added jsonl files

.gitattributes

@@ -35,3 +35,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+judgment/test.jsonl filter=lfs diff=lfs merge=lfs -text
+judgment/train.jsonl filter=lfs diff=lfs merge=lfs -text
+judgment/validation.jsonl filter=lfs diff=lfs merge=lfs -text
+unanimity/test.jsonl filter=lfs diff=lfs merge=lfs -text
+unanimity/train.jsonl filter=lfs diff=lfs merge=lfs -text
+unanimity/validation.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,251 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- pt
+licenses:
+- 'other-This data set should be used according to Brazilian law. '
+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 exist two configurations: judgment and unanimity
+
+#### 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` are removed. The splits are not changed other than that.
+
+Label Distribution
+
+| unanimity_label |    train | validation |    test |
+|:----------------|---------:|-----------:|--------:|
+| unanimity       |     1681 |        205 |     200 |
+| not-unanimity   |       34 |          6 |       4 |
+| **total**       | **1715** |    **211** | **204** |
+
+## 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.

convert_to_hf_dataset.py

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+import os
+
+import numpy as np
+import pandas as pd
+
+"""
+Dataset url: https://github.com/lagefreitas/predicting-brazilian-court-decisions/blob/main/dataset.zip
+Paper url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044329/
+
+There are no splits available ==> Make random split ourselves
+
+"""
+
+pd.set_option('display.max_colwidth', None)
+pd.set_option('display.max_columns', None)
+
+
+def perform_original_preprocessing():
+    # Original Preprocessing from: https://github.com/lagefreitas/predicting-brazilian-court-decisions/blob/main/predicting-brazilian-court-decisions.py#L81
+    # Loading the labeled decisions
+    data = pd.read_csv("dataset.csv", sep='<=>', header=0)
+    print('data.shape=' + str(data.shape) + ' full data set')
+    # Removing NA values
+    data = data.dropna(subset=[data.columns[9]])  # decision_description
+    data = data.dropna(subset=[data.columns[11]])  # decision_label
+    print('data.shape=' + str(data.shape) + ' dropna')
+    # Removing duplicated samples
+    data = data.drop_duplicates(subset=[data.columns[1]])  # process_number
+    print('data.shape=' + str(data.shape) + ' removed duplicated samples by process_number')
+    data = data.drop_duplicates(subset=[data.columns[9]])  # decision_description
+    print('data.shape=' + str(data.shape) + ' removed duplicated samples by decision_description')
+    # Removing not relevant decision labels and decision not properly labeled
+    data = data.query('decision_label != "conflito-competencia"')
+    print('data.shape=' + str(data.shape) + ' removed decisions labeled as conflito-competencia')
+    data = data.query('decision_label != "prejudicada"')
+    print('data.shape=' + str(data.shape) + ' removed decisions labeled as prejudicada')
+    data = data.query('decision_label != "not-cognized"')
+    print('data.shape=' + str(data.shape) + ' removed decisions labeled as not-cognized')
+    data_no = data.query('decision_label == "no"')
+    print('data_no.shape=' + str(data_no.shape))
+    data_yes = data.query('decision_label == "yes"')
+    print('data_yes.shape=' + str(data_yes.shape))
+    data_partial = data.query('decision_label == "partial"')
+    print('data_partial.shape=' + str(data_partial.shape))
+    # Merging decisions whose labels are yes, no, and partial to build the final data set
+    data_merged = data_no.merge(data_yes, how='outer')
+    data = data_merged.merge(data_partial, how='outer')
+    print('data.shape=' + str(data.shape) + ' merged decisions whose labels are yes, no, and partial')
+    # Removing decision_description and decision_labels whose values are -1 and -2
+    indexNames = data[(data['decision_description'] == str(-1)) | (data['decision_description'] == str(-2)) | (
+            data['decision_label'] == str(-1)) | (data['decision_label'] == str(-2))].index
