Update README.md

README.md

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 # 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 (CLS), NLI, Semantic Similarity Scoring (STS) and Named-Entity Recognition (NER).
+It contains 10 datasets and 18 Tasks for Classification (CLS), NLI, Semantic Similarity Scoring (STS) and Named-Entity Recognition (NER).
 
+| NER | Classification | NLI | STS |
+|---|---|---|---|
+| [LeNER-Br](https://teodecampos.github.io/LeNER-Br/) | [HateBR_offensive_binary](https://github.com/franciellevargas/HateBR) | [assin2-rte](https://sites.google.com/view/assin2) | [assin2-sts](https://sites.google.com/view/assin2) |
+| [UlyssesNER-Br-PL-coarse](https://github.com/ulysses-camara/ulysses-ner-br) | [HateBR_offensive_level](https://github.com/franciellevargas/HateBR) |  |  |
+| [UlyssesNER-Br-C-coarse](https://github.com/ulysses-camara/ulysses-ner-br) | [brazilian_court_decisions_judgment](https://github.com/lagefreitas/predicting-brazilian-court-decisions) |  |  |
+| [UlyssesNER-Br-PL-fine](https://github.com/ulysses-camara/ulysses-ner-br) | [brazilian_court_decisions_unanimity](https://github.com/lagefreitas/predicting-brazilian-court-decisions) |  |  |
+| [UlyssesNER-Br-C-fine](https://github.com/ulysses-camara/ulysses-ner-br) | [multi_eurlex_pt](https://github.com/nlpaueb/MultiEURLEX/) |  |  |
+| [harem-default](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html) | [Portuguese_Hate_Speech_binary](https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset) |  |  |
+| [harem-selective](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html) |  |  |  |
+| [mapa_pt_coarse](https://huggingface.co/datasets/joelniklaus/mapa) |  |  |  |
+| [mapa_pt_fine](https://huggingface.co/datasets/joelniklaus/mapa) |  |  |  |
+| [rrip](https://bit.ly/rhetoricalrole) |  |  |  |
+
+## Datasets Descriptions
+
+**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** (RTE) [\[Link\]](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.
+
+**assin2-sts** (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** (CLASSIFICATION) [\[Link\]](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.
+
+**HateBR_offensive_level** (CLASSIFICATION) [\[Link\]](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-PL-coarse** (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.
+
+**UlyssesNER-Br-C-coarse** (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.
+
+**UlyssesNER-Br-PL-fine** (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.
+
+**UlyssesNER-Br-C-fine** (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** (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.
+
+**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** (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.
+
+**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** (NER) [\[Link\]]()
+
+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.
+
+**mapa_pt_fine** (NER) [\[Link\]]()
+
+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.
+
+
+| NER | Classification | NLI | STS |
+|---|---|---|---|
+| [LeNER-Br](https://teodecampos.github.io/LeNER-Br/) | [HateBR_offensive_binary](https://github.com/franciellevargas/HateBR) | [assin2-rte](https://sites.google.com/view/assin2) | [assin2-sts](https://sites.google.com/view/assin2) |
+| [UlyssesNER-Br-PL-coarse](https://github.com/ulysses-camara/ulysses-ner-br) | [HateBR_offensive_level](https://github.com/franciellevargas/HateBR) |  |  |
+| [UlyssesNER-Br-C-coarse](https://github.com/ulysses-camara/ulysses-ner-br) | [brazilian_court_decisions_judgment](https://github.com/lagefreitas/predicting-brazilian-court-decisions) |  |  |
+| [UlyssesNER-Br-PL-fine](https://github.com/ulysses-camara/ulysses-ner-br) | [brazilian_court_decisions_unanimity](https://github.com/lagefreitas/predicting-brazilian-court-decisions) |  |  |
+| [UlyssesNER-Br-C-fine](https://github.com/ulysses-camara/ulysses-ner-br) | [multi_eurlex_pt](https://github.com/nlpaueb/MultiEURLEX/) |  |  |
+| [harem-default](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html) | [Portuguese_Hate_Speech_binary](https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset) |  |  |
+| [harem-selective](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html) |  |  |  |
+| mapa_pt_coarse |  |  |  |
+| mapa_pt_fine |  |  |  |
 
 ## Tasks: