Update README.md

README.md

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-# Portuguese Benchmark
+# Portuguese Benchmark
+
+This a collection of datasets in Portuguese initially meant to train and evaluate supervised language models such as BERT, RoBERTa, etc...
+It contains 9 datasets and 17 Tasks for Classification, NLI, Semantic Similarity Scoring and Named-Entity Recognition.
+
+
+## Tasks:
+
+**LeNER-Br** (NER) [\[Link\]](https://teodecampos.github.io/LeNER-Br/)
+
+LeNER-Br is a Portuguese language dataset for named entity recognition applied to legal documents. 
+LeNER-Br consists entirely of manually annotated legislation and legal cases texts and contains tags 
+for persons, locations, time entities, organizations, legislation and legal cases. To compose the dataset, 
+66 legal documents from several Brazilian Courts were collected. Courts of superior and state levels were considered, 
+such as Supremo Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas Gerais and Tribunal de Contas da União. 
+In addition, four legislation documents were collected, such as "Lei Maria da Penha", giving a total of 70 documents.
+
+**assin2-rte** and **assin2-sts** (RTE/STS) [\[Link\]](https://sites.google.com/view/assin2)
+
+The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1.
+The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese,
+annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment
+classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same
+annotation. All data were manually annotated.
+
+
+**HateBR_offensive_binary** and **HateBR_offensive_level** (CLS) [\[Link\]](https://github.com/franciellevargas/HateBR)
+
+HateBR is the first large-scale expert annotated dataset of Brazilian Instagram comments for abusive language detection 
+on the web and social media. The HateBR was collected from Brazilian Instagram comments of politicians and manually annotated 
+by specialists. It is composed of 7,000 documents annotated according to three different layers: a binary classification (offensive 
+versus non-offensive comments), offensiveness-level (highly, moderately, and slightly offensive messages), and nine hate speech 
+groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, 
+and fatphobia). Each comment was annotated by three different annotators and achieved high inter-annotator agreement. Furthermore, 
+baseline experiments were implemented reaching 85% of F1-score outperforming the current literature dataset baselines for 
+the Portuguese language. We hope that the proposed expert annotated dataset may foster research on hate speech detection in the 
+Natural Language Processing area.
+
+**UlyssesNER-Br-\*** (NER) [\[Link\]](https://github.com/ulysses-camara/ulysses-ner-br)
+
+UlyssesNER-Br is a corpus of Brazilian Legislative Documents for NER with quality baselines. 
+The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies.
+UlyssesNER-Br has seven semantic classes or categories. Based on HAREM,
+we defined five typical categories: person, location, organization, event and date.
+In addition, we defined two specific semantic classes for the legislative domain:
+law foundation and law product. The law foundation category makes reference to
+entities related to laws, resolutions, decrees, as well as to domain-specific entities
+such as bills, which are law proposals being discussed by the parliament, and legislative consultations, 
+also known as job requests made by the parliamentarians.
+The law product entity refers to systems, programs, and other products created
+from legislation.
+
+**brazilian_court_decisions_judgment** and **brazilian_court_decisions_unanimity** (CLASSIFICATION) [\[Link\]](https://github.com/lagefreitas/predicting-brazilian-court-decisions)
+
+The dataset is a collection of 4043 Ementa (summary) court decisions and their metadata from the Tribunal de 
+Justiça de Alagoas (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled according 
+to 7 categories and whether the decisions were unanimous on the part of the judges or not. The dataset 
+supports the task of Legal Judgment Prediction.
+
+**harem-default** and **harem-selective** (NER) [\[Link\]](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html)
+
+The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts,
+from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM
+documents are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set,
+a version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event,
+Abstraction, and Other) and a "selective" version with only 5 classes (Person, Organization, Location, Value, and Date).
+It's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely "Category" and "Sub-type".
+The dataset version processed here ONLY USE the "Category" level of the original dataset.
+[1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese."
+Brazilian Conference on Intelligent Systems. Springer, Cham, 2020.
+
+**multi_eurlex_pt** (MULTILABEL CLASSIFICATION) [\[Link\]](https://github.com/nlpaueb/MultiEURLEX/)
+
+MultiEURLEX comprises 65k EU laws in 23 official EU languages. 
+Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. 
+Each EUROVOC label ID is associated with a label descriptor, e.g., [60, agri-foodstuffs], 
+[6006, plant product], [1115, fruit]. The descriptors are also available in the 23 languages. 
+Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EUR-LEX, 
+comprising 57k EU laws with the originally assigned gold labels.
+
+**mapa_pt_coarse** and **mapa_pt_fine** (NER) [\[Link\]](https://huggingface.co/datasets/joelniklaus/mapa)
+
+The dataset consists of 12 documents (9 for Spanish due to parsing errors) taken from EUR-Lex, 
+a multilingual corpus of court decisions and legal dispositions in the 24 official languages 
+of the European Union. The documents have been annotated for named entities following the 
+guidelines of the MAPA project which foresees two annotation level, a general and a more 
+fine-grained one. The annotated corpus can be used for named entity recognition/classification.
+
+**Portuguese_Hate_Speech_binary** (CLASSIFICATION) [\[Link\]](https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset)
+
+The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by
+annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary
+labels (‘hate’ vs. ‘no-hate’). Secondly, expert annotators classified the tweets following a fine-grained
+hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement
+varied from category to category, which reflects the insight that some types of hate speech are more subtle
+than others and that their detection depends on personal perception. This hierarchical annotation scheme is
+the main contribution of the presented work, as it facilitates the identification of different types of
+hate speech and their intersections.
+
+
+## Citations
+
+Citation for each one of the Tasks:
+
+```bibtext
+# LeNER-Br
+@InProceedings{luz_etal_propor2018,
+ author = {Pedro H. {Luz de Araujo} and Te'{o}filo E. {de Campos} and
+ Renato R. R. {de Oliveira} and Matheus Stauffer and
+ Samuel Couto and Paulo Bermejo},
+ title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
+ booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},
+ publisher = {Springer},
+ series = {Lecture Notes on Computer Science ({LNCS})},
+ pages = {313--323},
+ year = {2018},
+ month = {September 24-26},
+ address = {Canela, RS, Brazil}, 
+ doi = {10.1007/978-3-319-99722-3_32},
+ url = {https://teodecampos.github.io/LeNER-Br/},
+}
+
+# assin2-rte, assin2-sts
+@inproceedings{real2020assin,
+ title={The assin 2 shared task: a quick overview},
+ author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
+ booktitle={International Conference on Computational Processing of the Portuguese Language},
+ pages={406--412},
+ year={2020},
+ organization={Springer}
+}
+
+# HateBR_offensive_binary, HateBR_offensive_level
+@inproceedings{vargas2022hatebr,
+ title={HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection},
+ author={Vargas, Francielle and Carvalho, Isabelle and de G{'o}es, Fabiana Rodrigues and Pardo, Thiago and Benevenuto, Fabr{'\i}cio},
+ booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},
+ pages={7174--7183},
+ year={2022}
+}
+
+# UlyssesNER-Br-PL-coarse, UlyssesNER-Br-C-coarse, UlyssesNER-Br-PL-fine, UlyssesNER-Br-C-fine
+@InProceedings{10.1007/978-3-030-98305-5_1,
+ author="Albuquerque, Hidelberg O.
+ and Costa, Rosimeire
+ and Silvestre, Gabriel
+ and Souza, Ellen
+ and da Silva, N{'a}dia F. F.
+ and Vit{'o}rio, Douglas
+ and Moriyama, Gyovana
+ and Martins, Lucas
+ and Soezima, Luiza
+ and Nunes, Augusto
+ and Siqueira, Felipe
+ and Tarrega, Jo{\~a}o P.
+ and Beinotti, Joao V.
+ and Dias, Marcio
+ and Silva, Matheus
+ and Gardini, Miguel
+ and Silva, Vinicius
+ and de Carvalho, Andr{'e} C. P. L. F.
+ and Oliveira, Adriano L. I.",
+ editor="Pinheiro, Vl{'a}dia
+ and Gamallo, Pablo
+ and Amaro, Raquel
+ and Scarton, Carolina
+ and Batista, Fernando
+ and Silva, Diego
+ and Magro, Catarina
+ and Pinto, Hugo",
+ title="UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition",
+ booktitle="Computational Processing of the Portuguese Language",
+ year="2022",
+ publisher="Springer International Publishing",
+ address="Cham",
+ pages="3--14",
+ isbn="978-3-030-98305-5"
+}
+@InProceedings{10.1007/978-3-031-16474-3_62,
+ author="Costa, Rosimeire
+ and Albuquerque, Hidelberg Oliveira
+ and Silvestre, Gabriel
+ and Silva, N{'a}dia F{'e}lix F.
+ and Souza, Ellen
+ and Vit{'o}rio, Douglas
+ and Nunes, Augusto
+ and Siqueira, Felipe
+ and Pedro Tarrega, Jo{\~a}o
+ and Vitor Beinotti, Jo{\~a}o
+ and de Souza Dias, M{'a}rcio
+ and Pereira, Fab{'i}ola S. F.
+ and Silva, Matheus
+ and Gardini, Miguel
+ and Silva, Vinicius
+ and de Carvalho, Andr{'e} C. P. L. F.
+ and Oliveira, Adriano L. I.",
+ editor="Marreiros, Goreti
+ and Martins, Bruno
+ and Paiva, Ana
+ and Ribeiro, Bernardete
+ and Sardinha, Alberto",
+ title="Expanding UlyssesNER-Br Named Entity Recognition Corpus with Informal User-Generated Text",
+ booktitle="Progress in Artificial Intelligence",
+ year="2022",
+ publisher="Springer International Publishing",
+ address="Cham",
+ pages="767--779",
+ isbn="978-3-031-16474-3"
+}
+
+# brazilian_court_decisions_judgment, brazilian_court_decisions_unanimity
+@article{Lage-Freitas2022,
+ author = {Lage-Freitas, Andr{'{e}} and Allende-Cid, H{'{e}}ctor and Santana, Orivaldo and Oliveira-Lage, L{'{i}}via},
+ doi = {10.7717/peerj-cs.904},
+ issn = {2376-5992},
+ journal = {PeerJ. Computer science},
+ keywords = {Artificial intelligence,Jurimetrics,Law,Legal,Legal NLP,Legal informatics,Legal outcome forecast,Litigation prediction,Machine learning,NLP,Portuguese,Predictive algorithms,judgement prediction},
+ language = {eng},
+ month = {mar},
+ pages = {e904--e904},
+ publisher = {PeerJ Inc.},
+ title = {{Predicting Brazilian Court Decisions}},
+ url = {https://pubmed.ncbi.nlm.nih.gov/35494851 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044329/},
+ volume = {8},
+ year = {2022}
+}
+
+# harem-default, harem-selective
+@inproceedings{santos2006harem,
+ title={Harem: An advanced ner evaluation contest for portuguese},
+ author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},
+ booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceedings of the 5 th International Conference on Language Resources and Evaluation (LREC'2006)(Genoa Italy 22-28 May 2006)},
+ year={2006}
+}
+
+# multi_eurlex_pt
+@InProceedings{chalkidis-etal-2021-multieurlex,
+ author = {Chalkidis, Ilias 
+ and Fergadiotis, Manos
+ and Androutsopoulos, Ion},
+ title = {MultiEURLEX -- A multi-lingual and multi-label legal document 
+ classification dataset for zero-shot cross-lingual transfer},
+ booktitle = {Proceedings of the 2021 Conference on Empirical Methods
+ in Natural Language Processing},
+ year = {2021},
+ publisher = {Association for Computational Linguistics},
+ location = {Punta Cana, Dominican Republic},
+ url = {https://arxiv.org/abs/2109.00904}
+}
+
+# mapa_pt_coarse, mapa_pt_fine
+@article{DeGibertBonet2022,
+ author = {{de Gibert Bonet}, Ona and {Garc{'{i}}a Pablos}, Aitor and Cuadros, Montse and Melero, Maite},
+ journal = {Proceedings of the Language Resources and Evaluation Conference},
+ number = {June},
+ pages = {3751--3760},
+ title = {{Spanish Datasets for Sensitive Entity Detection in the Legal Domain}},
+ url = {https://aclanthology.org/2022.lrec-1.400},
+ year = {2022}
+}
+
+# Portuguese_Hate_Speech_binary
+@inproceedings{fortuna-etal-2019-hierarchically,
+ title = "A Hierarchically-Labeled {P}ortuguese Hate Speech Dataset",
+ author = "Fortuna, Paula and
+ Rocha da Silva, Jo{\~a}o and
+ Soler-Company, Juan and
+ Wanner, Leo and
+ Nunes, S{'e}rgio",
+ editor = "Roberts, Sarah T. and
+ Tetreault, Joel and
+ Prabhakaran, Vinodkumar and
+ Waseem, Zeerak",
+ booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
+ month = aug,
+ year = "2019",
+ address = "Florence, Italy",
+ publisher = "Association for Computational Linguistics",
+ url = "https://aclanthology.org/W19-3510",
+ doi = "10.18653/v1/W19-3510",
+ pages = "94--104"
+}
+```