Datasets:
File size: 1,969 Bytes
312d05b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
#! /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()
|