Datasets:

eduagarcia
/

portuguese_benchmark

@@ -822,200 +822,6 @@ for persons, locations, time entities, organizations, legislation and legal case
 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:
-**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** (NLI/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.

 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** (NLI/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.