Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Languages:
Portuguese
Size:
1K - 10K
ArXiv:
License:
| annotations_creators: | |
| - found | |
| language_creators: | |
| - found | |
| languages: | |
| - pt | |
| licenses: | |
| - 'other' | |
| multilinguality: | |
| - monolingual | |
| pretty_name: predicting-brazilian-court-decisions | |
| size_categories: | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - multi-class-classification | |
| # Dataset Card for predicting-brazilian-court-decisions | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** | |
| - **Repository:** https://github.com/lagefreitas/predicting-brazilian-court-decisions | |
| - **Paper:** Lage-Freitas, A., Allende-Cid, H., Santana, O., & Oliveira-Lage, L. (2022). Predicting Brazilian Court | |
| Decisions. PeerJ. Computer Science, 8, e904–e904. https://doi.org/10.7717/peerj-cs.904 | |
| - **Leaderboard:** | |
| - **Point of Contact:** [Joel Niklaus]([email protected]) | |
| ### Dataset Summary | |
| 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. | |
| ### Supported Tasks and Leaderboards | |
| Legal Judgment Prediction | |
| ### Languages | |
| Brazilian Portuguese | |
| ## Dataset Structure | |
| ### Data Instances | |
| The file format is jsonl and three data splits are present (train, validation and test) for each configuration. | |
| ### Data Fields | |
| The dataset contains the following fields: | |
| - `process_number`: A number assigned to the decision by the court | |
| - `orgao_julgador`: Judging Body: one of '1ª Câmara Cível', '2ª Câmara Cível', '3ª Câmara Cível', 'Câmara Criminal', ' | |
| Tribunal Pleno', 'Seção Especializada Cível' | |
| - `publish_date`: The date, when the decision has been published (14/12/2018 - 03/04/2019). At that time (in 2018-2019), | |
| the scraping script was limited and not configurable to get data based on date range. Therefore, only the data from | |
| the last months has been scraped. | |
| - `judge_relator`: Judicial panel | |
| - `ementa_text`: Summary of the court decision | |
| - `decision_description`: **Suggested input**. Corresponds to ementa_text - judgment_text - unanimity_text. Basic | |
| statistics (number of words): mean: 119, median: 88, min: 12, max: 1400 | |
| - `judgment_text`: The text used for determining the judgment label | |
| - `judgment_label`: **Primary suggested label**. Labels that can be used to train a model for judgment prediction: | |
| - `no`: The appeal was denied | |
| - `partial`: For partially favourable decisions | |
| - `yes`: For fully favourable decisions | |
| - removed labels (present in the original dataset): | |
| - `conflito-competencia`: Meta-decision. For example, a decision just to tell that Court A should rule this case | |
| and not Court B. | |
| - `not-cognized`: The appeal was not accepted to be judged by the court | |
| - `prejudicada`: The case could not be judged for any impediment such as the appealer died or gave up on the | |
| case for instance. | |
| - `unanimity_text`: Portuguese text to describe whether the decision was unanimous or not. | |
| - `unanimity_label`: **Secondary suggested label**. Unified labels to describe whether the decision was unanimous or | |
| not (in some cases contains ```not_determined```); they can be used for model training as well (Lage-Freitas et al., | |
| 2019). | |
| ### Data Splits | |
| The data has been split randomly into 80% train (3234), 10% validation (404), 10% test (405). | |
| There are two tasks possible for this dataset. | |
| #### Judgment | |
| Label Distribution | |
| | judgment | train | validation | test | | |
| |:----------|---------:|-----------:|--------:| | |
| | no | 1960 | 221 | 234 | | |
| | partial | 677 | 96 | 93 | | |
| | yes | 597 | 87 | 78 | | |
| | **total** | **3234** | **404** | **405** | | |
| #### Unanimity | |
| In this configuration, all cases that have `not_determined` as `unanimity_label` can be removed. | |
| Label Distribution | |
| | unanimity_label | train | validation | test | | |
| |:-----------------|----------:|---------------:|---------:| | |
| | not_determined | 1519 | 193 | 201 | | |
| | unanimity | 1681 | 205 | 200 | | |
| | not-unanimity | 34 | 6 | 4 | | |
| | **total** | **3234** | **404** | **405** | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| This dataset was created to further the research on developing models for predicting Brazilian court decisions that are | |
| also able to predict whether the decision will be unanimous. | |
| ### Source Data | |
| The data was scraped from *Tribunal de Justiça de Alagoas* (TJAL, the State Supreme Court of Alagoas (Brazil). | |
| #### Initial Data Collection and Normalization | |
| *“We developed a Web scraper for collecting data from Brazilian courts. The scraper first searched for the URL that | |
| contains the list of court cases […]. Then, the scraper extracted from these HTML files the specific case URLs and | |
| downloaded their data […]. Next, it extracted the metadata and the contents of legal cases and stored them in a CSV file | |
| format […].”* (Lage-Freitas et al., 2022) | |
| #### Who are the source language producers? | |
| The source language producer are presumably attorneys, judges, and other legal professionals. | |
| ### Annotations | |
| #### Annotation process | |
| The dataset was not annotated. | |
| #### Who are the annotators? | |
| [More Information Needed] | |
| ### Personal and Sensitive Information | |
| The court decisions might contain sensitive information about individuals. | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton | |
| Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset | |
| consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the | |
| dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, | |
| differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to | |
| have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the | |
| original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to | |
| the bibliographical references and the original Github repositories and/or web pages provided in this dataset card. | |
| ## Additional Information | |
| Lage-Freitas, A., Allende-Cid, H., Santana Jr, O., & Oliveira-Lage, L. (2019). Predicting Brazilian court decisions: | |
| - "In Brazil [...] lower court judges decisions might be appealed to Brazilian courts (*Tribiunais de Justiça*) to be | |
| reviewed by second instance court judges. In an appellate court, judges decide together upon a case and their | |
| decisions are compiled in Agreement reports named *Acóordãos*." | |
| ### Dataset Curators | |
| The names of the original dataset curators and creators can be found in references given below, in the section *Citation | |
| Information*. Additional changes were made by Joel Niklaus ([Email]([email protected]) | |
| ; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email]([email protected]) | |
| ; [Github](https://github.com/kapllan)). | |
| ### Licensing Information | |
| No licensing information was provided for this dataset. However, please make sure that you use the dataset according to | |
| Brazilian law. | |
| ### Citation Information | |
| ``` | |
| @misc{https://doi.org/10.48550/arxiv.1905.10348, | |
| author = {Lage-Freitas, Andr{\'{e}} and Allende-Cid, H{\'{e}}ctor and Santana, Orivaldo and de Oliveira-Lage, L{\'{i}}via}, | |
| doi = {10.48550/ARXIV.1905.10348}, | |
| keywords = {Computation and Language (cs.CL),FOS: Computer and information sciences,Social and Information Networks (cs.SI)}, | |
| publisher = {arXiv}, | |
| title = {{Predicting Brazilian court decisions}}, | |
| url = {https://arxiv.org/abs/1905.10348}, | |
| year = {2019} | |
| } | |
| ``` | |
| ``` | |
| @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} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@kapllan](https://github.com/kapllan) and [@joelniklaus](https://github.com/joelniklaus) for adding this | |
| dataset. | |