import json import math import zipfile import bs4 import datasets import dateutil.parser import pandas as pd from tqdm import tqdm def yield_file_contents(zip_path, train_df, val_df): with (zipfile.ZipFile(zip_path, 'r') as zip_file): for file_info in zip_file.infolist(): with zip_file.open(file_info, 'r') as file: content = file.read() soup = bs4.BeautifulSoup(content, 'xml') id_blk = soup.find('idno', type="titelcode") text_id = id_blk.text.strip() if id_blk is not None else file_info.filename.replace('.xml', '') ti_id = '_'.join(text_id.split('_')[:-1]) train_row = train_df[train_df['ti_id'] == ti_id] val_row = val_df[val_df['ti_id'] == ti_id] is_train = len(train_row) > 0 is_val = len(val_row) > 0 if is_train: meta = train_row.iloc[0].to_dict() split = 'train' elif is_val: meta = val_row.iloc[0].to_dict() split = 'validation' else: print(f'Did not find meta for {text_id}!') for key, value in list(meta.items()): if isinstance(value, float) and math.isnan(value): meta[key] = '' edition_blk = soup.find('edition') edition = edition_blk.text.strip() if edition_blk is not None else None lang_blk = soup.find('language') language = lang_blk.get('id').strip() if lang_blk is not None else None date_blk = soup.find('revisionDesc') if date_blk is not None: date_blk = date_blk.find('date') if date_blk is not None: try: date = dateutil.parser.parse( date_blk.text.strip(), yearfirst=True, dayfirst=True ).isoformat() if date_blk is not None else None except Exception: date = None else: date = None meta['revision_date'] = date meta['edition'] = edition meta['language'] = language for chap_idx, chapter in enumerate(soup.find_all('div', type='chapter')): meta['chapter'] = chap_idx + 1 for sec_idx, section in enumerate(chapter.find_all('div', type='section')): meta['section'] = sec_idx + 1 text = section.text.strip() yield {'meta': meta, 'text': text, 'id': f"{text_id}_{chap_idx}_{sec_idx}"}, split if __name__ == '__main__': train_fraction = 0.90 metadata_path = '../origin/titels_pd.csv' meta_df = pd.read_csv(metadata_path, header=1, sep='|') meta_df = meta_df.sample(frac=1, random_state=0) num_train = round(train_fraction*len(meta_df)) train_df = meta_df.iloc[:num_train] val_df = meta_df.iloc[num_train:] with open('tmp/train.jsonl', 'w') as train_file: with open('tmp/val.jsonl', 'w') as val_file: for item, split in tqdm(yield_file_contents('../origin/xml_pd.zip', train_df, val_df)): if split == 'train': train_file.write('{}\n'.format(json.dumps(item))) if split == 'validation': val_file.write('{}\n'.format(json.dumps(item))) datasets.Dataset.from_json('tmp/train.jsonl', split='train').save_to_disk('../data/train') datasets.Dataset.from_json('tmp/val.jsonl', split='validation').save_to_disk('../data/validation')