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MartimZanatti/Descriptors_STJ

Work developed as part of [IRIS] (https://www.inesc-id.pt/projects/PR07005/) Extreme Multi-Label Classification of Descriptors The goal of this dataset is to train an Extreme Multi-Label classifier that, given a judgment from the Supreme Court of Justice of Portugal (STJ), can associate relevant descriptors to the judgment. Dataset Contents: Judgment ID: Unique identifier for each judgment. STJ Section: The section of the STJ to which the judgment belongs. Judgment Text: Full… See the full description on the dataset page: https://huggingface.co/datasets/MartimZanatti/Descriptors_STJ.

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Work developed as part of [IRIS] (https://www.inesc-id.pt/projects/PR07005/)

Extreme Multi-Label Classification of Descriptors

The goal of this dataset is to train an Extreme Multi-Label classifier that, given a judgment from the Supreme Court of Justice of Portugal (STJ), can associate relevant descriptors to the judgment.

Dataset Contents:

  • —Judgment ID: Unique identifier for each judgment.
  • —STJ Section: The section of the STJ to which the judgment belongs.
  • —Judgment Text: Full text of the judgment.
  • —Descriptors List: A list of binary values (0's and 1's) where 1's indicate the presence of active descriptors.

The dataset is organized by the sections of the STJ, and each section is further divided into training and testing subsets.

Additional Files:

  • —label.py: A Python file containing lists of descriptor names for each section. The order of these lists corresponds to the order of 0's and 1's in the dataset.

In each section, except for the contentious section, there is an additional file containing judgments excluded from the dataset. In these files, the judgments after the removal of descriptors appearing only once or twice were left with no descriptors.

Code: https://github.com/MartimZanatti/ExtrememultilabelPortugueseSupreme_Judgments

Contributions

Paper: Sleec Assignment of Descriptors to Judgments of the Supreme Court of Justice of Portugal

link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5017021