Upload dataset-preprocessor.py

dataset-preprocessor.py

@@ -0,0 +1,54 @@
+from datasets import load_dataset
+import re
+import random
+
+def split_into_paragraphs(text):
+    # Split by markdown headers or double newlines
+    paragraphs = re.split(r'\n\n|(?=^#)', text, flags=re.MULTILINE)
+    return [p.strip() for p in paragraphs if p.strip()]
+
+def create_input_output_pairs(example):
+    paragraphs = example['paragraphs']
+    n_paragraphs = len(paragraphs)
+    
+    # Randomly select about half of the paragraphs for input
+    n_input = max(1, random.randint(n_paragraphs // 2 - 1, n_paragraphs // 2 + 1))
+    
+    input_paragraphs = paragraphs[:n_input]
+    output_paragraphs = paragraphs[n_input:]
+    
+    return {
+        'inputs': ' '.join(input_paragraphs),
+        'targets': ' '.join(output_paragraphs)
+    }
+
+def preprocess_dataset(dataset_name, text_column='text'):
+    # Load the dataset
+    dataset = load_dataset(dataset_name)
+    
+    # Split text into paragraphs
+    dataset = dataset.map(
+        lambda example: {'paragraphs': split_into_paragraphs(example[text_column])},
+        remove_columns=[text_column]
+    )
+    
+    # Create input-output pairs
+    preprocessed_dataset = dataset.map(
+        create_input_output_pairs,
+        remove_columns=['paragraphs']
+    )
+    
+    return preprocessed_dataset
+
+# Usage example
+if __name__ == "__main__":
+    # Replace 'your_dataset' with the actual dataset name
+    dataset_name = 'your_dataset'
+    
+    preprocessed_dataset = preprocess_dataset(dataset_name)
+    
+    # Print some examples
+    print(preprocessed_dataset['train'][:5])
+    
+    # Save the preprocessed dataset
+    preprocessed_dataset.save_to_disk("preprocessed_dataset")