Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
German
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Source Datasets:
original
Tags:
License:
| from glob import glob | |
| from pathlib import Path | |
| import json | |
| import numpy as np | |
| import pandas as pd | |
| """ | |
| Dataset url: https://zenodo.org/record/3936490/files/annotated_corpus.zip?download=1 | |
| 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 analyze_dataset(df, num_characters_for_short_sentence=25): | |
| short_sentence = False | |
| counter = 0 | |
| same_label_counter = 0 | |
| other_number = 0 | |
| num_one_paragraph_len = 0 | |
| for i in range(200): | |
| for paragraph in df.iloc[i].decision_reasons: | |
| for sentence in paragraph: | |
| if short_sentence: | |
| print("previous sentence was short: ", short_sentence) | |
| print("current sentence label: ", sentence[1]) | |
| print("current paragraph: ", paragraph) | |
| if sentence[1] == short_sentence[1]: | |
| same_label_counter += 1 | |
| if len(sentence[0]) < num_characters_for_short_sentence: | |
| counter += 1 | |
| short_sentence = sentence | |
| print() | |
| print("short sentence: ", sentence) | |
| print("short paragraph: ", paragraph) | |
| if sentence[1] == 'other': | |
| other_number += 1 | |
| if len(paragraph) == 1: | |
| num_one_paragraph_len += 1 | |
| else: | |
| short_sentence = False | |
| print("num short sentences: ", counter) | |
| print("num short sentences containing the same label as the next one: ", same_label_counter) | |
| print("num short sentences containing 'other' as label: ", other_number) | |
| print("num short sentences where the paragraph contains only this one short sentence: ", num_one_paragraph_len) | |
| # ==> the label is only the same in very few cases | |
| # ==> the label is 'other' in the majority of cases, when it is not: it seems to be mislabeled | |
| # ==> think about removing them entirely | |
| # ==> we opted for not interfering in the content of the dataset | |
| # create a summary jsonl file | |
| dataset_filename = "dataset.jsonl" | |
| if not Path(dataset_filename).exists(): | |
| with open(dataset_filename, "a") as dataset_file: | |
| for filename in glob("annotated_corpus/*.json"): | |
| # we need to do this charade, because some jsons are formatted differently than others | |
| json_text = Path(filename).read_text() | |
| json_obj = json.loads(json_text) | |
| # make it less nested so that it is easier to read as df | |
| new_dict = {} | |
| new_dict.update(json_obj["meta"]) | |
| new_dict.update(json_obj["decision_text"]) | |
| dataset_file.write(json.dumps(new_dict) + "\n") | |
| else: | |
| print(f"{dataset_filename} already exists. Please delete it to re-aggregate it.") | |
| df = pd.read_json(dataset_filename, lines=True) | |
| # Do splits before expanding the df so that entire decisions are in the splits and not samples from one decision are spread across splits | |
| # perform random split 80% train (160 decisions), 10% validation (20 decisions), 10% test (20 decisions) | |
| train, validation, test = np.split(df.sample(frac=1, random_state=42), [int(.8 * len(df)), int(.9 * len(df))]) | |
| def expand_df(df): | |
| """ | |
| Expand the df so that each sentence has its own row and is its own sample | |
| :param df: | |
| :return: | |
| """ | |
| rows = [] | |
| for index, row in df.iterrows(): | |
| for paragraph in row.decision_reasons: | |
| for sent_idx, sentence in enumerate(paragraph): | |
| new_row = {'file_number': row['file_number'], 'input_sentence': sentence[0], 'label': sentence[1]} | |
| # Discussion with lawyer yielded, that the paragraph as context is enough | |
| # take the sentences before | |
| new_row['context_before'] = paragraph[:sent_idx] | |
| # take the remaining sentences afterwards | |
| new_row['context_after'] = paragraph[sent_idx + 1:] | |
| rows.append(new_row) | |
| return pd.DataFrame.from_records(rows) | |
| train = expand_df(train) | |
| validation = expand_df(validation) | |
| test = expand_df(test) | |
| # Num samples for each split: train (19271), validation (2726), test (3078) | |
| print(len(train.index), len(validation.index), len(test.index)) | |
| # save to jsonl files for huggingface | |
| train.to_json("train.jsonl", lines=True, orient="records") | |
| validation.to_json("validation.jsonl", lines=True, orient="records") | |
| test.to_json("test.jsonl", lines=True, orient="records") | |
| # save main df with meta information to file | |
| # link to splits is given via file_number | |
| df = df.drop(['decision_reasons'], axis=1) | |
| df.to_json("meta.jsonl", lines=True, orient="records") | |
| 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)) | |
| print_split_table_single_label(train, validation, test, "label") | |