Xabier and San Vicente, I{\~n}aki", booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.340", pages = "2796--2802", abstract = "This paper presents an approach for developing a task-oriented dialog system for less-resourced languages in scenarios where training data is not available. Both intent classification and slot filling are tackled. We project the existing annotations in rich-resource languages by means of Neural Machine Translation (NMT) and posterior word alignments. We then compare training on the projected monolingual data with direct model transfer alternatives. Intent Classifiers and slot filling sequence taggers are implemented using a BiLSTM architecture or by fine-tuning BERT transformer models. Models learnt exclusively from Basque projected data provide better accuracies for slot filling. Combining Basque projected train data with rich-resource languages data outperforms consistently models trained solely on projected data for intent classification. At any rate, we achieve competitive performance in both tasks, with accuracies of 81{\%} for intent classification and 77{\%} for slot filling.", language = "English", ISBN = "979-10-95546-34-4", } """ ) ), BasqueGLUEConfig( name='vaxx', description=textwrap.dedent( """\ The VaxxStance (Agerri et al., 2021) dataset originally provides texts and stance annotations for social media texts around the anti-vaccine movement. Texts are given a label indicating whether they express an AGAINST, FAVOR or NEUTRAL stance towards the topic. """ ), text_features=['text'], label_column="label", label_classes=['AGAINST', 'NONE', 'FAVOR'], citation=textwrap.dedent( """\ @article{agerriVaxxStanceIberLEF20212021, title = {{VaxxStance@IberLEF 2021: Overview of the Task on Going Beyond Text in Cross-Lingual Stance Detection}}, shorttitle = {{VaxxStance@IberLEF 2021}}, author = {Agerri, Rodrigo and Centeno, Roberto and Espinosa, Mar{\'i}a and de Landa, Joseba Fern{\'a}ndez and Rodrigo, {\'A}lvaro}, year = {2021}, month = sep, journal = {Procesamiento del Lenguaje Natural}, volume = {67}, number = {0}, pages = {173--181}, issn = {1989-7553}, abstract = {This paper describes the VaxxStance task at IberLEF 2021. The task proposes to detect stance in Tweets referring to vaccines, a relevant and controversial topic in the current pandemia. The task is proposed in a multilingual setting, providing data for Basque and Spanish languages. The objective is to explore crosslingual approaches which also complement textual information with contextual features obtained from the social network. The results demonstrate that contextual information is crucial to obtain competitive results, especially across languages.}, copyright = {Copyright (c) 2021 Procesamiento del Lenguaje Natural}, langid = {spanish}, } """ ) ), BasqueGLUEConfig( name='wic', description=textwrap.dedent( """\ Word in Context or WiC (Pilehvar and Camacho-Collados 2019) is a word sense disambiguation (WSD) task, designed as a particular form of sentence pair binary classification. Given two text snippets and a polyse mous word that appears in both of them (the span of the word is marked in both snippets), the task is to determine whether the word has the same sense in both sentences. This dataset is based on the EPEC-EuSemcor (Pociello et al. 2011) sense-tagged corpus. """ ), text_features=['sentence1', 'sentence2', 'word'], int_features=['start1', 'start2', 'end1', 'end2'], label_column="label", label_classes=['false', 'true'], citation=textwrap.dedent( """\ @article{agerriVaxxStanceIberLEF20212021, title = {{VaxxStance@IberLEF 2021: Overview of the Task on Going Beyond Text in Cross-Lingual Stance Detection}}, shorttitle = {{VaxxStance@IberLEF 2021}}, author = {Agerri, Rodrigo and Centeno, Roberto and Espinosa, Mar{\'i}a and de Landa, Joseba Fern{\'a}ndez and Rodrigo, {\'A}lvaro}, year = {2021}, month = sep, journal = {Procesamiento del Lenguaje Natural}, volume = {67}, number = {0}, pages = {173--181}, issn = {1989-7553}, abstract = {This paper describes the VaxxStance task at IberLEF 2021. The task proposes to detect stance in Tweets referring to vaccines, a relevant and controversial topic in the current pandemia. The task is proposed in a multilingual setting, providing data for Basque and Spanish languages. The objective is to explore crosslingual approaches which also complement textual information with contextual features obtained from the social network. The results demonstrate that contextual information is crucial to obtain competitive results, especially across languages.}, copyright = {Copyright (c) 2021 Procesamiento del Lenguaje Natural}, langid = {spanish}, } """ ) ), ] def _info(self): if self.config.is_tokens: features = { text_feature: datasets.Sequence(datasets.Value("string")) for text_feature in self.config.text_features } features[self.config.label_column] = datasets.Sequence( datasets.features.ClassLabel(names=self.config.label_classes) ) else: features = { text_feature: datasets.Value("string") for text_feature in self.config.text_features } features["label"] = datasets.features.ClassLabel(names=self.config.label_classes) if self.config.int_features: for int_feature in self.config.int_features: features[int_feature] = datasets.Value("int32") features["idx"] = datasets.Value("int32") return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features(features), citation=self.config.citation, ) def _split_generators(self, dl_manager: DownloadManager): """ Return SplitGenerators. """ data_urls = _URLS[self.config.name] splits = [] for split, sp_type in SPLITS.items(): data_url = data_urls[split] if 'jsonl' in data_url: data_file = dl_manager.download(data_url) else: data_dir = dl_manager.download_and_extract(data_url) json_file = [f for f in os.listdir(data_dir) if f.endswith('jsonl')][0] data_file = os.path.join(data_dir, json_file) splits.append( datasets.SplitGenerator( name=sp_type, gen_kwargs={ "data_file": data_file } ) ) return splits def _generate_examples(self, data_file): """ Yield examples. """ with open(data_file, encoding="utf8", mode="r") as f: id_ = 0 for line in f: data = json.loads(line) if self.config.name == 'coref': example = { 'text': data['text'], 'span1_text': data['target']['span1_text'], 'span2_text': data['target']['span2_text'], 'span1_index': int(data['target']['span1_index']), 'span2_index': int(data['target']['span2_index']) } else: example = { feat: data[feat] for feat in self.config.text_features } if self.config.int_features: for feat in self.config.int_features: example[feat] = int(data[feat]) example['idx'] = data['idx'] label_data = data[self.config.label_column] if type(label_data) == bool: label_data = str(label_data).lower() if self.config.is_tokens: label = [self.config.label_map[tag] for tag in label_data] else: label = self.config.label_map[label_data] example[self.config.label_column] = label # Filter out corrupted rows. for value in example.values(): if value is None: break else: yield id_, example id_ += 1