Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
Portuguese
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
Tags:
License:
| 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") | |