Datasets:

joelniklaus
/

MultiLegalPile_Wikipedia_Filtered

	# No chunks, one doc per line

	# remove new lines, etc.
	# create a corpus of min 200-400 GB ==> ~100B tokens
	# max file size: 4GB because of huggingface
	# validation set: ~100M tokens ==> 200-400MB

	import glob
	import json
	import multiprocessing

	import tqdm
	import os
	import re
	from multiprocessing import Pool

	from datasets import load_dataset
	from tokenizers import normalizers

	_LANGUAGES = ['bg', 'cs', 'da', 'de', 'el', 'en', 'es', 'et', 'fi', 'fr', 'ga', 'hr',
	'hu', 'it', 'lt', 'lv', 'mt', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl', 'sv']
	_DOMAIN_TYPES = ['legislation', 'caselaw', 'contracts', 'other', 'wikipedia']

	custom_normalizer = normalizers.NFKD()

	VALIDATION_SIZE = 1_000 # ~1MB per configuration ==> some low-resource configs will only have a validation file

	filtered_dir = os.path.join('data', 'filtered')
	os.makedirs(filtered_dir, exist_ok=True)


	def preprocess_dataset(languages=None, domain_types=None):
	lang_type_datasets = []
	# set defaults if they are not set
	if languages is None:
	languages = _LANGUAGES
	if domain_types is None:
	domain_types = _DOMAIN_TYPES

	for LANG in languages:
	for DOMAIN_TYPE in domain_types:
	try:
	if DOMAIN_TYPE == 'wikipedia':
	# get from EU_Wikipedias
	dataset = load_dataset("joelito/EU_Wikipedias", date="20221120", language=LANG,
	split='train', streaming=True, use_auth_token=True)
	else:
	# get from Multi_Legal_Pile
	dataset = load_dataset("joelito/Multi_Legal_Pile", f'{LANG}_{DOMAIN_TYPE}',
	split='train', streaming=True, use_auth_token=True)
	dataset = dataset.shuffle(seed=42, buffer_size=10_000)
	print(f'Found data for `{DOMAIN_TYPE}` in language `{LANG}`.')
	except:
	print(f'There is no data for `{DOMAIN_TYPE}` in language `{LANG}`.')
	continue
	lang_type_datasets.append(dataset)
	return lang_type_datasets


	def write_samples(dataset_number):
	dataset, dataset_name = dataset_number
	if len(dataset_name.split('_')) == 1: # wikipedia
	language = dataset_name.split('.')[1]
	domain_type = "wikipedia"
	dataset_name = f"{language}_{domain_type}" # reformat the config name so that we have wikipedia in the name
	else:
	language, domain_type = dataset_name.split('_')
	total_count, temp_count, all_samples, file_number = 0, 0, 0, 0
	out_file = open_file(dataset_name, file_number, "validation") # we save the first examples to the validation set
	print(f'Processing for dataset {dataset_name} started!')
	# Read each document
	for sample in tqdm.tqdm(dataset):
	try:
	text = normalize_text(sample['text'])
	if "validation" in out_file.name and temp_count > VALIDATION_SIZE:
	# if we are saving to eval, and we have enough samples in the eval set, switch to train
	out_file.close()
	temp_count = 0
	out_file = open_file(dataset_name, file_number, "train")
	# on average approx. 2GB per file, compresses (with xz) to around ~500MB (xz: ~75% compression ratio)
	if "train" in out_file.name and temp_count > 500_000: # err on the small side of the file size
	# if we are saving to train, and we reached the max size per file, switch to the next file
	out_file.close()
	file_number += 1
	temp_count = 0
	out_file = open_file(dataset_name, file_number, "train")
	# if the text is usable for pretraining, save it
	if is_text_usable(text):
	jurisdiction = sample.get('jurisdiction', "N/A") # set defaults for wikipedia
	type = sample.get("type", "wikipedia") # set defaults for wikipedia
	entry = {"language": sample["language"], "type": type, "jurisdiction": jurisdiction, "text": text}
	out_file.write(json.dumps(entry) + '\n')
	total_count += 1
	temp_count += 1
	all_samples += 1
	except:
	continue

	try:
	out_file.close()
	except:
	pass

	print(f'Processing for dataset {dataset_name} finished with {total_count}/{all_samples}!')
	return


	def is_text_usable(text):
	# Compute percentage of alphabetical characters in relation to full sequence length
	punctuation = '!\"#$%&\'()*+,\-\./:;<=>?@\[\\\]\^_`{\\|}~'
	alpha_text = re.sub(rf'[{punctuation}\d]', '', text) # remove numbers and punctuation
	alpha_percent = len(alpha_text) / len(text)
	# Compute total chunk length
	text_length = len(text.split())
	# Ignore sequences with more than 30% numbers or short sequences (less than 64 tokens)
	return alpha_percent > 0.7 and text_length > 64


	def normalize_text(text):
	# Normalize the document
	text = custom_normalizer.normalize_str(text)
	# Replace multiple newline and whitespaces
	return re.sub(r'(\n )+', r'\n ', re.sub(r'( [\n\r]+ )+', r'\n ', re.sub(r'[\t ]+', r' ', text)))


	def open_file(dataset_name, file_number, split):
	return open(os.path.join(filtered_dir, f'{dataset_name}_{split}_{file_number}.jsonl'), 'w', encoding='utf8')


	def clean_and_filter_documents():
	# Load all datasets across languages and types
	lang_type_datasets = preprocess_dataset(languages=None, domain_types=None)
	# also pass in dataset_name
	lang_type_datasets = [(dataset, dataset.config_name) for dataset in lang_type_datasets]
	print(lang_type_datasets)

	# Launch pool to preprocess datasets in parallel
	max_num_processes = min(multiprocessing.cpu_count() - 2, len(lang_type_datasets))
	num_processes = max(max_num_processes, 1)
	print(f'Launching a Pool with maximum {num_processes} processes...')
	with Pool(num_processes) as pool:
	pool.map(write_samples, lang_type_datasets)

	# Compress datasets
	print(f"Compressing datasets at {filtered_dir}")
	# Do this at the end because we use multithreading
	for path in glob.glob(os.path.join(filtered_dir, '*.jsonl')):
	print(f"Compressing {path}")
	os.system(f'xz -zkf -T0 {path}') # -TO to use multithreading
	print(f"Removing uncompressed file at {path}")
	os.system(f'rm {path}') # remove uncompressed file to save space

	print(f"Finished preparing legal data")



	if __name__ == '__main__':
	"""
	Run with
	export PYTHONPATH=. && python prepare_legal_data.py \| tee prepare_legal_data.log
	"""
	clean_and_filter_documents()

	# Get locally
	# def get_file(LANG, DOMAIN_TYPE, split, number):
	# base_folder = "data/mlm_dataset/chunks_512"
	# return f'{base_folder}/{LANG}_{DOMAIN_TYPE}_{split}_{number}.jsonl.xz'

	# files = [get_file(LANG, DOMAIN_TYPE, 'train', i) for i in range(1, 5)]
	# files = [f for f in files if os.path.exists(f)] # make sure the file actually exists
	# dataset = load_dataset("json", data_files={'train': files}, split='train', streaming=True)

	# TODO write dataset cards for chunked, eu wikipedia and filtered dataset