Datasets:
#! /usr/bin/env python3 | |
''' | |
This script will download and preprocess CCMatrix English-Basque parallel corpus | |
''' | |
from datasets import load_dataset, concatenate_datasets | |
import pandas as pd | |
import json | |
def main(): | |
dataset = load_dataset('xezpeleta/ccmatrix', 'en-eu', split='train', trust_remote_code=True) | |
#print(next(iter(dataset['train']))) | |
#Response: {'id': 0, 'score': 1.2498578, 'translation': {'en': "He stands to God's word, and God's worship.", 'eu': 'Jaungoikoa goratzera bideratuta egongo da eta Jaungoikoa bere borondatea betez goratzen da.'}} | |
# Filter sentences with 40 characters or more in both English and Basque | |
filtered_dataset = dataset.filter(lambda example: len(example['translation']['en']) >= 40 and len(example['translation']['eu']) >= 40) | |
# Sort the dataset based on the "score" column - DISABLED (the dataset is already sorted by score) | |
#sorted_dataset = dataset.sort("score", reverse=True) | |
# Calculate the number of samples for top 10% and the last 10% | |
num_samples = len(filtered_dataset) | |
top_10_percent = int(num_samples * 0.1) | |
last_10_percent = int(num_samples * 0.9) | |
# Get the top and last 10% samples | |
top_10_samples = filtered_dataset.select(range(top_10_percent)) | |
last_10_samples = filtered_dataset.select(range(num_samples-last_10_percent, last_10_percent)) | |
# Combine the top and last 10% samples | |
assert top_10_samples.features.type == last_10_samples.features.type | |
resulting_dataset = concatenate_datasets([top_10_samples, last_10_samples]) | |
# Shuffle the dataset | |
resulting_dataset = resulting_dataset.shuffle() | |
# Generate train and eval | |
#resulting_dataset = resulting_dataset.train_test_split(test_size=0.1) | |
# Save the dataset | |
#resulting_dataset.to_json("ccmatrix_eng_eus_filtered.jsonl") | |
# Upload the dataset to HF | |
resulting_dataset.push_to_hub("xezpeleta/ccmatrix_eng_eus_filtered") | |
if __name__ == '__main__': | |
main() | |