README.md

---
language:
- pt
pretty_name: Portuguese Benchmark
dataset_info:
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- config_name: Portuguese_Hate_Speech_binary
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  - name: sentence
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  - name: ner_tags
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- config_name: assin2-rte
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- config_name: assin2-sts
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- config_name: brazilian_court_decisions_judgment
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    dtype: int32
  - name: sentence
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- config_name: brazilian_court_decisions_unanimity
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  - name: idx
    dtype: int32
  - name: sentence
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  - name: label
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  - name: ner_tags
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- config_name: mapa_pt_coarse
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          '9': B-PERSON
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          '11': B-TIME
          '12': I-TIME
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  - name: idx
    dtype: int32
  - name: tokens
    sequence: string
  - name: ner_tags
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          '3': B-BUILDING
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          '5': B-CITY
          '6': I-CITY
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          '36': I-TYPE
          '37': B-UNIT
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    num_examples: 390
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  dataset_size: 1431665
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- config_name: rrip
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configs:
- config_name: HateBR_offensive_binary
  data_files:
  - split: train
    path: HateBR_offensive_binary/train-*
  - split: validation
    path: HateBR_offensive_binary/validation-*
  - split: test
    path: HateBR_offensive_binary/test-*
- config_name: HateBR_offensive_level
  data_files:
  - split: train
    path: HateBR_offensive_level/train-*
  - split: validation
    path: HateBR_offensive_level/validation-*
  - split: test
    path: HateBR_offensive_level/test-*
- config_name: LeNER-Br
  data_files:
  - split: train
    path: LeNER-Br/train-*
  - split: validation
    path: LeNER-Br/validation-*
  - split: test
    path: LeNER-Br/test-*
- config_name: Portuguese_Hate_Speech_binary
  data_files:
  - split: train
    path: Portuguese_Hate_Speech_binary/train-*
  - split: validation
    path: Portuguese_Hate_Speech_binary/validation-*
  - split: test
    path: Portuguese_Hate_Speech_binary/test-*
- config_name: UlyssesNER-Br-C-coarse
  data_files:
  - split: train
    path: UlyssesNER-Br-C-coarse/train-*
  - split: validation
    path: UlyssesNER-Br-C-coarse/validation-*
  - split: test
    path: UlyssesNER-Br-C-coarse/test-*
- config_name: UlyssesNER-Br-C-fine
  data_files:
  - split: train
    path: UlyssesNER-Br-C-fine/train-*
  - split: validation
    path: UlyssesNER-Br-C-fine/validation-*
  - split: test
    path: UlyssesNER-Br-C-fine/test-*
- config_name: UlyssesNER-Br-PL-coarse
  data_files:
  - split: train
    path: UlyssesNER-Br-PL-coarse/train-*
  - split: validation
    path: UlyssesNER-Br-PL-coarse/validation-*
  - split: test
    path: UlyssesNER-Br-PL-coarse/test-*
- config_name: UlyssesNER-Br-PL-fine
  data_files:
  - split: train
    path: UlyssesNER-Br-PL-fine/train-*
  - split: validation
    path: UlyssesNER-Br-PL-fine/validation-*
  - split: test
    path: UlyssesNER-Br-PL-fine/test-*
- config_name: assin2-rte
  data_files:
  - split: train
    path: assin2-rte/train-*
  - split: validation
    path: assin2-rte/validation-*
  - split: test
    path: assin2-rte/test-*
- config_name: assin2-sts
  data_files:
  - split: train
    path: assin2-sts/train-*
  - split: validation
    path: assin2-sts/validation-*
  - split: test
    path: assin2-sts/test-*
- config_name: brazilian_court_decisions_judgment
  data_files:
  - split: train
    path: brazilian_court_decisions_judgment/train-*
  - split: validation
    path: brazilian_court_decisions_judgment/validation-*
  - split: test
    path: brazilian_court_decisions_judgment/test-*
- config_name: brazilian_court_decisions_unanimity
  data_files:
  - split: train
    path: brazilian_court_decisions_unanimity/train-*
  - split: validation
    path: brazilian_court_decisions_unanimity/validation-*
  - split: test
    path: brazilian_court_decisions_unanimity/test-*
- config_name: harem-default
  data_files:
  - split: train
    path: harem-default/train-*
  - split: validation
    path: harem-default/validation-*
  - split: test
    path: harem-default/test-*
- config_name: harem-selective
  data_files:
  - split: train
    path: harem-selective/train-*
  - split: validation
    path: harem-selective/validation-*
  - split: test
    path: harem-selective/test-*
- config_name: mapa_pt_coarse
  data_files:
  - split: train
    path: mapa_pt_coarse/train-*
  - split: validation
    path: mapa_pt_coarse/validation-*
  - split: test
    path: mapa_pt_coarse/test-*
- config_name: mapa_pt_fine
  data_files:
  - split: train
    path: mapa_pt_fine/train-*
  - split: validation
    path: mapa_pt_fine/validation-*
  - split: test
    path: mapa_pt_fine/test-*
- config_name: multi_eurlex_pt
  data_files:
  - split: train
    path: multi_eurlex_pt/train-*
  - split: validation
    path: multi_eurlex_pt/validation-*
  - split: test
    path: multi_eurlex_pt/test-*
- config_name: rrip
  data_files:
  - split: train
    path: rrip/train-*
  - split: validation
    path: rrip/validation-*
  - split: test
    path: rrip/test-*
---

# 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 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** 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.
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** (CLS) [\[Link\]](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 CLS) [\[Link\]](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** (CLS) [\[Link\]](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.

**rrip** (CLS) [\[Link\]](https://bit.ly/rhetoricalrole)

Rhetorical role identification (RRI) is an NLP task that consists of labeling the sentences of a document according 
to a given set of semantic functions (rhetorical roles). This task is useful to applications like document summarization, 
semantic search, document analysis, among others.  
In this corpus, we propose to segment petitions into eight rhetorical roles by mainly considering the analytic needs of 
judge's' offices in Brazil. We present a corpus of 70 petitions comprising more than 10 thousand sentences manually labeled 
with the proposed rhetorical roles. These petitions were taken from civil lawsuits filed in the court of the Brazilian state 
of Mato Grosso do Sul (TJMS).
       


## 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"
}

#rrip
@inproceedings{aragy_rhetorical_2021,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Rhetorical {Role} {Identification} for {Portuguese} {Legal} {Documents}},
    isbn = {978-3-030-91699-2},
    doi = {10.1007/978-3-030-91699-2_38},
    booktitle = {Intelligent {Systems}},
    publisher = {Springer International Publishing},
    author = {Aragy, Roberto and Fernandes, Eraldo Rezende and Caceres, Edson Norberto},
    editor = {Britto, André and Valdivia Delgado, Karina},
    year = {2021},
    pages = {557--571},
}
```