Datasets:
Tasks:
Fill-Mask
Multilinguality:
multilingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
other
Source Datasets:
original
Tags:
License:
# 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 | |