Datasets:

Multilinguality:
multilingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
other
Source Datasets:
original
Tags:
License:
MultiLegalPile_Wikipedia_Filtered / prepare_legal_data.py
Joelito's picture
added dataloader, preparation script and dataset card
931df01
# 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