add ccmatrix_filtered generation scripts

eng-eus/ccmatrix/ccmatrix_generate_txts.py

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+#! /usr/bin/env python3
+
+'''
+This script will download the dataset xezpeleta/ccmatrix_eng_eus_filtered from Hugging Face
+with the following structure:
+
+{
+    '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.'
+    }
+}
+
+Then will create the following txt files:
+- ccmatrix_filtered.en-eu.en
+- ccmatrix_filtered.en-eu.eu
+'''
+
+from datasets import load_dataset
+
+def main():
+    dataset = load_dataset('xezpeleta/ccmatrix_eng_eus_filtered')
+
+    # Create the txt files
+    with open('ccmatrix_filtered.en-eu.en', 'w') as en_file, open('ccmatrix_filtered.en-eu.eu', 'w') as eu_file:
+        for data in dataset['train']:
+            en_file.write(data['translation']['en'] + '\n')
+            eu_file.write(data['translation']['eu'] + '\n')
+
+if __name__ == '__main__':
+    main()

eng-eus/ccmatrix/get_ccmatrix_eng_eus.py

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+#! /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()

eng-eus/ccmatrix/requirements.txt

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+datasets