Datasets:

AzizBelaweid
/

Tunisian_Language_Dataset

import time
import os
from datasets import load_dataset
import pickle


# Define a helper function to load datasets with retry mechanism
def load_dataset_with_retries(dataset_name, *args, retries=3, wait=5, **kwargs):
    for attempt in range(retries):
        try:
            return load_dataset(dataset_name, *args, **kwargs)
        except Exception as e:
            print(f"Attempt {attempt + 1} failed for {dataset_name}. Error: {e}")
            if attempt < retries - 1:
                time.sleep(wait)
            else:
                raise


# Checkpoint file to save progress
checkpoint_file = 'tunisian_data_checkpoint.txt'
dataset_count_path = 'data_count.pkl'

# Load progress if checkpoint exists
if os.path.exists(checkpoint_file):
    with open(checkpoint_file, 'r') as f:
        final_data = eval(f.read())
else:
    final_data = []

if os.path.exists(dataset_count_path):
    # Loading the variable back
    with open(dataset_count_path, 'rb') as f:
        loaded_data = pickle.load(f)
        datasets_completed = loaded_data['datasets_completed']
else:
    datasets_completed = 0


# Helper function to save progress to a checkpoint file
def save_checkpoint(data):
    with open(checkpoint_file, 'w') as f:
        f.write(str(data))


def save_datasets_completed(num):
    # Saving the variable to a file
    with open(dataset_count_path, 'wb') as f:
        pickle.dump({'datasets_completed': num}, f)


# Load and process datasets
try:
    if datasets_completed < 1:
        ds_xp3x = load_dataset_with_retries("Muennighoff/xP3x", "aeb_Arab", trust_remote_code=True)
        final_data.extend(list(sentence['targets'] for sentence in ds_xp3x['train']))
        save_checkpoint(final_data)
        datasets_completed += 1

    if datasets_completed < 2:
        ds_glotcc = load_dataset_with_retries("cis-lmu/glotcc-v1", name="aeb-Arab", split="train")
        final_data.extend(list(sentence['content'] for sentence in ds_glotcc))
        save_checkpoint(final_data)
        datasets_completed += 1

    if datasets_completed < 3:
        ds_flores = load_dataset_with_retries('facebook/flores', 'aeb_Arab')
        final_data.extend(list(sentence['sentence'] for sentence in ds_flores['dev']))
        final_data.extend(list(sentence['sentence'] for sentence in ds_flores['devtest']))
        save_checkpoint(final_data)
        datasets_completed += 1

    if datasets_completed < 4:
        ds_glotstory = load_dataset_with_retries('cis-lmu/GlotStoryBook', 'default', split='train')
        glotstory_sentences = [sentence for sentence in ds_glotstory if sentence["Language"] == 'aeb']
        final_data.extend(glotstory_sentences)
        save_checkpoint(final_data)
        datasets_completed += 1

    if datasets_completed < 5:
        ds_sib200 = load_dataset_with_retries('Davlan/sib200', 'aeb_Arab')
        final_data.extend(list(sentence['text'] for sentence in ds_sib200['train']))
        final_data.extend(list(sentence['text'] for sentence in ds_sib200['validation']))
        final_data.extend(list(sentence['text'] for sentence in ds_sib200['test']))
        save_checkpoint(final_data)
        datasets_completed += 1

    if datasets_completed < 6:
        ds_xsimplus = load_dataset_with_retries("jaygala24/xsimplusplus", "aeb_Arab")
        final_data.extend(list(sentence['query'] for sentence in ds_xsimplus['dev']))
        final_data.extend(list(sentence['query'] for sentence in ds_xsimplus['devtest']))
        save_checkpoint(final_data)
        datasets_completed += 1

    if datasets_completed < 7:
        ds_gentai = load_dataset_with_retries("gentaiscool/bitext_sib200_miners", "eng_Latn-aeb_Arab")
        final_data.extend(list(sentence['sentence2'] for sentence in ds_gentai['train']))
        save_checkpoint(final_data)
        datasets_completed += 1

    if datasets_completed < 8:
        dataset_reddit = load_dataset_with_retries('dataverse-scraping/reddit_dataset_219', split='train',
                                                   streaming=True)


        def filter_function(example):
            return example['communityName'] == 'r/Tunisia'  # Replace with your filter condition


        filtered_dataset = dataset_reddit.filter(filter_function)
        final_data.extend(list(sentence['text'] for sentence in filtered_dataset))
        save_checkpoint(final_data)
        datasets_completed += 1