+    data.drop(indexNames, inplace=True)
+    print('data.shape=' + str(data.shape) + ' removed -1 and -2 decision descriptions and labels')
+
+    data.to_csv("dataset_processed_original.csv", index=False)
+
+
+def perform_additional_processing():
+    df = pd.read_csv("dataset_processed_original.csv")
+
+    # remove strange " characters sometimes occurring in the beginning and at the end of a line
+    df.ementa_filepath = df.ementa_filepath.str.replace('^"', '')
+    df.decision_unanimity = df.decision_unanimity.str.replace('"$', '')
+
+    # removing process_type and judgment_date, since they are the same everywhere (-)
+    # decisions only contains 'None', nan and '-2'
+    # ementa_filepath refers to the name of file in the filesystem that we created when we scraped the data from the Court. It is temporary data and can be removed
+    # decision_description = ementa_text - decision_text - decision_unanimity_text
+    df = df.drop(['process_type', 'judgment_date', 'decisions', 'ementa_filepath'], axis=1)
+
+    # some rows are somehow not read correctly. With this, we can filter them
+    df = df[df.decision_text.str.len() > 1]
+
+    # rename "-2" to more descriptive name ==> -2 means, that they were not able to determine it
+    df.decision_unanimity = df.decision_unanimity.replace('-2', 'not_determined')
+
+    # rename cols for more clarity
+    df = df.rename(columns={"decision_unanimity": "unanimity_label"})
+    df = df.rename(columns={"decision_unanimity_text": "unanimity_text"})
+    df = df.rename(columns={"decision_text": "judgment_text"})
+    df = df.rename(columns={"decision_label": "judgment_label"})
+
+    df.to_csv("dataset_processed_additional.csv", index=False)
+
+    return df
+
+
+perform_original_preprocessing()
+df = perform_additional_processing()
+
+# perform random split 80% train (3234), 10% validation (404), 10% test (405)
+train, validation, test = np.split(df.sample(frac=1, random_state=42), [int(.8 * len(df)), int(.9 * len(df))])
+
+
+def save_splits_to_jsonl(config_name):
+    # save to jsonl files for huggingface
+    if config_name: os.makedirs(config_name, exist_ok=True)
+    train.to_json(os.path.join(config_name, "train.jsonl"), lines=True, orient="records", force_ascii=False)
+    validation.to_json(os.path.join(config_name, "validation.jsonl"), lines=True, orient="records", force_ascii=False)
+    test.to_json(os.path.join(config_name, "test.jsonl"), lines=True, orient="records", force_ascii=False)
+
+
+def print_split_table_single_label(train, validation, test, label_name):
+    train_counts = train[label_name].value_counts().to_frame().rename(columns={label_name: "train"})
+    validation_counts = validation[label_name].value_counts().to_frame().rename(columns={label_name: "validation"})
+    test_counts = test[label_name].value_counts().to_frame().rename(columns={label_name: "test"})
+
+    table = train_counts.join(validation_counts)
+    table = table.join(test_counts)
+    table[label_name] = table.index
+    total_row = {label_name: "total",
+                 "train": len(train.index),
+                 "validation": len(validation.index),
+                 "test": len(test.index)}
+    table = table.append(total_row, ignore_index=True)
+    table = table[[label_name, "train", "validation", "test"]]  # reorder columns
+    print(table.to_markdown(index=False))
+
+
+save_splits_to_jsonl("judgment")
+
+print_split_table_single_label(train, validation, test, "judgment_label")
+
+# create second config by filtering out rows with unanimity label == not_determined, while keeping the same splits
+train = train[train.unanimity_label != "not_determined"]
+validation = validation[validation.unanimity_label != "not_determined"]
+test = test[test.unanimity_label != "not_determined"]
+
+print_split_table_single_label(train, validation, test, "unanimity_label")
+
+# it is a very small dataset and very imbalanced (only very few not-unanimity labels)
+save_splits_to_jsonl("unanimity")

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