Add data origins and generation script

origin/titels_pd.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a6dec2b669b3b2374e3fe53107d1f837f98e7233062418c425270185a6ecd9c
+size 2679987

origin/xml_pd.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5f97a9189ed547bb54150fcb1343a9a77ae698ec99f1978ba0e6ed5a08761bf
+size 665703428

src/create_nl_dataset.py

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+import json
+import math
+import zipfile
+
+import bs4
+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 = 0.95
+    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('../data/train.jsonl', 'w') as train_file:
+        with open('../data/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)))

src/requirements.txt

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+beautifulsoup4
+python-dateutil
+pandas
+datasets
+lxml
+tqdm