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:
File size: 6,703 Bytes
0363a0b 31ca8e7 0363a0b 31ca8e7 0363a0b 31ca8e7 0363a0b 31ca8e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
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")
|