Update parquet files

.gitattributes

@@ -1,40 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac 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
-test.jsonl filter=lfs diff=lfs merge=lfs -text
-train.jsonl filter=lfs diff=lfs merge=lfs -text
-validation.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,252 +0,0 @@
----
-annotations_creators:
-- found
-language_creators:
-- found
-language:
-- pt
-license:
-- '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](mailto:[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](mailto:[email protected])
-; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:[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

@@ -1,132 +0,0 @@
-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("")
-
-print_split_table_single_label(train, validation, test, "judgment_label")
-print_split_table_single_label(train, validation, test, "unanimity_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"]
-
-
-# it is a very small dataset and very imbalanced (only very few not-unanimity labels)
-# save_splits_to_jsonl("unanimity")

joelito--brazilian_court_decisions/json-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:032a397b906e1239a490a4366e680076273635376dfac290ad48fc04470df726
+size 412536

joelito--brazilian_court_decisions/json-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c90b12e13f429d3c9bfd0654222f7058f1fa8b0da39ddf44770266394827138
+size 3245400

joelito--brazilian_court_decisions/json-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1853e6118499b34684da635d60214d1228f62cf8cd6f18abf966382e42706b82
+size 425023