    # Final save to a text file
    with open('tunisian_data.txt', 'w') as f:
        for line in final_data:
            f.write(f"{line}\n")

except Exception as e:
    print(f"An error occurred: {e}. Progress saved.")
    save_checkpoint(final_data)

	import time
	import os
	from datasets import load_dataset
	import pickle


	# Define a helper function to load datasets with retry mechanism
	def load_dataset_with_retries(dataset_name, args, retries=3, wait=5, *kwargs):
	for attempt in range(retries):
	try:
	return load_dataset(dataset_name, args, *kwargs)
	except Exception as e:
	print(f"Attempt {attempt + 1} failed for {dataset_name}. Error: {e}")
	if attempt < retries - 1:
	time.sleep(wait)
	else:
	raise


	# Checkpoint file to save progress
	checkpoint_file = 'tunisian_data_checkpoint.txt'
	dataset_count_path = 'data_count.pkl'

	# Load progress if checkpoint exists
	if os.path.exists(checkpoint_file):
	with open(checkpoint_file, 'r') as f:
	final_data = eval(f.read())
	else:
	final_data = []

	if os.path.exists(dataset_count_path):
	# Loading the variable back
	with open(dataset_count_path, 'rb') as f:
	loaded_data = pickle.load(f)
	datasets_completed = loaded_data['datasets_completed']
	else:
	datasets_completed = 0


	# Helper function to save progress to a checkpoint file
	def save_checkpoint(data):
	with open(checkpoint_file, 'w') as f:
	f.write(str(data))


	def save_datasets_completed(num):
	# Saving the variable to a file
	with open(dataset_count_path, 'wb') as f:
	pickle.dump({'datasets_completed': num}, f)


	# Load and process datasets
	try:
	if datasets_completed < 1:
	ds_xp3x = load_dataset_with_retries("Muennighoff/xP3x", "aeb_Arab", trust_remote_code=True)
	final_data.extend(list(sentence['targets'] for sentence in ds_xp3x['train']))
	save_checkpoint(final_data)
	datasets_completed += 1

	if datasets_completed < 2:
	ds_glotcc = load_dataset_with_retries("cis-lmu/glotcc-v1", name="aeb-Arab", split="train")
	final_data.extend(list(sentence['content'] for sentence in ds_glotcc))
	save_checkpoint(final_data)
	datasets_completed += 1

	if datasets_completed < 3:
	ds_flores = load_dataset_with_retries('facebook/flores', 'aeb_Arab')
	final_data.extend(list(sentence['sentence'] for sentence in ds_flores['dev']))
	final_data.extend(list(sentence['sentence'] for sentence in ds_flores['devtest']))
	save_checkpoint(final_data)
	datasets_completed += 1

	if datasets_completed < 4:
	ds_glotstory = load_dataset_with_retries('cis-lmu/GlotStoryBook', 'default', split='train')
	glotstory_sentences = [sentence for sentence in ds_glotstory if sentence["Language"] == 'aeb']
	final_data.extend(glotstory_sentences)
	save_checkpoint(final_data)
	datasets_completed += 1

	if datasets_completed < 5:
	ds_sib200 = load_dataset_with_retries('Davlan/sib200', 'aeb_Arab')
	final_data.extend(list(sentence['text'] for sentence in ds_sib200['train']))
	final_data.extend(list(sentence['text'] for sentence in ds_sib200['validation']))
	final_data.extend(list(sentence['text'] for sentence in ds_sib200['test']))
	save_checkpoint(final_data)
	datasets_completed += 1

	if datasets_completed < 6:
	ds_xsimplus = load_dataset_with_retries("jaygala24/xsimplusplus", "aeb_Arab")
	final_data.extend(list(sentence['query'] for sentence in ds_xsimplus['dev']))
	final_data.extend(list(sentence['query'] for sentence in ds_xsimplus['devtest']))
	save_checkpoint(final_data)
	datasets_completed += 1

	if datasets_completed < 7:
	ds_gentai = load_dataset_with_retries("gentaiscool/bitext_sib200_miners", "eng_Latn-aeb_Arab")
	final_data.extend(list(sentence['sentence2'] for sentence in ds_gentai['train']))
	save_checkpoint(final_data)
	datasets_completed += 1

	if datasets_completed < 8:
	dataset_reddit = load_dataset_with_retries('dataverse-scraping/reddit_dataset_219', split='train',
	streaming=True)


	def filter_function(example):
	return example['communityName'] == 'r/Tunisia' # Replace with your filter condition


	filtered_dataset = dataset_reddit.filter(filter_function)
	final_data.extend(list(sentence['text'] for sentence in filtered_dataset))
	save_checkpoint(final_data)
	datasets_completed += 1

	# Final save to a text file
	with open('tunisian_data.txt', 'w') as f:
	for line in final_data:
	f.write(f"{line}\n")

	except Exception as e:
	print(f"An error occurred: {e}. Progress saved.")
	save_checkpoint(final_data)